we build automations, test it, and hand it over running
AI Automations Sprint
3–5 production-ready AI workflows built for your team in 4–6 weeks. Each one saves hours per week, runs reliably, and comes with documentation your team can maintain independently.
Who This Is For
Teams ready to move from automation opportunities to production-ready workflows — fast.
Opportunities identified, ready to build
You’ve identified automation opportunities — through our Audit or your own assessment — and want them built properly.
No in-house AI engineering
Your team doesn’t have the AI engineering skills to build these workflows in-house.
Need working automations, not prototypes
You want automations tested with real data, documented, and handed over with training — not proofs of concept.
Results in weeks, not quarters
You need results fast — not a 6-month project that competes with product priorities.
Scope of Work
3–5 workflows · 4–6 weeks · 2 revision cycles per workflow · 2-week post-launch support
Design & Architecture
- Select 3–5 workflows and design automation architecture: AI agents, integrations, human-in-the-loop checkpoints
- Define success metrics per workflow — time saved, error reduction, output quality
- Set up API connections, tool access, and sandbox environments
Build & Iterate
- Build each workflow end-to-end with weekly demos to your team
- Test with real data and real team members — 2 revision cycles per workflow
- Document each workflow as we build: what it does, how to monitor, how to modify
Handover & Training
- 1-hour training session per workflow with the team members who will use it
- Runbook per automation: monitoring, troubleshooting, and modification guide
- 2-week support window post-handover for questions and adjustments
Example Workflows We Build
- Automated weekly reporting — pull analytics data → generate narrative → post to Slack or email
- Pipeline monitoring agent — check CRM daily → flag stale deals → draft follow-ups
- Experiment QA — validate setup against best practices → flag issues before launch
- Client onboarding prep — new client signed → pull data → generate briefing doc → create project structure
Roadmap
Three clear phases — from architecture to handover.
Design & Architecture
- Kickoff: select workflows, define success metrics
- Design automation architecture per workflow
- Set up API connections, tool access, and sandbox environments
Build & Iterate
- Build each workflow end-to-end with weekly demos
- Test with real data — 2 revision cycles per workflow
- Document as we build
Handover & Training
- 1-hour training session per workflow
- Runbooks handed over
- 2-week post-launch support begins
- 3–5 working automations saving 20+ hours/week
Frequently Asked Questions
They can — but it’ll take 3–6 months of their time away from product work. We deliver in 4–6 weeks because this is all we do, and we build on proven patterns from our own internal operations.
Every workflow comes with a runbook and documentation. We also include 2 weeks of post-launch support. For ongoing maintenance, our Managed AI Ops retainer covers that.
No — if you haven’t done the Audit, we run a condensed discovery in Week 1 to identify and prioritise the right workflows. That said, Audit clients are pre-scoped and move faster.
We cap at 5 workflows per Sprint. Better to deliver 3–5 excellent, well-documented workflows than rush through more. Additional workflows can be scoped in a follow-on Sprint or Managed AI Ops retainer.