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 time4–8 weeks
Agent count4–6 agents
Data freshnessReal-time → daily
Sources supportedAny 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 / GraphQLPostgresGoogle SheetsCron 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 transformsJSON schemaDedup 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
ClaudeMetric engineAnomaly rulesPeriod 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.jsGoogle Slides APIHTML emailPDF 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 APIGmail / SMTPDrive APISupabase 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 machinePostgres logRetry 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 · live trace · weekly-revenue-report · Mon 08:00:00processing
Orchestrator
Supervisor · State machine
coordinating handoffs
Collector
Ingest
idle
Transformer
Clean · Join
idle
Analyst
Metrics
idle
Composer
Format
idle
Dispatcher
Deliver
idle
Orchestrator
Supervisor · State machine
coordinating handoffs
agents
Collector
Ingest
idle
Transformer
Clean · Join
idle
Analyst
Metrics
idle
Composer
Format
idle
Dispatcher
Deliver
idle

What we need from you.

🔌
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.