Question 1

My company doesn't fit neatly into any of these six categories - is Edilitics still a fit?

Accepted Answer

Likely yes. These six reflect where Edilitics is used most, not a hard boundary on who it works for. The underlying platform - connecting your data, cleaning it, asking questions in plain English, and building dashboards - is the same regardless of industry or team size; the use cases differ in which privacy setting or AI capability a team leans on hardest, not in what the product can do.

Question 2

We're a regulated fintech team that's also growing fast like a startup - which use case actually applies to us?

Accepted Answer

Both, and that's normal. A team can be compliance-driven and resource-constrained at the same time. Start with whichever page matches the sharper pain right now - Privacy Modes and audit trail if regulatory scrutiny is the immediate concern, or the startup page if the immediate blocker is not having a data team yet. Nothing about the setup changes based on which page you start from.

Question 3

How is Edilitics different from other AI-native BI tools that also claim to keep data private?

Accepted Answer

Most AI-native BI tools that make a privacy claim still send a sample of your actual data rows to the LLM for context. Edilitics sends zero raw data rows to any LLM provider, in any of its three privacy modes - the AI reasons only on schema, statistics, and human-validated column descriptions, never on sampled records.

Question 4

Is AI analytics safe for regulated data like HIPAA or PCI-DSS records?

Accepted Answer

It depends entirely on what the vendor sends to the underlying AI model. Edilitics never transmits raw database rows to any LLM provider in any privacy mode - the AI reasons only on schema metadata, Data Quality statistics, and human-validated column descriptions. Private mode goes further and anonymizes column names themselves before they reach the AI. This describes the architecture, not a specific compliance certification - check the fintech and healthcare use cases for the privacy-mode detail relevant to your regulatory context.

Question 5

Do I need a data team to use AI analytics?

Accepted Answer

No, not to get started. Connect a source and it's graded automatically for data quality and AI-readiness - no engineer required to write that logic. A data team's role, when one exists, shifts to a one-time setup (connecting sources, approving AI-generated column definitions) rather than fielding every ad-hoc request afterward. The startup and lean-teams use case covers this in more depth.

Question 6

Is Edilitics built for my specific industry, or is it a generic BI tool with different branding per page?

Accepted Answer

It's the same platform underneath every use case - connecting data, cleaning it, asking questions in plain English, and building dashboards. What differs by industry is which capability a team leans on hardest: regulated fintech and healthcare teams lean on privacy settings and answer traceability, e-commerce teams lean on cleanup for messy multi-source data, and B2B SaaS teams lean on recommended-action framing for what to do next. The pages describe different entry points into the same auditable system, not different products.

Question 7

Can an agency use one analytics tool across multiple clients without their data mixing together?

Accepted Answer

Yes. Each connected source is profiled and governed independently - one client's schema, data, and AI context never mix with another's, even within the same Edilitics workspace. See the agencies use case for how this maps to onboarding and per-client data isolation specifically.

Question 8

How much does AI analytics cost for a small team?

Accepted Answer

Individual Launch starts at $49/mo for a single seat, covering 40 AI analyses, 60 minutes of data cleanup compute, and 2 data sources - the full platform included, not a stripped-down tier. Team plans start at $89/user/month and include free viewer seats for the rest of the team the moment the first full seat is purchased. Full pricing detail is on the pricing page.

Question 9

Is one AI analytics tool enough for multiple teams or departments, or do we need separate tools per use case?

Accepted Answer

One workspace covers this by design. Connected sources, cleanup pipelines, and AI query sessions are all shared within a workspace, so finance, ops, and a data team can all query the same governed sources without duplicating connections or standing up separate tools per department.

Question 10

What actually changes about the setup depending on which use case matches my team?

Accepted Answer

Very little at the infrastructure level - connecting a source, grading it for data quality, and asking it questions in plain English works identically regardless of industry. What changes is which privacy setting you'd choose by default (the strictest option for the most sensitive fintech/healthcare data, a lighter default for most other teams), and which AI capability gets used most often (recommended-next-action framing for SaaS teams, a checkable methodology behind every number for healthcare teams needing to verify it before trusting it).

Six teams.
Six different problems. One page to find yours.

One system, fully auditable.
Six different starting points.

Everything you need to know before you decide.

Still not sure which one is you?

Six teams.Six different problems. One page to find yours.

One system, fully auditable.Six different starting points.

Everything you need to know before you decide.

Still not sure which one is you?

Six teams.
Six different problems. One page to find yours.

One system, fully auditable.
Six different starting points.