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How We Work

Prototype to production in weeks, not quarters

A four-phase framework designed for fast, reliable AI delivery — with clear ownership, measurable checkpoints, and no surprises.

01

Discover

Week 1

We start by understanding your business deeply — not by pitching technology. Discovery is a structured working session, not a slide deck.

↳Stakeholder alignment on the single most valuable outcome
↳Workflow mapping and pain point prioritisation
↳System inventory — what you have, what connects, what's missing
↳Define measurable success criteria (KPIs, benchmarks, baselines)
↳ICP and use-case scoping for AI feature prioritisation
↳Identify data sources, access patterns, and compliance constraints
Deliverable:A scoped brief with clear outcome, architecture sketch, and timeline estimate.
02

Prototype

Weeks 2–3

A working system on your real data — not a demo. Prototype sprints are tight, feedback-driven, and designed to surface what matters early.

↳Working proof-of-concept on your actual knowledge base or workflow
↳Daily async progress updates with async feedback loops
↳Iteration sessions with your team to tune behavior and edge cases
↳Initial safety layer and output validation
↳Prompt versioning from the first run — no throwaway work
↳Benchmark run against defined success criteria
Deliverable:A deployed prototype your team can interact with, evaluate, and provide structured feedback on.
03

Deploy

Weeks 4–5

Production-grade engineering. Every deployment includes observability, guardrails, integration tests, and a clear escalation path.

↳Production-hardened codebase with CI/CD pipeline
↳Full observability stack — logging, tracing, and alerting
↳Safety layer: moderation, role constraints, input sanitisation
↳Integration testing across all connected systems
↳Staging environment with load testing
↳Launch runbook with rollback procedure
Deliverable:A live system with monitoring, documented handoff, and on-call support through go-live.
04

Optimise

Ongoing

AI systems degrade without ongoing attention. Optimise is a continuous loop — evaluating outputs, improving prompts, and evolving the system with your business.

↳Weekly conversation log review and failure mode analysis
↳Prompt A/B testing and model evaluation cycles
↳Conversion and funnel analysis for customer-facing AI
↳Capacity planning as usage scales
↳New use case identification from production patterns
↳Quarterly system health review and roadmap planning
Deliverable:Continuous improvement reports, prompt version history, and a live roadmap for the next iteration.

Guiding Principles

How we think about every engagement

Outcomes before technology

We choose the tool after we understand the problem — not before. If AI isn't the right lever, we'll say so.

No throwaway prototypes

Everything built in week 2 is versioned and carries forward into production. We don't start over.

Observability from day one

Every system ships with logging, tracing, and alerting. You always know what the AI is doing.

Safety is not optional

Input sanitisation, output moderation, and role constraints are included in every build — not an afterthought.

Start with a discovery call

30 minutes. No pitch deck. Just a structured conversation about your business and where AI creates the most leverage.

Book a Discovery Call