Production & Scale
Turn successful PoVs into a repeatable way of building, shipping, and scaling AI across your business.
AI isn't a single project. It's a sequence of working prototypes, production systems, and organisational changes that have to line up.
We turn a few successful PoVs into an internal AI capability — architecture, governance, and teams that can ship new agents quarter after quarter.
Your infrastructure. Your data. Yours to own.
What It Is
A repeatable system for shipping agents across departments — not isolated pilots.
Anchored in working software, not slideware. We use existing PoV builds as starting points, then design the processes, tooling, and governance to scale them.
This isn't "AI strategy." It's the engineering and organisational work required to go from "we have a few things working" to "we ship AI capabilities routinely."
What You Get
Everything documented. Yours to keep. Built for your team to own and extend.
AI Portfolio & Value Map
Prioritised view of where agents and orchestration layers create measurable impact
Target Architecture
Reference architectures for agentic workflows, data pipelines, integrations — cloud, on-prem, or hybrid
Operating Model
Roles, responsibilities, ways of working between business, data, and engineering
Governance Blueprint
Guardrails for model choice, data access, logging, monitoring, approvals
Skills & Change Plan
Upskilling plan for engineers, analysts, domain experts to build and operate AI systems
12–24 Month Rollout Plan
Sequenced plan connecting PoV builds, production deployments, and platform work
Production Agents
We don't just plan — we build the first wave of production agents alongside your team
Production Agents
We don't just plan — we build the first wave of production agents alongside your team
How It Works
Total engagement typically runs 6–12 months. But you're shipping production agents within the first 3 months — not waiting for a "transformation" to complete.
Discover
Map current AI initiatives, data landscape, tech stack. Agree on baselines and constraints.
- Initiative mapping
- Data landscape
- Constraint alignment
Design
AI architecture for your environment — orchestration layers, reusable components, pipelines. Define team ownership.
- Target architecture
- Component design
- Team ownership
Build
Select high-impact use cases. Build production-grade agents with monitoring, observability, security baked in.
- Production agents
- Monitoring setup
- Security integration
Scale
Roll out to additional teams, regions, product lines. Refine governance. Establish backlog of new workflows.
- Multi-team rollout
- Governance refinement
- Workflow backlog
Common Failure Modes We Prevent
We've seen these patterns across dozens of AI initiatives. Here's how we prevent them.
We built 10 pilots, none went to production
Every build designed for production from day one
Each team built their own thing, nothing connects
Shared architecture, reusable components, clear standards
We can't hire fast enough to scale
Upskilling your existing team, not replacing them
Legal/compliance blocked everything
Governance designed in, not bolted on after
We don't know what's running or how it's performing
Observability and monitoring from the start
The consultants left and we can't maintain it
Your team builds alongside us; full handover with docs and training
Pilot Mode vs Scale Mode
The difference between "we have some AI projects" and "we ship AI routinely."
Each project starts from zero
Shared components, reusable patterns
Hero engineers carry the work
Teams with clear ownership
Governance added after the fact
Governance built in from the start
Success measured by "did it work?"
Success measured by business KPIs
Handover is a problem
Handover is designed in
No one knows what's running
Observability across all agents
What We Don't Do
From kickoff to production system. Not a demo—a working system on your infrastructure.