Data Pipelines
& Reporting.

Raw data from your tools, databases, and APIs — collected, transformed, and delivered as clean reports to your inbox or Slack on a schedule you define.

Typical build time 4–8 weeks
Agent count 4–6 agents
Data freshness Real-time → daily
Sources supported Any API or DB

Five agents.
One dashboard.

Each agent owns one stage of the pipeline. Data flows from source to delivery without a human touching a spreadsheet.

Agent 01
Collector
Ingest · Scheduler
+

Pulls data from your sources on a schedule — APIs, databases, spreadsheets, webhooks. Handles pagination, auth refresh, and retry on failure.

Tools
REST / GraphQL Postgres Google Sheets Cron scheduler
Agent 02
Transformer
Clean · Normalize · Join
+

Cleans raw data, normalises formats, joins datasets across sources, and applies your business logic — currency conversion, date normalisation, deduplication.

Tools
Claude (reasoning) SQL transforms JSON schema Dedup logic
Agent 03
Analyst
Metrics · Anomaly · Narrative
+

Computes KPIs, compares against targets and prior periods, flags anomalies, and writes the narrative summary — so the report explains itself.

Tools
Claude Metric engine Anomaly rules Period compare
Agent 04
Composer
Format · Render
+

Builds the final report — charts, tables, and narrative — in the format you need: Slack message, HTML email, Google Slides, or PDF.

Tools
Chart.js Google Slides API HTML email PDF renderer
Agent 05
Dispatcher
Deliver · Archive
+

Sends the report to the right people at the right time, archives a copy, and logs the delivery — with read-receipt tracking if you need it.

Tools
Slack API Gmail / SMTP Drive API Supabase log
Agent 00
Orchestrator
Supervisor · State machine
+

Triggers runs on schedule or on-demand, coordinates every handoff, retries failed stages, and keeps a full audit log of every pipeline run.

Tools
State machine Postgres log Retry policy

Monday 08:00.
The weekly report
writes itself.

Every Monday at 08:00, the pipeline wakes up. By 08:01:12 your team has a Slack message with last week's revenue, churn, and the three numbers that moved — and why.

live trace · weekly-revenue-report · Mon 08:00:00 running
Orchestrator
Supervisor · State machine
coordinating pipeline
Collector
Ingest
idle
Transformer
Clean · Join
idle
Analyst
Metrics · Narrative
idle
Composer
Format · Render
idle
Dispatcher
Deliver
idle

What we need from you.

Most pipelines are live in 4–8 weeks. The clearer your data sources and KPI definitions, the faster we move. We sign an NDA before anything changes hands.

🔌
Data source access
Read credentials for every source — database, API, or SaaS tool. We document what we touch and need nothing with write access unless the pipeline outputs there.
📐
KPI definitions
Your exact formulas: what counts as revenue, how you define churn, which date fields to use. If it's in a spreadsheet today, that's fine — we'll formalise it.
📅
A report sample
The last version of the report you're trying to automate, even if it's a rough Notion doc or a Google Sheet. We reverse-engineer the logic from what you already have.
👤
A point person
One person who can say "that number looks wrong" during QA. ~1 hour a week for 3 weeks — most of the time we just need a sanity check on edge cases.

From kick-off to live.

01
Discovery
Days 1–7
Audit your data sources, define KPIs, agree the schedule and delivery format.
02
Build
Days 8–28
Pipeline wired end-to-end on your real data. Numbers verified against your current manual process.
03
Test & tune
Days 29–42
Run in parallel with your existing report for two weeks. Fix any discrepancies until numbers match.
04
Go live
Days 43–56+
Cut over. We monitor the first two delivery cycles. Retainer available for new metrics or source changes.

Still doing this by hand?

Book a free 30-minute scoping call. Bring your current report and your data sources — we'll tell you exactly what's automatable and what it would take.

More use cases → Book a free call →