Question 1

How do I know the answers aren't hallucinated?

Accepted Answer

AskEdi never answers from model memory. Before your first question, Integrate builds a governed semantic layer from your database metadata - column descriptions, table relationships, and Data Quality scores - that your team reviews and locks. AskEdi reasons only on that validated layer. A two-stage pipeline (Classifier + Synthesizer) produces deterministic SQL or aggregation queries that run live against your database. No free-form generation. No guessing. Every answer is traceable to a real query result.

Question 2

Does Edilitics send my data to an AI provider?

Accepted Answer

No. Your actual database rows never leave your infrastructure under any circumstance. Edilitics extracts only metadata - column names, data types, cardinality, null rates - to build the semantic layer. When AskEdi runs a query, the results are aggregated numbers used to render charts, not raw records. All database credentials are encrypted using PBKDF2-derived Fernet encryption before storage. In Private Mode, even column names are anonymized before reaching an external AI provider.

Question 3

How long does it actually take to get a first answer?

Accepted Answer

Under 5 minutes from a fresh connection. Connect your database, Edilitics reads the schema and runs an automatic data quality check, the AI drafts plain-English column definitions, your team reviews and approves them (or skips to let defaults stand), and AskEdi is ready. The 5-minute figure assumes a small-to-medium schema. Larger schemas with hundreds of tables take longer to review - but you can approve a subset of tables and start querying immediately.

Question 4

Do I still get charged if the query returns a wrong result?

Accepted Answer

You are charged when the AI pipeline successfully produces and executes a query - full stop, regardless of what the query returns. If your query runs but returns zero rows because the data genuinely does not exist, one credit is consumed - the pipeline did its job correctly. No credits are deducted only when the pipeline itself fails: the LLM API call errors out, the LLM returns a response that can't be parsed into a valid query at all, or the question is out of scope. Wrong results caused by bad data quality in your database are a data problem, not a billing error.

Question 5

We already have a BI tool. Why would we use this instead?

Accepted Answer

Edilitics is not a replacement for your BI dashboards - it is what you reach for when the dashboard does not have the answer. BI tools answer the questions someone predicted you would ask, days or weeks ago. AskEdi answers the question you are thinking of right now, in plain English, with a live query against current data. The two complement each other: dashboards for known KPIs, AskEdi for ad-hoc questions and decision-type analysis that would otherwise go to the data team.

Question 6

Is this for our data team or for the business users?

Accepted Answer

Both play a role, but in different phases. Your data team connects integrations via Integrate and approves column definitions - a one-time setup that takes under an hour per data source. After that, business users - founders, heads of finance, ops leads, CMOs - ask questions independently in plain English without opening a ticket. The data team stops fielding ad-hoc requests. Business users stop waiting. That is the shift Edilitics is built for.

Question 7

We already run Fivetran, dbt, a warehouse like Snowflake or BigQuery, and a BI tool like Looker or Tableau. What does Edilitics actually replace?

Accepted Answer

Edilitics replaces three layers with one product: the connector layer (Fivetran/Airbyte), the transformation layer (dbt), and the query/dashboard layer (Looker/Tableau/Power BI) - and connects directly to an existing warehouse (Snowflake, BigQuery, Redshift) as a data source rather than replacing it. Integrate connects the same source databases and warehouses, Transform does no-code ETL in place of dbt models, and AskEdi/Visualize replace writing SQL or building Looker explores to get an answer. The market itself is consolidating this way: Fivetran and dbt Labs announced their merger in October 2025, citing 1,500+ shared customers and 80-90% of Fivetran's customers already running dbt. Separately, multiple data engineering teams reported the Fivetran 2025 billing model change increased their connector costs 40-70%, with some bills doubling or quadrupling and high-volume deployments reported in the $15,000-$30,000/month range for the connector layer alone, before a single dashboard or AI query is built on top. Edilitics' highest published Team tier (Pinnacle) is $179/user/month with all four modules included.

Question 8

How do I know an AI analyst isn't hallucinating numbers, and what's the actual accuracy difference a semantic layer makes?

Accepted Answer

Anthropic, the maker of Claude, published their own internal architecture for this exact problem in June 2026: without a structured, human-validated semantic layer, Claude's accuracy on self-service analytics queries did not exceed 21%. With one, accuracy was consistently above 95%. That 21%-to-95% gap is not a vendor claim - it is Anthropic's own published, sourced result (see Anthropic, 'How Anthropic enables self-service data analytics with Claude,' June 2026). Edilitics is built on the same architectural conclusion: AskEdi never reasons from raw schema or model memory. It reasons only against a governed semantic layer - human-validated column descriptions and metric definitions - via a two-stage pipeline (Classifier resolves intent against that layer, Synthesizer generates a deterministic query from it). Every query that runs is shown in the Analysis View so the exact SQL or aggregation pipeline can be independently verified, not just trusted.

Question 9

Can the AI accidentally show a user data they're not authorized to see?

Accepted Answer

A documented CFO-level concern with AI analytics tools is that AI workflows querying data through a separate connection can bypass the role-based permissions that govern human users, letting a model query data a human user would not be authorized to see. AskEdi avoids that failure mode structurally: it never receives raw rows in the first place, reasoning only on schema metadata and DQ statistics, and the query it generates executes against your database using your own credentials and existing database-level permissions - not a separate AI-granted access path. There is no separate AI data-access layer with broader permissions than your database already enforces.

Question 10

What does it actually cost us to keep waiting on the data team instead of switching?

Accepted Answer

Data teams are widely cited as the single biggest bottleneck in self-service analytics delivery, fielding one-off requests that should not require an engineering ticket - a 2025 industry survey found 55% of US marketers said fragmented data had directly cost their company revenue (source: Funnel, 'Self-serve analytics will free marketing from IT and data bottlenecks,' 2025). Edilitics removes that bottleneck directly: business users ask AskEdi questions in plain English without filing a ticket, and the data team's one-time setup work (connecting integrations, approving column definitions) is typically under an hour per data source.

Question 11

Anthropic makes Claude - why would Anthropic's own findings about AI analytics architecture be relevant to evaluating Edilitics?

Accepted Answer

Because Anthropic faced the identical problem - making AI analytics trustworthy enough to act on - and published their internal architecture and accuracy results in June 2026, independently of Edilitics. They identified three failure modes (concept-entity ambiguity, data staleness, unstructured retrieval) and converged on four architectural decisions: human-validated semantic layers, continuous data freshness checks, structured retrieval over raw query-corpus access, and separating intent classification from query generation. Edilitics was built on the same four decisions before that post existed; the post is independent third-party validation that the approach is correct, not the source of the approach. Anthropic is also one of four LLM providers AskEdi can run on (alongside OpenAI, Sarvam, and Google DeepMind) - available on the Pinnacle tier and via BYOK on qualifying plans. Full comparison: /blog/anthropic-validated-ai-analytics-architecture.

Question 12

Why not just upload our data to ChatGPT instead of using AskEdi?

Accepted Answer

ChatGPT has no live connection to a database - its Advanced Data Analysis feature works only on files you upload (capped at roughly 512MB per file, about 50MB for a CSV or spreadsheet, up to 10-20 files per conversation), and any explanation of what your columns mean lives only in that one chat thread, not as a reusable, auditable semantic layer. A real company's orders, transactions, or customer table routinely exceeds those upload limits, so the file analyzed is already a sample, not the live dataset - and it does not persist for the next person who asks a question or update when the underlying data changes. Anthropic's own published research found that without a structured, persistent semantic layer, AI accuracy on analytics questions did not exceed 21% - the same failure mode a single ChatGPT thread reproduces by design, since nothing in that thread is reusable, auditable, or kept current. AskEdi instead reasons against a semantic layer built once in Integrate, validated by your team, reused by every question from every user, and kept current through automatic profiling - with every query that runs visible in the Analysis View for anyone to check.

Question 13

Why not just upload our data to Claude instead of using AskEdi?

Accepted Answer

Claude has no live connection to a database - Claude Projects accepts file uploads up to 30MB each, bounded overall by a roughly 200K-token context window, and any context or business-rule explanation provided to ground its answers lives inside that one Project rather than as a governed, team-wide semantic layer. That setup covers a sample, not a live, continuously updated business database: it does not update automatically when the source data changes, and the work of explaining what each column means has to be maintained by whoever owns that Project, not validated and shared across a team. Anthropic's own published architecture research is explicit on why this matters: without a structured, persistent, human-validated semantic layer, Claude's accuracy on analytics questions did not exceed 21%, rising to 95%+ only when that layer is continuously maintained and validated - an infrastructure commitment, not something a single Project's uploaded files achieve on their own. AskEdi reasons against exactly that kind of governed, continuously profiled semantic layer, built once in Integrate and reused by every question, with every query traceable in the Analysis View.

Question 14

Why not just use Gemini in Google Sheets instead of using AskEdi?

Accepted Answer

Gemini in Sheets has no live connection to a database - it only analyzes data already sitting in a spreadsheet, with a practical ceiling around 50MB or roughly 1,000 rows, so getting a business's actual data in front of it means exporting and re-importing into Sheets every time the underlying data changes. Whatever explanation of column meanings or business rules someone adds to make Gemini's answers reliable lives in that spreadsheet alone, not as a governed layer shared and reused across a team, and there is no record of the exact computation behind a given answer that a second person could independently verify. AskEdi instead queries a live database directly, reasoning against a semantic layer built once in Integrate and validated by the team, with every executed query shown in the Analysis View - no manual export, no per-spreadsheet context rebuild, and no row ceiling tied to a single file.

Question 15

What are the penalties under India's DPDP Act, and why does that matter for an AI analytics tool?

Accepted Answer

India's Digital Personal Data Protection (DPDP) Rules, 2025 were notified on November 13, 2025, with the Data Protection Board of India now active and full compliance required by May 13, 2027. Penalties are severe and specific: up to ₹250 crore for a Data Fiduciary's failure to maintain reasonable security safeguards, and up to ₹200 crore per incident for failing to notify a breach (notification to the Board is required within 72 hours, with logs retained for one year). This matters for an AI analytics tool specifically because every additional system that touches personal data - including an AI provider a query passes through - is a potential point of exposure under these obligations. Edilitics is built to send zero raw database rows to any AI provider: AskEdi extracts only schema metadata and statistical profiles, and all connected database credentials are encrypted with PBKDF2-derived Fernet encryption before storage. Source: PIB, DPDP Rules 2025 notification, November 2025; Tsaaro, 'Enforcement and Penalties under the DPDPA 2023 and Draft DPDP Rules 2025.'

Question 16

Does using Edilitics make us DPDP-compliant?

Accepted Answer

Edilitics' zero-raw-data architecture removes one specific exposure: raw personal data rows are never transmitted to any AI provider in AskEdi's pipeline, so the AI layer of your analytics stack is not an additional place personal data can leak or require separate breach disclosure. Full DPDP compliance is a broader organizational responsibility - your own data fiduciary obligations, consent records, and breach response process - that sits with your organization, not with any single tool in your stack. This describes what the architecture achieves, not a compliance certification.

Question 17

What does RBI's FREE-AI framework require, and does Edilitics satisfy it?

Accepted Answer

The Reserve Bank of India published its FREE-AI (Framework for Responsible and Ethical Enablement of AI) framework in August 2025, organized around seven principles for financial institutions deploying AI: Governance, Protection, Assurance, Fairness, Explainability, Auditability, and Resilience. Edilitics was not built to satisfy FREE-AI specifically and makes no claim of RBI certification or endorsement. What is architecturally true and verifiable: every AskEdi query is logged and viewable in the Analysis View (relevant to Auditability), the AI never receives raw transaction-level personal data (relevant to Protection), and the two-stage Classifier/Synthesizer pipeline produces deterministic, traceable queries rather than free-form generation (relevant to Explainability). Source: RBI FREE-AI framework, August 2025.

Question 18

Does Edilitics' RBI-relevant architecture mean a fintech's FREE-AI compliance program is satisfied?

Accepted Answer

No - whether Edilitics' architecture satisfies a specific institution's FREE-AI compliance program is a determination that institution's own compliance team must make, not a guarantee Edilitics can offer. Edilitics is describing what the platform architecturally does (query logging, no raw personal data to AI providers, deterministic queries) and mapping those facts to FREE-AI's stated principles for context. It is not issuing or implying RBI certification, audit sign-off, or regulatory approval of any kind.

Question 19

Why not just hire a data analyst instead of paying for a platform?

Accepted Answer

A 2025 ManpowerGroup Talent Shortage Survey of 3,000+ Indian employers found 80% struggling to find skilled talent, above the 74% global average; data and AI-focused roles reportedly command a 1.4-1.8x salary premium over standard software engineering roles, and data-focused positions take an average of 51 days to fill - 10 days longer than the overall labor market average. A mid-level Indian data analyst salary band runs roughly ₹6-12 LPA (₹50,000-100,000/month) before accounting for the multi-month hiring cycle and onboarding ramp-up; a senior hire commands more, and that is one person with no redundancy if they leave. Edilitics' Team plans range from ₹7,499 to ₹14,999/user/month and include four modules - Integrate, Transform, AskEdi, Visualize - operating continuously with no hiring cycle, no attrition risk, and no single point of departure, absorbing the ad-hoc 'can you pull this number' request cycle a hired analyst would otherwise handle. Source: ManpowerGroup 2025 Talent Shortage Survey (India); India data analyst salary ranges per Coursera, Indeed, and Amity Online 2026 guides.

Question 20

Most of what I see in India is Tableau-to-Power BI migration, not platform consolidation. Why is Edilitics different from that move?

Accepted Answer

The dominant BI migration in India in 2025-2026 has been Tableau to Power BI, driven largely by Microsoft Fabric standardization and per-user cost savings inside Global Capability Centres (GCCs) - but that is a lateral move from one dashboard tool to a cheaper dashboard tool, and both still require someone to predict the question and build the report in advance. Edilitics solves the problem underneath that migration: Integrate connects and governs the data, Transform replaces dbt-style engineering with no-code pipelines, and AskEdi answers ad-hoc questions in plain English without a pre-built report - with Visualize covering the dashboard use case as well. The bottleneck a BI-tool migration does not remove - waiting on someone to build the report or query in the first place - is exactly what Edilitics is built for. Source: industry BI migration trend reporting on Tableau-to-Power BI moves in India GCCs, 2025-2026.

Question 21

Can a data analytics services provider or agency use Edilitics to service more clients with the same team?

Accepted Answer

Yes, directionally: Edilitics removes the recurring manual work that consumes agency staff time across multiple clients. Dashboard report creation and maintenance is a documented time cost in services delivery - one industry estimate puts manual report creation at roughly 2 hours per client per month, which across 20 clients is about 40 hours per month, the equivalent of a full-time role. Edilitics addresses that work directly: Integrate connects each client's data source once and keeps it profiled automatically, Transform replaces engineer-built per-client ETL with a no-code pipeline builder, dashboards in Visualize refresh on a schedule instead of being manually rebuilt, and AskEdi answers a client's ad-hoc question in plain English without an analyst opening a query editor. The resulting capacity - serving more clients with the same staff, or the same clients with less manual effort - depends on each agency's delivery model and client mix.

Stop waiting on the data team
AskEdi answers instantly, with zero raw rows ever reaching the AI.

Ask in natural language.
From question to decision in seconds.

How It Works

How Edilitics makes sure every answer
is correct before you see it.

Read structure only

Automatic health check

AI writes plain-English definitions

Your team approves

Verified answers only

Other AI analytics tools guess. AskEdi asks your database.

24 Live Connectors

Nothing Moves.
Everything Gets Checked.

Connect Without Moving Anything

Automatic Accuracy Check

Reliability Score Before You Ask

AI Translates Your Columns

You Approve The Final Definitions

Catches Changes In Your Data

Three Privacy Modes

You control exactly what the AI sees.

Private Mode

Balanced Mode

Full Context Mode

AskEdi

Not just answers.
Decisions.

Five Analysis Types, Picked Automatically

Role-Aware Persona

30+ Chart Types, Live

Charged Only On Success

Voice to Insight

@ Column Targeting

The Full Platform

Start Anywhere. Grow Into It.

Everything you need to know before you decide.

Stop stitching five tools together. Start with one.

Stop waiting on the data teamAskEdi answers instantly, with zero raw rows ever reaching the AI.

Ask in natural language.From question to decision in seconds.