From signal to production intelligence.
A structured engagement model with clear stages, gates, and shared accountability. Every step is scoped, measurable, and reversible.
Four stages to production
Each stage has defined duration, deliverables, and exit criteria. No stage is skipped.
Discovery
Signal mapping and readiness assessment. We identify where AI delivers the highest-value outcomes.
Pilot
Controlled deployment of one capability against real signals with measurable success criteria.
Scale
Multi-capability system with monitoring, integrations, and operational hardening.
Production
Full system with continuous improvement, drift detection, and performance optimisation.
Discovery — what happens
Workshops, data walkthroughs, and stakeholder interviews to produce an evidence-backed roadmap.
Deliverables
- Signal inventory & data audit
- Readiness scorecard
- Recommended capability stack
- Risk & constraint map
Discovery
2 weeksSignal mapping and readiness assessment. We identify where AI delivers the highest-value outcomes.
- Signal inventory & data audit
- Readiness scorecard
- Recommended capability stack
- Risk & constraint map
Pilot
4–6 weeksControlled deployment of one capability against real signals with measurable success criteria.
- Working pilot system
- Accuracy & latency benchmarks
- User feedback synthesis
- Go/no-go recommendation
Scale
8–12 weeksMulti-capability system with monitoring, integrations, and operational hardening.
- Multi-capability deployment
- Monitoring dashboards
- Integration endpoints
- Runbook & escalation paths
Production
OngoingFull system with continuous improvement, drift detection, and performance optimisation.
- Production SLA & uptime
- Model refresh pipeline
- Drift detection alerts
- Quarterly optimisation reviews
Which situation fits your team?
Every engagement starts where you are — not where a package says you should be. Pick the scenario that matches your current reality.
We're exploring AI for the first time
You have ideas, maybe some rough use cases, but no AI systems yet. You want to know what's viable before committing budget.
Signs this is you
- No existing AI or ML in production
- Multiple ideas — not sure which to prioritise
- Unclear if your data is good enough
- Stakeholders want evidence before approving spend
You leave with
A prioritised opportunity map, readiness score, and architecture sketch for your best-fit use case.
We have a specific use case ready to test
You know what you want to build and have data to work with. You need a working system with real benchmarks, not a demo.
Signs this is you
- Clear hypothesis about what AI should do
- Data is accessible and roughly understood
- Internal stakeholders are aligned on the goal
- Need a go/no-go decision with evidence
You leave with
A production-grade capability deployed against real data, with accuracy benchmarks and a go/no-go recommendation.
We have a working system that needs to grow
A pilot or prototype exists. Now you need monitoring, integrations, multi-capability expansion, and production readiness.
Signs this is you
- A working AI system already in use
- No monitoring or drift detection in place
- Needs to connect with your existing tools
- Ready to commit to production SLAs
You leave with
A hardened, observable, multi-capability AI system integrated with your stack and running under defined SLAs.
No lock-in, ever
Each stage is a separate agreement. If the pilot fails, you stop. If scope changes, we re-scope. No minimum commitments, no exit penalties.
Evidence at every step
Nothing advances without data. Accuracy benchmarks, latency measurements, and stakeholder sign-off are required at every stage gate.
Everything is yours
All source code, model weights, documentation, and architecture diagrams transfer to you on handoff. We retain nothing.
Radical transparency
Weekly demos showing exactly what was built. Metrics shared openly. If we hit a blocker, you hear about it the same day — not at a monthly review.
Velocity you can plan around
Defined milestones at every step — so your stakeholders always know what's coming next.
Kickoff
Environment setup, data access confirmed, team introduced.
First Prototype
Working model against sample data. Initial accuracy baseline established.
Discovery Complete
Full signal inventory, readiness score, and capability recommendation delivered.
Pilot Decision
Live accuracy benchmarks. Stakeholders make a data-backed go/no-go call.
Scale System
Multi-capability deployment with monitoring, integrations, and runbooks.
Production SLA
Full production system under agreed SLA with quarterly reviews.
What every engagement includes
No matter where you start, these are non-negotiable standards across every engagement.
Dedicated AI Architect
A senior engineer owns your engagement from day one — not rotated mid-project.
Weekly Sprint Demos
Live demo every Friday showing exactly what was built and what it measured.
Async Channel
Direct line to the team. No ticket queues, no support portals, no 48h delays.
Full Documentation
Architecture diagrams, runbooks, API specs, and model cards delivered with every stage.
Security Review
Every system passes a data governance and security posture checklist before handoff.
Accuracy Benchmarks
Quantified performance metrics at every milestone — no vague "it works" claims.
Iteration Cycles
Weekly feedback loops baked in. We adjust course based on what the data shows.
Deployment Support
We stay through go-live — not just until code is merged.
You always know what's happening
No status surprises, no chasing updates. A structured cadence keeps every stakeholder informed at the right level.
Daily
- Async engineering updates in Slack
- Blocker escalation (same-day response)
- CI/CD build status shared
Weekly
- Live sprint demo (45 min)
- Accuracy & velocity metrics shared
- Next-sprint planning brief
Bi-weekly
- Architecture deep-dive call
- Risk & dependency review
- Stakeholder alignment check
Monthly
- Business value review with metrics
- Roadmap adjustment if needed
- Cost & resource optimisation pass
No stage advances without evidence
Hard gates between stages ensure quality and alignment. Every transition is earned, not assumed.
What happens when things go off-track
We plan for setbacks. Every engagement has a defined response for common blockers — known before they happen.
If: Data is messy or incomplete
Our response
Discovery surfaces data gaps before any model work begins. We scope around what you have, not what you wish you had.
If: Pilot accuracy falls short
Our response
Every pilot has a go/no-go gate. If the signal is weak, we say so honestly — with a root-cause report and alternative recommendations.
If: Stakeholders change mid-project
Our response
We document decisions as we go. New stakeholders get a structured briefing. No tribal knowledge, no catch-up tax.
If: Budget or scope changes
Our response
Each stage is an independent contract with clear deliverables. You can pause, re-scope, or stop between stages with no penalty.
Collaboration model
Clear ownership. Shared outcomes. Your domain expertise meets our intelligence engineering.
Your team brings
- Domain expertise & business context
- Data access & governance approvals
- Stakeholder coordination
- Acceptance criteria & success definition
Our team brings
- AI architecture & engineering
- Model selection, training & tuning
- System integration & DevOps
- Performance optimisation & monitoring
Built together
- Production AI system
- Observability & drift alerts
- Iteration & feedback loops
- Continuous improvement roadmap
Questions before starting
Honest answers to what teams ask before their first engagement.
Start with a controlled pilot.
2 weeks to first signal. 6 weeks to working intelligence. Every stage reversible.