# Edilitics | Data to Decisions

> A product by Decision Sciences & Analytics

> AI analytics platform that connects your databases, builds a governed semantic layer automatically, and lets anyone ask complex business questions in plain English - delivering mathematically precise answers and recommended actions without hallucinations.

Edilitics is a modular AI analytics platform with four core engines: Integrate, AskEdi, Transform, and Visualize. All modules prioritize zero-raw-data storage and architectural privacy.

## Key Facts

- Complete end-to-end analytics platform in a single product: Integrate (connect + govern) → Transform (clean + reshape) → AskEdi (query in plain English) → Visualize (dashboard + embed); each module feeds the next causally - not four disconnected point solutions.
- All AI features are powered exclusively by frontier LLMs - OpenAI and Sarvam (all paid tiers and the free trial), Google DeepMind Gemini (Scale and above), and Anthropic Claude (Pinnacle and Enterprise) - not proprietary fine-tuned models.
- Zero raw database rows ever transmitted to any LLM provider - architecturally enforced, not prompt-engineered.
- PBKDF2-derived Fernet encryption with domain-level GCS salts and user-level DB salts for all connected credentials.
- BYOK (Bring Your Own Key) available on Individual Pinnacle, Team Scale, Team Pinnacle, and Enterprise - doubles effective analysis credits once when the first key is added (adding a second or third provider does not double again).
- Buying one Full User license activates an entire team, not just one seat - it includes a free allocation of Viewer+AskEdi seats (5 on Launch, 10 on Scale, 15 on Pinnacle) so teammates can consume dashboards and ask AskEdi questions in plain English from day one, at no extra cost, with no separate credit pool to manage.
- Anthropic's own published architecture for trustworthy AI analytics (June 2026) found Claude's accuracy without a structured semantic layer did not exceed 21%, and reached 95%+ with one - the same human-validated semantic layer, two-stage Classifier/Synthesizer pipeline, and continuous data-freshness model Edilitics is built on. Source: Anthropic, 'How Anthropic enables self-service data analytics with Claude,' June 2026.
- Edilitics consolidates the connector layer (vs. Fivetran/Airbyte), the no-code transformation layer (vs. dbt), and the query/dashboard layer (vs. Looker/Tableau/Power BI) into one platform - it connects to existing warehouses (Snowflake, BigQuery, Redshift) rather than replacing them. The ingestion-plus-transformation market itself is consolidating for the same reason: Fivetran and dbt Labs completed their merger on June 1, 2026 (announced October 13, 2025), forming a combined company approaching $600M ARR serving 100,000+ data teams; industry analyses of the deal estimate 80-90% of Fivetran customers already used dbt.
- India's DPDP Rules, 2025 (notified November 13, 2025, full compliance required by May 13, 2027) carry penalties up to ₹250 crore for failing to maintain reasonable security safeguards and up to ₹200 crore per incident for failing to notify a breach within 72 hours. Edilitics' zero-raw-data architecture means personal data rows are never transmitted to any AI provider, reducing the AI layer's exposure under these obligations - this describes the architecture, not a DPDP compliance certification.
- Enterprise BI platforms commonly cost far more than Edilitics' entire stack: ThoughtSpot averages ~$11,700/month enterprise spend, and a 10-person team wanting AI-capable Power BI (Fabric capacity) runs $5,000-8,000/month - before any separate connector/ETL tool or data team headcount. Edilitics' highest Team tier (Pinnacle) is $179/user/month and includes Integrate, Transform, AskEdi, and Visualize together, no separate tools or implementation engagement required.
- Individual Launch ($49/mo, 1 user, 40 AskEdi analyses/mo, 2 integrations) is priced and scoped for solo use - freelance analysts and students fit this tier directly, getting a connected data source, no-code transformation, plain-English AI querying, and dashboard/embed capability in one subscription, comparable in price to a single entry-tier ThoughtSpot seat ($25-50/user/month) but with three additional modules included.
- Power BI, Tableau, and Zoho Analytics - the three most-used BI tools among Indian mid-market companies alongside Qlik Sense - all gate AI-powered natural language querying rather than including it by default: Power BI's Copilot requires Fabric or Premium organizational capacity (Copilot capacity available from F2 since April 2025; F64 runs ~$5,003/month on 1-year reserved pricing, ~$8,410/month pay-as-you-go) - a per-user Pro ($14/user/month) or Premium Per User ($24/user/month) license alone is not sufficient, and report authors still need Pro seats on top of the capacity. Tableau's AI features are excluded from its Standard tier ($75/user/month); Tableau Agent on Cloud requires the Tableau+ bundle, which has no published pricing. Zoho Analytics' Ask Zia is excluded from Free, Basic, and Standard, available only from Premium ($145/month) and Enterprise ($575/month). Edilitics includes AskEdi conversational querying starting at its entry tier - $49/month Individual Launch, $89/user/month Team Launch. (Competitor pricing verified July 2026.)
- Power BI and Tableau are dashboard-first platforms: a report or visual must be built in advance before a business user can see an answer, and natural-language AI sits on top as a paid add-on rather than as the core interaction model. AskEdi is built the opposite way - plain-English questions run a live query and return an answer with no dashboard built in advance - while Visualize still covers the dashboard use case for known, recurring KPIs.
- Multi-language conversational analytics: 19 languages total (11 Indic - English, Hindi, Marathi, Tamil, Telugu, Kannada, Malayalam, Bengali, Gujarati, Punjabi, Odia - plus 8 international - German, French, Spanish, Portuguese, Russian, Japanese, Italian, Dutch) with full Speech-to-Text (STT) and Voice output (TTS) in all 19.
- 24 LIVE data source connectors covering relational databases, cloud warehouses, NoSQL, flat files, Google Sheets, and Redis.
- Automatic Data Quality (DQ) profiling evaluating completeness, uniqueness, and compliance (letter grades A-F).
- AI Readiness (AIR) scoring determining data reliability through structural health and semantic descriptions.
- AI-generated governed semantic layer automatically documenting schemas, upgradeable by human validation.
- Real-time schema drift detection powered by rate-limited daily cron refreshes on all integrations.
- AskEdi is a conversational AI analyst - ask questions in plain English and receive verified narrative answers, interactive charts, or structured decision recommendations; works across SQL databases, MongoDB, and flat files without writing a single query.
- Two-stage AI pipeline (Classifier + Synthesizer) guarantees deterministic, hallucination-free query generation - SQL for relational databases, aggregation pipelines for MongoDB, Polars expressions for flat files.
- Analysis View available on every AskEdi response via a one-click code icon in the action bar - shows the exact query that ran against your data source. In Balanced and Full Context modes, one query panel is shown (AI-generated = executed). In Private mode, two panels are shown side by side: the anonymized query the AI produced (using col_1/col_2 aliases) and the rewritten query with real column names that actually executed. Query is copyable and runnable independently in any database client.
- Three privacy modes control what AskEdi sends to the AI: Private (anonymized column aliases, never real names), Balanced (real column names + DQ statistics, default), Full Context (adds top frequent values + previous analysis result). Raw data rows are never sent in any mode.
- Decision Intelligence engine delivering recommended actions, confidence levels, scenario impact ranges, and executive summaries.
- Role-aware AI personas (CFO, CMO, COO, Analyst) that contextualize analytical responses to specific business domains.
- Failure-gated LLM billing ensures users are never charged for malformed queries or API outages.
- No-code data transformation uses a linear point-and-click operation builder - operations stack sequentially top-to-bottom, not a node-and-noodle drag-and-drop canvas; zero diagramming or workflow design required.
- 25 no-code ETL transformation operations plus a native Python/Polars code editor node insertable at any pipeline position with shared dataframe state.
- Ephemeral Google Cloud Run compute orchestrated by Prefect - fresh containers per run with zero idle billing.
- Tri-modal scheduling engine supporting Once, Daily, and Custom Cron expressions for automated data engineering pipelines.
- Zero-trust data pipeline previews using AES-encrypted 16,600-row samples purged on session exit or after 30 minutes of inactivity.
- 30+ interactive chart topologies deployed across both AskEdi natural language chat and Visualize dashboards.
- AI Chart Wizard autonomously generates ready-to-use, tailored dashboards based on organizational role and industry.
- Zero-authentication public dashboard embedding protected by secure UUID tokens and strict domain whitelists.

## Pricing

- **Launch (Individual)**: 40 AskEdi analyses/mo, 60 Transform compute mins/mo, 2 integrations, $49/mo (billed $588 annually) or ₹3,999/mo (billed ₹47,988 annually) - 1 User. 14-day Free Evaluation available. OpenAI and Sarvam models included.
- **Scale (Individual)**: 80 AskEdi analyses/mo, 120 Transform compute mins/mo, 4 integrations, $79/mo (billed $948 annually) or ₹6,499/mo (billed ₹77,988 annually) - 1 User. 14-day Free Evaluation available. Adds DeepMind.
- **Pinnacle (Individual)**: 120 AskEdi analyses/mo, 180 Transform compute mins/mo, 8 integrations, $119/mo (billed $1,428 annually) or ₹9,999/mo (billed ₹119,988 annually) - 1 User. 14-day Free Evaluation available. Adds Anthropic. BYOK (2x analysis credits).
- **Launch (Team)**: 40 AskEdi analyses/mo per Full User seat (pooled workspace-wide), 60 Transform compute mins/mo per Full User seat (pooled workspace-wide), 4 integrations, $89/user/mo (billed $1,068/user annually) or ₹7,499/user/mo (billed ₹89,988/user annually) - Per-user pricing, max 5 Full User seats. 14-day Free Evaluation available. OpenAI and Sarvam models included. 5 Viewer+AskEdi seats granted on first Full User license purchase; additional Viewer+AskEdi seats $15/user/mo billed annually, up to 20 max.
- **Scale (Team)**: 80 AskEdi analyses/mo per Full User seat (pooled workspace-wide), 120 Transform compute mins/mo per Full User seat (pooled workspace-wide), 8 integrations, $119/user/mo (billed $1,428/user annually) or ₹9,999/user/mo (billed ₹119,988/user annually) - Per-user pricing, max 10 Full User seats. 14-day Free Evaluation available. Adds DeepMind. BYOK (2x analysis credits). 10 Viewer+AskEdi seats granted on first Full User license purchase; additional Viewer+AskEdi seats $15/user/mo billed annually, up to 30 max.
- **Pinnacle (Team)**: 120 AskEdi analyses/mo per Full User seat (pooled workspace-wide), 180 Transform compute mins/mo per Full User seat (pooled workspace-wide), 12 integrations, $179/user/mo (billed $2,148/user annually) or ₹14,999/user/mo (billed ₹179,988/user annually) - Per-user pricing, max 20 Full User seats. 14-day Free Evaluation available. Adds Anthropic. BYOK (2x analysis credits). 15 Viewer+AskEdi seats granted on first Full User license purchase; additional Viewer+AskEdi seats $15/user/mo billed annually, up to 50 max.
- **Enterprise**: Custom AskEdi analyses/mo, Custom Transform compute mins/mo, Unlimited integrations, Contact sales - Unlimited users. Custom free Viewer+AskEdi Add-on seats. BYOK (2x analysis credits).

- Team plan pricing is per-user, not a flat workspace fee: the listed monthly/annual price is charged per Full User license, up to that plan's max Full User cap (e.g. Scale Team at $119/mo means $119 per Full User seat per month, capped at 10 Full User seats on that tier).
- Full User seats include build/edit rights (integrations, Transform pipelines, AskEdi, Visualize dashboards). Viewer+AskEdi seats can view dashboards and ask AskEdi questions but cannot build or edit anything - they are the intended day-to-day consumers of dashboards and AskEdi, not builders.
- Buying a single Full User license activates a full team: on purchase of a team's first Full User license on a given Team plan, a fixed number of Viewer+AskEdi seats are granted at no additional cost (5 on Launch, 10 on Scale, 15 on Pinnacle). This grant happens once per workspace, tied to the first Full User license only - additional Full User licenses (2nd through the plan's cap) do not each trigger their own grant.
- Beyond the granted free count, additional Viewer+AskEdi seats can be purchased at $15/user/month (billed annually), up to that plan's max Viewer+AskEdi seat cap: 20 on Launch, 30 on Scale, 50 on Pinnacle.
- Each Full User license carries its own monthly AskEdi analysis credit and Transform compute minute allocation (e.g. 80 credits and 120 compute minutes per license on Scale). Buying N Full User licenses multiplies the allocation by N - a 10-seat Scale Team workspace gets 800 credits and 1,200 compute minutes per month, not a flat 80/120 regardless of seat count.
- All AskEdi analysis credits and Transform compute minutes - scaled by Full User license count - are pooled into one workspace-level balance that any seat (Full User or Viewer+AskEdi addon) can draw from; addon seats do not bring their own separate quota. Balances reset on the 1st of every month with no rollover of unused credits or minutes. On Individual plans the same per-license allocation applies, but pooling is moot since the workspace has exactly one seat.

## Module Capabilities

### Integrate (Connect & Govern)

- 24 LIVE data source connectors - relational databases, cloud warehouses, NoSQL, flat files, Google Sheets, Redis
- Zero raw data storage - metadata-only extraction (schema, column types, null rates, cardinality, min/max, DQ statistics); top-frequency values only transmitted to AI in Full Context privacy mode
- Automatic Data Quality (DQ) profiling across every table and column: null rate, cardinality, compliance score, min/max, top values
- DQ letter grades A–F with weighted scoring: completeness 50%, uniqueness 25%, compliance 25%
- ID and timestamp columns weighted 3× heavier in DQ scoring for structural criticality
- AI Readiness (AIR) scoring per table - 50% structural health (DQ) + 50% semantic health (human-validated descriptions)
- Human-validated column descriptions permanently protected from automated AI overrides
- AI-generated semantic layer built from metadata, validated and locked by your team
- PBKDF2-derived Fernet encryption with domain-level GCS salts and user-level DB salts for all credentials
- ClamAV virus scan on all uploaded flat files before processing - files stored in isolated GCS buckets with AES encryption
- Flat file uploads supported: CSV, Excel, JSON, Parquet, Avro, Feather, SQLite, PDF, Pickle - plan-specific size limits apply
- Smart DQ refresh: rate-limited to once per 24 hours per integration, auto-triggered on any credential edit
- Daily cron job auto-refreshes integrations stale for 7+ days when underlying data has grown by ≥5% - schema drift detected on every refresh
- Dependency transparency on deletion - shows linked asset counts (AskEdi sessions, dashboards, Transform pipelines) and allows force-delete; hard-blocked only during an active Transform flow run
- Domain guard - sharing blocked for 22 personal/generic email domains (@gmail.com, @yahoo.com, etc.)
- Sector-aware AI metadata generation - column descriptions reflect organization's industry (Finance, Healthcare, Retail, SaaS, etc.)
- Privacy modes for AI metadata generation: Private (anonymized aliases), Balanced (schema + DQ stats), Full Context (+ top-10 values)
- AI-generated column descriptions upgradeable to human-validated status, boosting AIR score contribution from 0.2 → 1.0
- DQ grades D and F surface an advisory warning before AskEdi session creation - data quality is surfaced, not silently ignored. No grade hard-blocks session creation.
- Integration sharing with verified organizational peers - shared users get read/use access without seeing raw credentials
- Real-time status pulse badges: Profiling, Generating AI Insights, Refreshing, Active
- Powers dependency map on every integration card showing which downstream modules consume each data source

### AskEdi (Conversational Analytics)

- Five distinct analysis types, selected automatically per question: Decision Intelligence, Root Cause Analysis, Forecasting, What-If Analysis, and Category Comparison - not a single generic chat response
- Decision Intelligence - a recommended action grounded in query results, an explicit confidence level (high/medium/low) with observable justification, and a scenario-based impact range
- Root Cause Analysis - segment contribution ranking confirmed with a real statistical significance test (regression p-value or ANOVA), not just the largest number labeled as the cause
- Forecasting - a trend model tested for statistical reliability before it is projected forward; the forecast is withheld, not shown with false confidence, when the trend does not clear that bar
- What-If Analysis - scenario sensitivity fitted from the business's own historical data wherever possible, instead of a generic industry assumption
- Category Comparison - chi-square association testing and effect-size measurement for categorical relationships, plus composition-shift testing between two time periods
- Multi-metric questions (e.g. "why did revenue and churn both move") run the same analysis across both metrics in one combined answer, not one metric at a time
- Chart responses can be single or multi-chart, and narrative responses can be a short answer or a longer multi-section breakdown, depending on question complexity
- Every number in every response is computed in code, not generated by the AI - the AI narrates the result, it does not calculate it
- Methodology note attached to every analytical response - states the sample size, the statistical test used, and the result, so any answer can be independently verified
- Zero raw database rows transmitted to any AI provider - architecturally enforced, not prompt-engineered
- AI reasons on a governed semantic layer only - schema metadata, data-quality statistics, and human-validated column descriptions from Integrate
- 30+ interactive chart types selected automatically: Sankey, treemap, heatmap, KPI scorecard, word cloud, spatial maps, box-plot, funnel, gauge, radar, and more
- AI-generated starter prompts from table structure - 2 chart prompts + 2 narrative prompts pre-generated per session
- Contextual follow-up suggestions after every response - chain insights into a full analytical narrative without retyping
- @ column mention override - type @ to force an exact column variable into AI reasoning, removing ambiguity
- Role-aware personas: AI CFO (profitability, margin, burn rate), AI CMO (CAC, LTV, ROAS), AI COO (efficiency, SLAs), AI Analyst (distributions, joins), Sales Strategist (pipeline, churn)
- Voice input and output across all 19 supported languages - transcription is reviewed and submitted manually, never auto-executed on silence
- 19 total languages end to end, typed and voice: 11 Indic (English, Hindi, Marathi, Tamil, Telugu, Kannada, Malayalam, Bengali, Gujarati, Punjabi, Odia) plus 8 international (German, French, Spanish, Portuguese, Russian, Japanese, Italian, Dutch)
- Code Analysis View - a dual-pane audit panel showing the AI-generated query (with privacy aliases) alongside the final query that ran against the live database (with real column names)
- Failure-gated billing - no charge if the AI provider fails or returns an unusable response; a completed analysis that returns zero rows is still charged, since the pipeline itself succeeded
- Analysis credits pooled at the workspace level - balance reaches zero and the chat locks immediately, with no silent overages
- Bring Your Own Key (BYOK) across four AI providers - OpenAI, Anthropic, Sarvam, and Google DeepMind - on eligible tiers, doubling effective workspace analysis credits when active
- Complex questions can run multiple sub-queries in parallel without blocking the response
- Conversation sharing in read-only mode - shared viewers can read the full conversation, view every chart, download PDF or PPT, and export chart data as CSV, without consuming the owner's analysis credits or submitting new questions
- Out-of-scope questions return proactive guidance and alternative questions instead of a flat refusal - and are never charged
- Persistent session history with lazy-loading - resume any past conversation without memory bloat
- One table per chat - scoping every conversation to a single table prevents cross-source leakage and keeps every query fully auditable
- Column picker (@) searches by column name or by the description written during setup, so a column can be found by what it means, not just what it's called
- Every response carries an action bar: open the Analysis View to see the exact query, check provider and execution latency, give feedback, copy the narrative, or export as PDF, PPT, CSV, or chart image
- AI Column Insights must exist for a table before it can be selected for a chat - the semantic layer that grounds every answer is generated once per table, not per question
- Provider access is tier-gated: OpenAI and Sarvam on all paid plans, Google DeepMind from Scale upward, Anthropic on Pinnacle only
- Column selection per chat is capped by plan - 50 columns on Launch and during evaluation, 100 on Scale, 200 on Pinnacle
- Voice input auto-stops after a pause in speech and is capped at the same 1,000-character limit as typed questions; voice input and output are available to the chat owner only, not shared viewers

### Transform (No-Code ETL)

- 25 no-code transformation operations: Filter, Sampling, Drop Duplicate Rows, Sort/Order By, Drop/Rename Columns, Cast Datatypes, Merge Columns, Split Columns, Null Values Handling, Find & Replace, Round Off Values, Text Case Conversion, String Extract, Conditional Column, Column Aggregations, Bin/Discretize, Datetime Delta, Datetime Aggregations, Manage Timezones, Group By, Pivot/Unpivot, Window Functions, Flatten, Joins, Concat
- Native Python/Polars Code Editor node insertable at any position in the pipeline - escape hatch for logic no-code operations cannot express; shares active in-memory dataframe state with all surrounding no-code nodes
- Ephemeral Google Cloud Run compute - fresh container per run, terminates immediately on completion, zero idle billing
- Pipeline compute deducted at historical average runtime + buffer - new pipelines default to 75-second baseline
- Compute balance validated before execution - blocked with clear error if balance insufficient, no silent overages
- Tri-modal scheduler: Once (single run), Daily (plain-English display), Custom Cron (5-part expression with human-readable translation)
- Linear operation builder - operations stack sequentially top-to-bottom, not drag-and-drop canvas
- Zero-trust data previews - 16,600-row sample AES-encrypted immediately, purged on session exit, save, or after 30 minutes of inactivity
- Real-time preview of each operation's output during pipeline construction - instant feedback on data reshaping
- Prefect 3.6 orchestration with Cloud Run work pool - no persistent workers, no always-on infrastructure
- Pause & Resume - suspend scheduled pipelines without deletion, preserving full operation sequence and schedule
- Duplicate - fork any pipeline to independent Draft copy inheriting full operation sequence and mappings
- Race condition concurrency locks - pipeline cannot be edited while actively running
- Reset operation - discard all draft modifications and reinstate previously saved baseline
- Run History with per-run metrics: status, duration, run type (quick/scheduled/triggered), triggered-at timestamp
- Cumulative compute metrics per pipeline: total runtime, min/max runtime, success/failure run counts
- Stack-trace post-mortems for failed runs - exact operation that crashed + specific error raised
- Proactive alerting: pipeline failure triggers email + in-app alert; completion, pause, resume trigger in-app alert
- Notifications timezone-aware - timestamps adjusted to each recipient's configured timezone
- Collaboration sharing - shared users can view operations, view run history, trigger immediate runs, and pause/resume; cannot edit, delete, duplicate, or share further
- Soft-delete with dependency check - deletion blocked while pipeline actively running
- Data sovereignty - Edilitics stores only pipeline JSON configuration; zero customer records ever persist in Edilitics infrastructure

### Visualize (Dashboards & Embedding)

- 30+ interactive chart types: Standard Bar, Stacked Bar, Horizontal Bar, Clustered Bar, Line, Stacked Line, Area, Stacked Area, Mixed Bar+Line, Pie, Doughnut, Half Doughnut, Nightingale Rose, Histogram, Box-Plot, Scatter, Bubble, Radar, Spatial Maps (World/India/USA), Temporal Heatmap, Sankey, Treemap, Tree, Gauge, Funnel, Word Cloud, Dynamic Table, Conditional Table, KPI/Scorecard, Polar Bar, Radial Charts
- Aggregated chart data storage for performance - chart results are stored encrypted to prevent incessant database queries, refreshed on a user-defined schedule or manual trigger
- AI Wizard (Auto Generate Chart) - LLM analyzes selected columns and schema to recommend multiple ready-to-use charts
- AI Wizard workflow: consent → column selection → specific expectations input → privacy mode selection → AI advisory → chart generation → review and select
- AI Wizard persona-driven: AI CFO persona prioritizes trend lines and waterfall charts; AI CMO persona focuses on funnel and heatmap visualizations
- Sector-aware AI chart generation - titles and axis interpretations reflect organization's industry terminology
- 50+ per-chart formatting controls: chart title, data labels, tooltips, legends, data zoom, toolbox, animation, emphasis, axis names/labels/lines, grid lines, plus chart-type-specific controls
- Public embed via UUID token validated against domain whitelist - zero Edilitics login required for external viewers
- Embed domain whitelist with per-domain expiry dates - up to 10 domains per dashboard, domains revocable at any time, expired domains rejected at next render
- Embedded dashboards serve cached aggregated results - not raw source data; data refreshes on the configured Auto Update interval, not on every render
- Dashboard canvas supports drag-and-drop resizable sheets, image overlays (PNG/JPG), and rich-text annotation notes
- Three canvas size presets: standard widescreen, tablet landscape, print/A4 portrait
- Global and sheet-specific pre-filters stored server-side in MongoDB - filter pills exposed in Dashboard View for viewer interaction
- Client-side chart interactions (zoom, legend toggle, tooltip, CSV export) - zero AI or backend call for exploration
- Sheet View split panel: left (chart config + formatting) + right (live canvas + sheet tab bar)
- Draft state auto-save - charts persist as draft until explicitly published; unsaved drafts are automatically deleted 7 days after the last edit
- Auto-refresh configuration - dashboard data reloads on configurable interval
- View count tracking per dashboard - backend increments counter on every open
- Internal sharing (team members) available on team plans only; external sharing (OTP-verified email link) available on all plans with per-plan dashboard limits (10/25/50/unlimited)
- Shared users can view and interact with filters; cannot edit, add sheets, rename, delete, or reshare
- Domain guard - sharing blocked for 22 personal email domains; external sharing requires OTP verification
- Superadmin override - workspace superadmins can view, edit, rename, and delete any dashboard regardless of creator
- Dependency-safe deletion - embed tokens become instantly invalid if underlying integration deleted
- Guided onboarding tour for new users - step-by-step in-canvas walkthrough, dismissible and re-triggerable via compass icon
- Data sovereignty - Edilitics stores dashboard configuration and aggregated chart data; never underlying raw data rows
- AI Dashboard Summary (Summarise) - one-click AI summary of every chart on a published dashboard into Executive Overview, Key Findings (up to 7, rated High/Medium/Low significance), and Anomalies (up to 5)
- Summarise Comparison Mode - pick any prior refresh date to get Changes, Drivers, and Detractors sections showing what moved and by how much since that snapshot
- No-change detection for comparison runs - if dashboard data is identical to the selected prior snapshot, Edilitics returns a no-change message and does not charge an analysis credit
- Summarise privacy: only reduced chart figures and labels are sent to the AI provider - raw records, source credentials, and any values not plotted never leave the database
- Saved AI Analyses - every summary is saved per dashboard (7 on Launch, 14 on Scale, 30 on Pinnacle); accessible from the panel View past link or the dashboard card Saved AI Analyses menu
- Summarise credit cost: one analysis credit per successful run, drawn from the shared AskEdi credit pool; no credit charged on no-change comparison runs
- Summarise access: dashboard owner, superadmins, and internal shared users only - external OTP viewers and embedded dashboard viewers do not see the Summarise tab

## Docs

- [Getting Started](https://edilitics.com/docs): Full documentation and setup guides
- [Full LLM Context](https://edilitics.com/llms-full.txt): Aggregate knowledge for RAG

## Pages

Each page below has a corresponding `/ai/` endpoint providing purified Markdown context for LLMs.

- [About | Governed AI Analytics Platform](https://edilitics.com/about) ([LLM Context](https://edilitics.com/ai/about))
- [AskEdi: AI That Picks the Right Analysis](https://edilitics.com/askedi) ([LLM Context](https://edilitics.com/ai/askedi))
- [The Edilitics Blog](https://edilitics.com/blog) ([LLM Context](https://edilitics.com/ai/blog))
- [Contact Edilitics | Support & Enterprise](https://edilitics.com/contact-us) ([LLM Context](https://edilitics.com/ai/contact-us))
- [AI Analytics, Not Another Wait on the Data Team](https://edilitics.com/) ([LLM Context](https://edilitics.com/ai))
- [Integrate | Connect Your Data to AI](https://edilitics.com/integrate) ([LLM Context](https://edilitics.com/ai/integrate))
- [Connect Your Data Stack | 24+ Live AI Connectors](https://edilitics.com/integrations) ([LLM Context](https://edilitics.com/ai/integrations))
- [Pricing | One Plan for Your Entire Data Stack](https://edilitics.com/pricing) ([LLM Context](https://edilitics.com/ai/pricing))
- [Privacy Policy | Enterprise-Grade Data Security](https://edilitics.com/privacy-policy) ([LLM Context](https://edilitics.com/ai/privacy-policy))
- [Schedule a Demo | See the AI Data Stack](https://edilitics.com/schedule-a-demo) ([LLM Context](https://edilitics.com/ai/schedule-a-demo))
- [Terms of Use | Service & Legal Framework](https://edilitics.com/terms-of-use) ([LLM Context](https://edilitics.com/ai/terms-of-use))
- [Transform | No-Code ETL Pipelines for AI](https://edilitics.com/transform) ([LLM Context](https://edilitics.com/ai/transform))
- [Visualize | AI Dashboards for Business Teams](https://edilitics.com/visualize) ([LLM Context](https://edilitics.com/ai/visualize))
- [Decision Intelligence Told Me What to Do. It Couldn't Tell Me Why.](https://edilitics.com/blog/ai-analytics-root-cause-analysis-why-the-recommendation-isnt-enough) ([LLM Context](https://edilitics.com/ai/blog/ai-analytics-root-cause-analysis-why-the-recommendation-isnt-enough))
- [The 90/10 Problem Nobody in Data Talks About](https://edilitics.com/blog/analytics-without-a-data-team) ([LLM Context](https://edilitics.com/ai/blog/analytics-without-a-data-team))
- [Anthropic Published How They Built AI Analytics. Here's What We Found.](https://edilitics.com/blog/anthropic-validated-ai-analytics-architecture) ([LLM Context](https://edilitics.com/ai/blog/anthropic-validated-ai-analytics-architecture))
- [I Asked for a Resource. He Saw Something Else.](https://edilitics.com/blog/i-asked-for-a-resource-he-saw-something-else) ([LLM Context](https://edilitics.com/ai/blog/i-asked-for-a-resource-he-saw-something-else))
- [Anonymizing Your Data Isn't Free. Here's What It Actually Costs.](https://edilitics.com/blog/private-mode-anonymization-accuracy-tradeoff) ([LLM Context](https://edilitics.com/ai/blog/private-mode-anonymization-accuracy-tradeoff))
- [The Proof of Concept Worked. Then the World Changed.](https://edilitics.com/blog/the-proof-of-concept-worked-then-the-world-changed) ([LLM Context](https://edilitics.com/ai/blog/the-proof-of-concept-worked-then-the-world-changed))
- [Why One AI Model Isn't Enough for Trustworthy Analytics](https://edilitics.com/blog/why-one-ai-model-isnt-enough) ([LLM Context](https://edilitics.com/ai/blog/why-one-ai-model-isnt-enough))
- [Why Self-Serve Analytics Fails (And What Works)](https://edilitics.com/blog/why-self-serve-analytics-fails) ([LLM Context](https://edilitics.com/ai/blog/why-self-serve-analytics-fails))
- [Why We Built What Nobody Wanted to Build.](https://edilitics.com/blog/why-we-built-what-nobody-wanted-to-build) ([LLM Context](https://edilitics.com/ai/blog/why-we-built-what-nobody-wanted-to-build))
- [Why Your AI Analytics Tool Doesn't Know Your Business](https://edilitics.com/blog/your-ai-analytics-tool-doesnt-know-your-business) ([LLM Context](https://edilitics.com/ai/blog/your-ai-analytics-tool-doesnt-know-your-business))
- [Apache Avro | Root Cause & Forecast](https://edilitics.com/integrations/avro-connector) ([LLM Context](https://edilitics.com/ai/integrations/avro-connector))
- [BigQuery | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/bigquery-connector) ([LLM Context](https://edilitics.com/ai/integrations/bigquery-connector))
- [CSV | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/csv-connector) ([LLM Context](https://edilitics.com/ai/integrations/csv-connector))
- [Databricks | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/databricks-connector) ([LLM Context](https://edilitics.com/ai/integrations/databricks-connector))
- [Excel | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/excel-connector) ([LLM Context](https://edilitics.com/ai/integrations/excel-connector))
- [Apache Feather | Root Cause & Forecast](https://edilitics.com/integrations/feather-connector) ([LLM Context](https://edilitics.com/ai/integrations/feather-connector))
- [SAP HANA | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/hana-connector) ([LLM Context](https://edilitics.com/ai/integrations/hana-connector))
- [JSON | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/json-connector) ([LLM Context](https://edilitics.com/ai/integrations/json-connector))
- [MongoDB Atlas | Root Cause & Forecast](https://edilitics.com/integrations/mongodb-atlas-connector) ([LLM Context](https://edilitics.com/ai/integrations/mongodb-atlas-connector))
- [MongoDB | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/mongodb-connector) ([LLM Context](https://edilitics.com/ai/integrations/mongodb-connector))
- [MySQL | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/mysql-connector) ([LLM Context](https://edilitics.com/ai/integrations/mysql-connector))
- [Cloud SQL MySQL | Root Cause & Forecast](https://edilitics.com/integrations/mysql-gcp-connector) ([LLM Context](https://edilitics.com/ai/integrations/mysql-gcp-connector))
- [Apache Parquet | Root Cause & Forecast](https://edilitics.com/integrations/parquet-connector) ([LLM Context](https://edilitics.com/ai/integrations/parquet-connector))
- [PDF | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/pdf-connector) ([LLM Context](https://edilitics.com/ai/integrations/pdf-connector))
- [Python Pickle | Root Cause & Forecast](https://edilitics.com/integrations/pickle-connector) ([LLM Context](https://edilitics.com/ai/integrations/pickle-connector))
- [PostgreSQL | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/postgres-connector) ([LLM Context](https://edilitics.com/ai/integrations/postgres-connector))
- [Cloud SQL Postgres | Root Cause & Forecast](https://edilitics.com/integrations/postgres-gcp-connector) ([LLM Context](https://edilitics.com/ai/integrations/postgres-gcp-connector))
- [Redis | Real-Time Analytics Pipeline Without Custom Scripts](https://edilitics.com/integrations/redis-connector) ([LLM Context](https://edilitics.com/ai/integrations/redis-connector))
- [Redshift | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/redshift-connector) ([LLM Context](https://edilitics.com/ai/integrations/redshift-connector))
- [Google Sheets | Root Cause & Forecast](https://edilitics.com/integrations/sheets-connector) ([LLM Context](https://edilitics.com/ai/integrations/sheets-connector))
- [Snowflake | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/snowflake-connector) ([LLM Context](https://edilitics.com/ai/integrations/snowflake-connector))
- [SQLite | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/sqlite-connector) ([LLM Context](https://edilitics.com/ai/integrations/sqlite-connector))
- [SQL Server | Root Cause, Forecast & Decisions](https://edilitics.com/integrations/sqlserver-connector) ([LLM Context](https://edilitics.com/ai/integrations/sqlserver-connector))
- [Cloud SQL Server | Root Cause & Forecast](https://edilitics.com/integrations/sqlserver-gcp-connector) ([LLM Context](https://edilitics.com/ai/integrations/sqlserver-gcp-connector))
- [Edilitics vs Airbyte + Matillion + Looker: A Module-by-Module Comparison](https://edilitics.com/compare/airbyte-matillion-looker-alternative) ([LLM Context](https://edilitics.com/ai/compare/airbyte-matillion-looker-alternative))
- [Edilitics vs Fivetran + dbt + Metabase: A Module-by-Module Comparison](https://edilitics.com/compare/fivetran-dbt-metabase-alternative) ([LLM Context](https://edilitics.com/ai/compare/fivetran-dbt-metabase-alternative))
- [Edilitics vs Golden Analytics: Two Approaches to Governed AI Analytics](https://edilitics.com/compare/golden-analytics-alternative) ([LLM Context](https://edilitics.com/ai/compare/golden-analytics-alternative))
- [Compare Edilitics to Your Current Data Stack](https://edilitics.com/compare) ([LLM Context](https://edilitics.com/ai/compare))
- [Edilitics vs Spreadsheets & Scripts: The Hidden Cost of 'Free'](https://edilitics.com/compare/spreadsheets-scripts-alternative) ([LLM Context](https://edilitics.com/ai/compare/spreadsheets-scripts-alternative))
- [Edilitics vs Tableau / Power BI: Where 'Revenue' Stops Meaning One Thing](https://edilitics.com/compare/tableau-power-bi-alternative) ([LLM Context](https://edilitics.com/ai/compare/tableau-power-bi-alternative))
- [Edilitics vs Zoho Analytics: Ecosystem Bundle vs Governed AI Platform](https://edilitics.com/compare/zoho-analytics-alternative) ([LLM Context](https://edilitics.com/ai/compare/zoho-analytics-alternative))
- [AI Analytics for Agencies & Consultancies](https://edilitics.com/solutions/ai-analytics-for-agencies) ([LLM Context](https://edilitics.com/ai/solutions/ai-analytics-for-agencies))
- [Omnichannel Inventory Visibility, One Number](https://edilitics.com/solutions/ai-analytics-for-ecommerce) ([LLM Context](https://edilitics.com/ai/solutions/ai-analytics-for-ecommerce))
- [AI Analytics That Never Sends Customer Data to an LLM](https://edilitics.com/solutions/ai-analytics-for-fintech) ([LLM Context](https://edilitics.com/ai/solutions/ai-analytics-for-fintech))
- [Governed AI Analytics for Healthcare Tech](https://edilitics.com/solutions/ai-analytics-for-healthcare) ([LLM Context](https://edilitics.com/ai/solutions/ai-analytics-for-healthcare))
- [AI Analytics for B2B SaaS: 106 Tools, One Answer](https://edilitics.com/solutions/ai-analytics-for-saas) ([LLM Context](https://edilitics.com/ai/solutions/ai-analytics-for-saas))
- [AI Analytics for Startups & Lean Teams](https://edilitics.com/solutions/ai-analytics-for-startups) ([LLM Context](https://edilitics.com/ai/solutions/ai-analytics-for-startups))
- [AI Analytics Solutions by Industry & Team](https://edilitics.com/solutions) ([LLM Context](https://edilitics.com/ai/solutions))

## Documentation

- [Analysis Credits](https://edilitics.com/docs/askedi/analysis-credits) ([LLM Context](https://edilitics.com/ai/docs/askedi/analysis-credits))
- [Analysis View](https://edilitics.com/docs/askedi/analysis-view) ([LLM Context](https://edilitics.com/ai/docs/askedi/analysis-view))
- [Category Comparison](https://edilitics.com/docs/askedi/category-comparison) ([LLM Context](https://edilitics.com/ai/docs/askedi/category-comparison))
- [Charts](https://edilitics.com/docs/askedi/charts) ([LLM Context](https://edilitics.com/ai/docs/askedi/charts))
- [Decision Intelligence](https://edilitics.com/docs/askedi/decision-intelligence) ([LLM Context](https://edilitics.com/ai/docs/askedi/decision-intelligence))
- [Forecasting](https://edilitics.com/docs/askedi/forecasting) ([LLM Context](https://edilitics.com/ai/docs/askedi/forecasting))
- [AskEdi](https://edilitics.com/docs/askedi) ([LLM Context](https://edilitics.com/ai/docs/askedi))
- [Methodology Notes](https://edilitics.com/docs/askedi/methodology-notes) ([LLM Context](https://edilitics.com/ai/docs/askedi/methodology-notes))
- [Privacy Modes](https://edilitics.com/docs/askedi/privacy-modes) ([LLM Context](https://edilitics.com/ai/docs/askedi/privacy-modes))
- [Root Cause Analysis](https://edilitics.com/docs/askedi/root-cause-analysis) ([LLM Context](https://edilitics.com/ai/docs/askedi/root-cause-analysis))
- [Voice](https://edilitics.com/docs/askedi/voice) ([LLM Context](https://edilitics.com/ai/docs/askedi/voice))
- [What-If Analysis](https://edilitics.com/docs/askedi/what-if-analysis) ([LLM Context](https://edilitics.com/ai/docs/askedi/what-if-analysis))
- [14-Day Evaluation](https://edilitics.com/docs/getting-started/14-day-evaluation) ([LLM Context](https://edilitics.com/ai/docs/getting-started/14-day-evaluation))
- [Getting Started with Edilitics](https://edilitics.com/docs/getting-started) ([LLM Context](https://edilitics.com/ai/docs/getting-started))
- [Workspace and Roles](https://edilitics.com/docs/getting-started/workspace-and-roles) ([LLM Context](https://edilitics.com/ai/docs/getting-started/workspace-and-roles))
- [AI Column Insights](https://edilitics.com/docs/integrate/ai-column-insights) ([LLM Context](https://edilitics.com/ai/docs/integrate/ai-column-insights))
- [AI Readiness (AIR) Score](https://edilitics.com/docs/integrate/ai-readiness-score) ([LLM Context](https://edilitics.com/ai/docs/integrate/ai-readiness-score))
- [Data Quality Profiling](https://edilitics.com/docs/integrate/data-quality-profiling) ([LLM Context](https://edilitics.com/ai/docs/integrate/data-quality-profiling))
- [DQ Refresh & Schema Drift](https://edilitics.com/docs/integrate/dq-refresh) ([LLM Context](https://edilitics.com/ai/docs/integrate/dq-refresh))
- [Integrate](https://edilitics.com/docs/integrate) ([LLM Context](https://edilitics.com/ai/docs/integrate))
- [Sharing & Access Control](https://edilitics.com/docs/integrate/sharing) ([LLM Context](https://edilitics.com/ai/docs/integrate/sharing))
- [Audit Logs](https://edilitics.com/docs/security-and-governance/audit-logs) ([LLM Context](https://edilitics.com/ai/docs/security-and-governance/audit-logs))
- [Bring Your Own Key (BYOK)](https://edilitics.com/docs/security-and-governance/byok) ([LLM Context](https://edilitics.com/ai/docs/security-and-governance/byok))
- [Data Encryption](https://edilitics.com/docs/security-and-governance/data-encryption) ([LLM Context](https://edilitics.com/ai/docs/security-and-governance/data-encryption))
- [Security & Governance](https://edilitics.com/docs/security-and-governance) ([LLM Context](https://edilitics.com/ai/docs/security-and-governance))
- [Privacy & Context Modes](https://edilitics.com/docs/security-and-governance/privacy-context-modes) ([LLM Context](https://edilitics.com/ai/docs/security-and-governance/privacy-context-modes))
- [Code Editor](https://edilitics.com/docs/transform/code-editor) ([LLM Context](https://edilitics.com/ai/docs/transform/code-editor))
- [Compute and Billing](https://edilitics.com/docs/transform/compute-and-billing) ([LLM Context](https://edilitics.com/ai/docs/transform/compute-and-billing))
- [Hybrid Pipelines](https://edilitics.com/docs/transform/hybrid-pipelines) ([LLM Context](https://edilitics.com/ai/docs/transform/hybrid-pipelines))
- [Transform](https://edilitics.com/docs/transform) ([LLM Context](https://edilitics.com/ai/docs/transform))
- [Run History](https://edilitics.com/docs/transform/run-history) ([LLM Context](https://edilitics.com/ai/docs/transform/run-history))
- [Scheduling](https://edilitics.com/docs/transform/scheduling) ([LLM Context](https://edilitics.com/ai/docs/transform/scheduling))
- [Aggregations: Complete Reference for Edilitics Visualize](https://edilitics.com/docs/visualize/aggregations) ([LLM Context](https://edilitics.com/ai/docs/visualize/aggregations))
- [AI Dashboard Summary: Summarise Every Chart](https://edilitics.com/docs/visualize/ai-dashboard-summary) ([LLM Context](https://edilitics.com/ai/docs/visualize/ai-dashboard-summary))
- [AI Chart Generator: Auto-Generate Dashboards](https://edilitics.com/docs/visualize/auto-generate-charts) ([LLM Context](https://edilitics.com/ai/docs/visualize/auto-generate-charts))
- [No-Code Chart Builder: Build Your First Dashboard](https://edilitics.com/docs/visualize/build-your-first-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/build-your-first-chart))
- [Dashboard Assembly: Canvas, Layout, and Save](https://edilitics.com/docs/visualize/dashboard-assembly) ([LLM Context](https://edilitics.com/ai/docs/visualize/dashboard-assembly))
- [Dashboard Embed: Publish to Any Website](https://edilitics.com/docs/visualize/embed) ([LLM Context](https://edilitics.com/ai/docs/visualize/embed))
- [Dashboard Filters: Hidden Filters and Show Filters](https://edilitics.com/docs/visualize/filters) ([LLM Context](https://edilitics.com/ai/docs/visualize/filters))
- [Format Charts: Complete Options Guide](https://edilitics.com/docs/visualize/format-charts) ([LLM Context](https://edilitics.com/ai/docs/visualize/format-charts))
- [Edilitics Visualize: Dashboard and Chart Builder](https://edilitics.com/docs/visualize) ([LLM Context](https://edilitics.com/ai/docs/visualize))
- [Dashboard Sharing Guide: Internal and External](https://edilitics.com/docs/visualize/sharing-and-permissions) ([LLM Context](https://edilitics.com/ai/docs/visualize/sharing-and-permissions))
- [Data Store Integrations](https://edilitics.com/docs/integrate/data-stores) ([LLM Context](https://edilitics.com/ai/docs/integrate/data-stores))
- [Redis Integration](https://edilitics.com/docs/integrate/data-stores/redis) ([LLM Context](https://edilitics.com/ai/docs/integrate/data-stores/redis))
- [SQLite Integration](https://edilitics.com/docs/integrate/data-stores/sqlite) ([LLM Context](https://edilitics.com/ai/docs/integrate/data-stores/sqlite))
- [Google BigQuery Integration](https://edilitics.com/docs/integrate/data-warehouses/bigquery) ([LLM Context](https://edilitics.com/ai/docs/integrate/data-warehouses/bigquery))
- [Databricks Integration](https://edilitics.com/docs/integrate/data-warehouses/databricks) ([LLM Context](https://edilitics.com/ai/docs/integrate/data-warehouses/databricks))
- [Data Warehouse Integrations](https://edilitics.com/docs/integrate/data-warehouses) ([LLM Context](https://edilitics.com/ai/docs/integrate/data-warehouses))
- [Amazon Redshift Integration](https://edilitics.com/docs/integrate/data-warehouses/redshift) ([LLM Context](https://edilitics.com/ai/docs/integrate/data-warehouses/redshift))
- [SAP HANA Integration](https://edilitics.com/docs/integrate/data-warehouses/sap-hana) ([LLM Context](https://edilitics.com/ai/docs/integrate/data-warehouses/sap-hana))
- [Snowflake Integration](https://edilitics.com/docs/integrate/data-warehouses/snowflake) ([LLM Context](https://edilitics.com/ai/docs/integrate/data-warehouses/snowflake))
- [Integrate Avro Files with Edilitics](https://edilitics.com/docs/integrate/file-formats/avro) ([LLM Context](https://edilitics.com/ai/docs/integrate/file-formats/avro))
- [Integrate CSV Files with Edilitics](https://edilitics.com/docs/integrate/file-formats/csv) ([LLM Context](https://edilitics.com/ai/docs/integrate/file-formats/csv))
- [Integrate Excel Files with Edilitics](https://edilitics.com/docs/integrate/file-formats/excel) ([LLM Context](https://edilitics.com/ai/docs/integrate/file-formats/excel))
- [Integrate Feather Files with Edilitics](https://edilitics.com/docs/integrate/file-formats/feather) ([LLM Context](https://edilitics.com/ai/docs/integrate/file-formats/feather))
- [File Format Integrations for Offline Data](https://edilitics.com/docs/integrate/file-formats) ([LLM Context](https://edilitics.com/ai/docs/integrate/file-formats))
- [Integrate JSON Files with Edilitics](https://edilitics.com/docs/integrate/file-formats/json) ([LLM Context](https://edilitics.com/ai/docs/integrate/file-formats/json))
- [Integrate Parquet Files with Edilitics](https://edilitics.com/docs/integrate/file-formats/parquet) ([LLM Context](https://edilitics.com/ai/docs/integrate/file-formats/parquet))
- [Integrate PDF Files with Edilitics](https://edilitics.com/docs/integrate/file-formats/pdf) ([LLM Context](https://edilitics.com/ai/docs/integrate/file-formats/pdf))
- [Integrate Pickle Files with Edilitics](https://edilitics.com/docs/integrate/file-formats/pickle) ([LLM Context](https://edilitics.com/ai/docs/integrate/file-formats/pickle))
- [Google Sheets Integration](https://edilitics.com/docs/integrate/file-formats/sheets) ([LLM Context](https://edilitics.com/ai/docs/integrate/file-formats/sheets))
- [Database Integrations for Unified Workflows](https://edilitics.com/docs/integrate/databases) ([LLM Context](https://edilitics.com/ai/docs/integrate/databases))
- [Integrate MongoDB Atlas with Edilitics](https://edilitics.com/docs/integrate/databases/mongodb-atlas) ([LLM Context](https://edilitics.com/ai/docs/integrate/databases/mongodb-atlas))
- [Integrate MongoDB with Edilitics](https://edilitics.com/docs/integrate/databases/mongodb) ([LLM Context](https://edilitics.com/ai/docs/integrate/databases/mongodb))
- [Set Up Google Cloud MySQL Integration](https://edilitics.com/docs/integrate/databases/mysql-on-google-cloud) ([LLM Context](https://edilitics.com/ai/docs/integrate/databases/mysql-on-google-cloud))
- [Set Up MySQL Integration with Edilitics](https://edilitics.com/docs/integrate/databases/mysql) ([LLM Context](https://edilitics.com/ai/docs/integrate/databases/mysql))
- [Set Up Google Cloud PostgreSQL Integration](https://edilitics.com/docs/integrate/databases/postgresql-on-google-cloud) ([LLM Context](https://edilitics.com/ai/docs/integrate/databases/postgresql-on-google-cloud))
- [PostgreSQL Integration Guide](https://edilitics.com/docs/integrate/databases/postgresql) ([LLM Context](https://edilitics.com/ai/docs/integrate/databases/postgresql))
- [SQL Server (GCP) Integration Guide](https://edilitics.com/docs/integrate/databases/sql-server-on-google-cloud) ([LLM Context](https://edilitics.com/ai/docs/integrate/databases/sql-server-on-google-cloud))
- [Integrate SQL Server with Edilitics](https://edilitics.com/docs/integrate/databases/sql-server) ([LLM Context](https://edilitics.com/ai/docs/integrate/databases/sql-server))
- [Bin / Discretize Numerical Data](https://edilitics.com/docs/transform/no-code-operations/bin-discretize) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/bin-discretize))
- [Cast Data Types](https://edilitics.com/docs/transform/no-code-operations/cast-datatypes) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/cast-datatypes))
- [Column Aggregations](https://edilitics.com/docs/transform/no-code-operations/column-aggregations) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/column-aggregations))
- [Concat](https://edilitics.com/docs/transform/no-code-operations/concat) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/concat))
- [Conditional Column](https://edilitics.com/docs/transform/no-code-operations/conditional-column) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/conditional-column))
- [Datetime Aggregation](https://edilitics.com/docs/transform/no-code-operations/datetime-aggregations) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/datetime-aggregations))
- [Datetime Delta](https://edilitics.com/docs/transform/no-code-operations/datetime-delta) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/datetime-delta))
- [Drop Duplicate Rows](https://edilitics.com/docs/transform/no-code-operations/drop-duplicate-rows) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/drop-duplicate-rows))
- [Drop / Rename Columns](https://edilitics.com/docs/transform/no-code-operations/drop-rename-columns) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/drop-rename-columns))
- [Filter](https://edilitics.com/docs/transform/no-code-operations/filter) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/filter))
- [Find & Replace](https://edilitics.com/docs/transform/no-code-operations/find-and-replace) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/find-and-replace))
- [Flatten](https://edilitics.com/docs/transform/no-code-operations/flatten) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/flatten))
- [Group By](https://edilitics.com/docs/transform/no-code-operations/group-by) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/group-by))
- [No-Code Operations](https://edilitics.com/docs/transform/no-code-operations) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations))
- [Joins](https://edilitics.com/docs/transform/no-code-operations/joins) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/joins))
- [Manage Timezones](https://edilitics.com/docs/transform/no-code-operations/manage-timezones) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/manage-timezones))
- [Merge Columns](https://edilitics.com/docs/transform/no-code-operations/merge-columns) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/merge-columns))
- [Manage Nulls](https://edilitics.com/docs/transform/no-code-operations/null-values) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/null-values))
- [Pivot / Unpivot](https://edilitics.com/docs/transform/no-code-operations/pivot-unpivot) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/pivot-unpivot))
- [Round Off Values](https://edilitics.com/docs/transform/no-code-operations/round-off-values) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/round-off-values))
- [Sampling](https://edilitics.com/docs/transform/no-code-operations/sampling) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/sampling))
- [Sort / Order By](https://edilitics.com/docs/transform/no-code-operations/sort-order-by) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/sort-order-by))
- [Split Columns](https://edilitics.com/docs/transform/no-code-operations/split-columns) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/split-columns))
- [String Extract](https://edilitics.com/docs/transform/no-code-operations/string-extract) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/string-extract))
- [Text Case Conversion](https://edilitics.com/docs/transform/no-code-operations/text-case-conversion) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/text-case-conversion))
- [Window Functions](https://edilitics.com/docs/transform/no-code-operations/window-functions) ([LLM Context](https://edilitics.com/ai/docs/transform/no-code-operations/window-functions))
- [Area Chart](https://edilitics.com/docs/visualize/chart-types/area-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/area-chart))
- [Basic Bar Chart](https://edilitics.com/docs/visualize/chart-types/bar-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/bar-chart))
- [Box Plot](https://edilitics.com/docs/visualize/chart-types/box-plot) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/box-plot))
- [Bubble Chart](https://edilitics.com/docs/visualize/chart-types/bubble-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/bubble-chart))
- [Choropleth Map](https://edilitics.com/docs/visualize/chart-types/choropleth-map) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/choropleth-map))
- [Combo Chart](https://edilitics.com/docs/visualize/chart-types/combo-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/combo-chart))
- [Donut Chart](https://edilitics.com/docs/visualize/chart-types/donut-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/donut-chart))
- [Funnel Chart](https://edilitics.com/docs/visualize/chart-types/funnel-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/funnel-chart))
- [Gauge Chart](https://edilitics.com/docs/visualize/chart-types/gauge-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/gauge-chart))
- [Grouped Bar Chart](https://edilitics.com/docs/visualize/chart-types/grouped-bar-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/grouped-bar-chart))
- [Heatmap](https://edilitics.com/docs/visualize/chart-types/heatmap) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/heatmap))
- [Highlighted Table](https://edilitics.com/docs/visualize/chart-types/highlighted-table) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/highlighted-table))
- [Histogram](https://edilitics.com/docs/visualize/chart-types/histogram) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/histogram))
- [Horizontal Bar Charts Guide](https://edilitics.com/docs/visualize/chart-types/horizontal-bar-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/horizontal-bar-chart))
- [Horizontal Grouped Bar Chart](https://edilitics.com/docs/visualize/chart-types/horizontal-grouped-bar-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/horizontal-grouped-bar-chart))
- [Horizontal Stacked Bar Chart](https://edilitics.com/docs/visualize/chart-types/horizontal-stacked-bar-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/horizontal-stacked-bar-chart))
- [Chart Types](https://edilitics.com/docs/visualize/chart-types) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types))
- [KPI Card](https://edilitics.com/docs/visualize/chart-types/kpi-card) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/kpi-card))
- [Line Chart](https://edilitics.com/docs/visualize/chart-types/line-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/line-chart))
- [Nightingale Rose Chart](https://edilitics.com/docs/visualize/chart-types/nightingale-rose-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/nightingale-rose-chart))
- [Pie Chart](https://edilitics.com/docs/visualize/chart-types/pie-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/pie-chart))
- [Radar Chart](https://edilitics.com/docs/visualize/chart-types/radar-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/radar-chart))
- [Radial Bar Chart](https://edilitics.com/docs/visualize/chart-types/radial-bar-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/radial-bar-chart))
- [Sankey Diagram](https://edilitics.com/docs/visualize/chart-types/sankey-diagram) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/sankey-diagram))
- [Scatter Map](https://edilitics.com/docs/visualize/chart-types/scatter-map) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/scatter-map))
- [Scatter Plot](https://edilitics.com/docs/visualize/chart-types/scatter-plot) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/scatter-plot))
- [Semi Donut Chart](https://edilitics.com/docs/visualize/chart-types/semi-donut-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/semi-donut-chart))
- [Stacked Area Chart](https://edilitics.com/docs/visualize/chart-types/stacked-area-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/stacked-area-chart))
- [Stacked Bar Chart](https://edilitics.com/docs/visualize/chart-types/stacked-bar-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/stacked-bar-chart))
- [Stacked Line Chart](https://edilitics.com/docs/visualize/chart-types/stacked-line-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/stacked-line-chart))
- [Text Table](https://edilitics.com/docs/visualize/chart-types/text-table) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/text-table))
- [Tree Chart](https://edilitics.com/docs/visualize/chart-types/tree-chart) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/tree-chart))
- [Treemap](https://edilitics.com/docs/visualize/chart-types/treemap) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/treemap))
- [Word Cloud](https://edilitics.com/docs/visualize/chart-types/word-cloud) ([LLM Context](https://edilitics.com/ai/docs/visualize/chart-types/word-cloud))

## Blog

- [Decision Intelligence Told Me What to Do. It Couldn't Tell Me Why.](https://edilitics.com/blog/ai-analytics-root-cause-analysis-why-the-recommendation-isnt-enough) ([LLM Context](https://edilitics.com/ai/blog/ai-analytics-root-cause-analysis-why-the-recommendation-isnt-enough))
- [Anonymizing Your Data Isn't Free. Here's What It Actually Costs.](https://edilitics.com/blog/private-mode-anonymization-accuracy-tradeoff) ([LLM Context](https://edilitics.com/ai/blog/private-mode-anonymization-accuracy-tradeoff))
- [Why One AI Model Isn't Enough for Trustworthy Analytics](https://edilitics.com/blog/why-one-ai-model-isnt-enough) ([LLM Context](https://edilitics.com/ai/blog/why-one-ai-model-isnt-enough))
- [Why Your AI Analytics Tool Doesn't Know Your Business](https://edilitics.com/blog/your-ai-analytics-tool-doesnt-know-your-business) ([LLM Context](https://edilitics.com/ai/blog/your-ai-analytics-tool-doesnt-know-your-business))
- [Why Self-Serve Analytics Fails (And What Works)](https://edilitics.com/blog/why-self-serve-analytics-fails) ([LLM Context](https://edilitics.com/ai/blog/why-self-serve-analytics-fails))
- [Anthropic Published How They Built AI Analytics. Here's What We Found.](https://edilitics.com/blog/anthropic-validated-ai-analytics-architecture) ([LLM Context](https://edilitics.com/ai/blog/anthropic-validated-ai-analytics-architecture))
- [Why We Built What Nobody Wanted to Build.](https://edilitics.com/blog/why-we-built-what-nobody-wanted-to-build) ([LLM Context](https://edilitics.com/ai/blog/why-we-built-what-nobody-wanted-to-build))
- [I Asked for a Resource. He Saw Something Else.](https://edilitics.com/blog/i-asked-for-a-resource-he-saw-something-else) ([LLM Context](https://edilitics.com/ai/blog/i-asked-for-a-resource-he-saw-something-else))
- [The Proof of Concept Worked. Then the World Changed.](https://edilitics.com/blog/the-proof-of-concept-worked-then-the-world-changed) ([LLM Context](https://edilitics.com/ai/blog/the-proof-of-concept-worked-then-the-world-changed))
- [The 90/10 Problem Nobody in Data Talks About](https://edilitics.com/blog/analytics-without-a-data-team) ([LLM Context](https://edilitics.com/ai/blog/analytics-without-a-data-team))