Talk to Data

Why Talk to Data — and not legacy BI, an AI-BI add-on, or a chatbot.

The positioning in one page: the shift this creates, how it beats the alternatives, and the moats a competitor structurally can't copy. For when someone's already weighing Talk to Data against what they have today. (New to the product? Get the Product One-Pager.)

The problem today

Business teams wait days for answers that live one query away — stuck behind an analyst queue or a dashboard nobody pre-built for this question. And a generic chatbot can't be trusted on real company data: it doesn't know your schema, your definitions, or your access rules.

→
With Talk to Data

Anyone asks in plain English and gets a trustworthy answer instantly — the number, the chart, and the SQL behind it — governed and audited, so it's safe on production data. Self-service analytics without the BI project.

Why it's different (what competitors structurally can't copy)

1

Agents that learn your business

Correct it once — "Region A = North", "active user = logged in within 30 days" — and it remembers, scoped per agent. Accuracy compounds with use.

Compounding switching cost
2

MCP-native

Query your data — and your trained agents — from inside the AI assistants your team already uses, with no separate app and no tab-switching. Still rare among BI tools, and a channel most haven't built for.

Novel channel + moat
3

Governed by design

Tenant isolation, roles & groups, row filtering / column masking, SSO, MFA, immutable audit log — and the AI only ever writes read-only, validated SQL.

Passes the security review

Versus the alternatives

Instead of…Talk to Data
The analyst report queueBusiness users self-serve answers in seconds; analysts stop being a ticket desk.
Legacy BIAnswers new questions the moment they come up — no pre-built dashboard, no BI project to stand up.
A generic AI chatbot on your dataKnows your schema, your business definitions, and your governance — and shows the exact query, so results are auditable, not a black box.

Who it's for

  • Ops / analytics leads at 20–100-person B2B SaaS on Postgres/MySQL with no dedicated analyst — the report-queue pain is acute.
  • AI-forward teams already working in AI assistants — reach your data through the MCP wedge.
  • Teams frustrated with heavy legacy BI who want real self-service + AI.

Why they stay

  • Every answer ships with the SQL that produced it — trust, not a black box.
  • Agents accumulate your team's rules & corrections into an asset that compounds.
  • Connect a database and get a useful answer on your own data in your first session — most teams the same day.
Security & governance controls are implemented and documented; a SOC 2 attestation is not yet held, and the platform is built ready for that process. Row filtering & column masking are enforced at the application layer. Hosted SaaS with optional bring-your-own-AI-key (not self-hosted). No contractual uptime SLA is implied.