For data teams using Claude Code & Codex

Give data science its time back.

AI answers are fast. The cleanup isn’t.

ChatData routes Claude Code and Codex through trusted metrics, validation, caveats, and 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

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
Answer stateValidated · 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 resolves trusted context and gates answers inside the command flow. No extra prompting required after setup.

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?

Uses saved validation rules, expected ranges, and source tie-outs before drawing conclusions.

Validation
4

Caveats visible?

Known exceptions, lags, and schema quirks are attached from saved context.

Caveats
5

Fix saved for next time?

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

Answer path
✗ 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 saved caveats

iOS lag, schema note, freshness — pulled from saved context.

✅
Answer is ready to share

Less cleanup: source, caveat, and validation path are visible.

🧠
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 estimated productivity values, not telemetry claims — and ChatData works on top of your existing dashboards and sources.

~10h estimated cleanup time saved per data person per month
5× estimated faster delivery on recurring questions once paths are saved
>$1K estimated value of cleanup time avoided per data person per month

Buyer test

Run the same metric question two ways.

Ask once as an unguided prompt and once through ChatData. Look for the visible proof: definition, freshness, caveat, validation, and reusable answer path.

Uses your metric definitionsGuesses from schemaGoverned registry
Checks data freshnessAssumes currentTimestamp verified
Surfaces known caveatsOmits by defaultFrom saved context
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 ChatData support
Start free trial

Trial access

Bring one metric question you want to trust.

Describe the data work you want ChatData to fix first. No card. Trial access opens immediately.

Include the messy context: conflicting metrics, uneven source quality, skeptical stakeholders, or a weekly review that needs proof.

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 is metadata-only by design and blocks obvious secrets and raw-row patterns before writes are accepted.

No card. Trial access opens immediately.