Operations · Frontier · updated 2026-10-23

Ask-your-data analyst agent

Team members ask questions in plain English; an agent writes SQL against your warehouse, runs it, checks the result and explains it, in several reasoning phases.

Building block of the Company brain: docs, data and tickets in Slack.

1Is this my problem?

Founders and ops leads wait days for simple data questions. A careful analyst agent answers many of them in minutes.

You'll recognise this if…

Data requests wait days.
Analysts answer simple questions.
Dashboards don't cover the question.

Companies affectedCompanies with a warehouse and a small data team. Typical fit: 50 staff.

Cost of doing nothing (estimate)Roughly 3–10 hours a week of manual work stays on the team, and the work is done inconsistently.

2Is this solution worth it?

The sources describe how this is built but publish no hard results, so the figures below are our estimates.

Your ROI, with the assumptions shown

Prefilled with illustrative assumptions (8 h/week at $80/h, 40% automated, build $15,000). Change them to your numbers.

Time saved (estimate)3–10 h/week

Typical build cost (estimate)$6k–$20k

DifficultyAdvanced

Steps6

Limits and when not to build it

Skip it if: No clean warehouse.

Read-only credentials only, with row limits.
Ship a semantic layer (approved metric definitions) or answers will disagree with finance.

3How does it work?

One small internal tool does this for you. The same job, before and after:

Before · by hand

Question in Slack / web: someone notices
Read schema + metric definitions
Plan and write SQL (by hand)
Run on read-only replica
Check result, explain (by hand)
Return table / chart

Ask-your-data analyst agentIllustrative

Starts automatically: question in slack / web
Read schema + metric definitions
AI: Plan and write SQL
Run on read-only replica
AI: Check result, explain
Return table / chart

✦ AI step · ● human checkpoint · ✓ automatic

The AI's job here

Translating questions into correct SQL and noticing when a result looks wrong.

Plan and write SQL
Check result, explain

Your tool connects to

Slack / TeamsData warehouseAI model

Under the hood: flow, architecture and vendors

When: Question in Slack / web
Read schema + metric definitions
Plan and write SQL
Run on read-only replica
Check result, explain
Return table / chart

Architecture

Data sourcesWarehouse schema, metric docs

ModelFrontier model, multi-phase

LogicPlan → SQL → execute → verify → explain

Human checkpointAnalyst reviews new metrics; users see the SQL

Vendors commonly used

Vercel AI SDKBigQueryPostgresSnowflakeSlackClaude

Maturity ladder

What each level adds. Most teams start at level 1 or 2 and climb. Highlighted: where this build sits.

Level 1Basic automation
Trigger: question in slack / web. Fixed rules then read schema + metric definitions, run on read-only replica, return table / chart. No AI involved.
Level 2AI-assisted
An LLM handles the judgement step (plan and write sql) and a person reviews every output before it is used.
Level 3Autonomous agent
The agent runs end to end on its own and only stops for exceptions. Analyst reviews new metrics; users see the SQL.
Level 4Self-learning system
Becomes part of the company brain: docs, data and tickets in slack: Thumbs-down answers and unanswered questions become doc tasks and eval cases, so the bot is measured on real misses.

Level 4 lives in the Company brain: docs, data and tickets in Slack: 6 stages with a learning loop.

Seen in the wild

Vercel: what we learned building agents at Vercel

Links checked 2026-10-23. We describe these builds from public material; we are not affiliated with these companies.

4Who can build it?

Matched on services, platforms and industries agencies list on their own sites, and on their published projects.

Softweb SolutionsUnited States