For data teams using Claude Code & Codex

Give data science its time back.

AI answers are fast. The cleanup isn’t.

ChatData forces Claude Code and Codex to use trusted metrics, run validation, show caveats, and save reusable context. Data teams spend less time correcting AI output and more time on decisions that move the business.

Start 7-day free trial See what changes

Created by Paras Doshi. Free for 7 days, then $49/month.

Works with
ChatData · Answer Receipt
/chatdata:investigate why did WAU drop 12% last week?
Metric✓ WAU (7-day, deduplicated, ex-internal)
Source✓ analytics.dbt_prod · refreshed 2h ago
Validation✓ Row count +0.3% vs. 4-week avg
Caveat⚠ iOS SDK lag — Mon events arrive +24h
DriverWeb cohort −18% · mobile flat
ConfidenceHigh · approved answer path used
Saved✓ WAU-drop path → company context layer

The problem

AI made analysis faster. It also created a new cleanup tax.

Every wrong metric, missing caveat, or stale source lands back on the data team. The agent sounds confident. The data scientist pays the cleanup bill.

📐

Wrong metric definitions

Claude uses the metric name, not your business definition. “Active users” means five different things depending on the team. Only one of them is right.

🔳

No caveats surfaced

The agent doesn’t know your data has a 2-day lag, that Q1 had a schema change, or that mobile is excluded. The answer looks clean. It isn’t.

🔁

Fixes don’t compound

You correct the same wrong answer every week. Nothing sticks. The next person hits the same wall. The cleanup loops forever.

How it works

Five checks run before any answer leaves the agent.

ChatData intercepts the question, resolves trusted context, and gates the answer. No prompting required — it runs automatically inside Claude Code and Codex.

1

Right metric definition?

Resolves against your governed metric registry, not what the agent guessed from the schema.

Metric trust
2

Source trusted and fresh?

Checks the approved source and last-refreshed timestamp before any answer is formed.

Freshness
3

Data passed quality checks?

Runs row counts, null checks, and expected-range validation before drawing conclusions.

Validation
4

Caveats visible?

Known exceptions, lags, and schema quirks are attached to the answer automatically.

Caveats
5

Fix saved for next time?

Approved answer paths go into your private context layer. Every correction compounds.

Memory
✗ Without ChatData
🤖
Claude answers confidently

Wrong grain. No caveat. Sounds right.

😲
Data scientist finds the error

Number doesn’t match the dashboard.

🔧
Cleanup loop begins

Recalculate, add caveats, re-share.

🔁
Same question next week

Nothing was saved. Repeat.

✓ With ChatData
🔍
Resolves the right metric

Governed definition, approved source, correct grain.

📌
Attaches caveats automatically

iOS lag, schema note, freshness — all included.

✅
Answer is ready to share

No cleanup needed. Cite source and caveat.

🧠
Path saved for next time

Context layer updated. Next question is faster.

The time math

Put a number on cleanup and rework.

Fewer cleanup loops means more time for the analysis that actually moves decisions. These are estimates, not telemetry claims — your mileage varies.

~10h saved per data scientist per month from fewer cleanup loops
5× faster answer delivery on recurring questions once paths are saved
$0 new dashboards required — works on top of what you already have

Comparison

ChatData vs. unguided AI on real data work.

Run the same metric question twice: once as a normal agent prompt, once through ChatData. The useful delta is less cleanup, clearer caveats, validated numbers, and reusable context.

Uses your metric definitionsGuesses from schemaGoverned registry
Checks data freshnessAssumes currentTimestamp verified
Surfaces known caveatsOmits by defaultAuto-attached
Validates data qualityNo checksPre-answer validation
Saves fixes for reuseStatelessAnswer paths compounded

Pricing

Try it on real work before you pay.

The first week is free. No card required. After that, ChatData is $49/month. Bring a metric question you want to trust.

7-day trial

$0for 7 days
  • Claude Code + Codex trust layer
  • 5 metric trust packets
  • Private context layer
  • Answer receipts
  • Validation + caveats
Start free trial

Individual

$49/ month
  • Unlimited metric packets
  • Compounding answer memory
  • Private GitHub context repo
  • WBR prep automation
  • Direct support from Paras
Start free trial

Trial access

Bring one metric question you want to trust.

Tell me what piece of data work you want ChatData to fix first. No card. Trial access opens immediately.

I read every reply personally. If your first question has messy metrics, messy sources, or a skeptical audience, say so. That is where ChatData is most useful.

Company context will use your email domain. Context id: ChatData-your-domain.

By starting the trial, you allow ChatData to sync analytics metadata: SQL patterns, metric definitions, answer paths, and caveats. ChatData does not sync query results, credentials, PII, or raw row data.

No card. Trial access opens immediately.