AI readiness & strategy
01
- Value-case identification
- AI readiness assessments
- Operating model design
- Build-buy-partner calls
- AI investment thesis
Data, AI & Automation · Practice III
AI ships when the value case and the data foundation are honest.
Most AI initiatives fail at the joints — between the model, the data, and the operating reality. We close those joints, and we keep them closed.
ScrollWhat we do, exactly
Act I · The thesis
Models are commodity now.
The bottleneck moved.
It moved to data foundations,
to value cases that survive a CFO,
to adoption that survives a Tuesday.
We work the joints, not the headlines.
Act II · The lens
We hold every AI initiative to four honest tests.
- 01
Is the value case real, or is it a ribbon?
Most "AI use cases" are demos in disguise. We pressure-test the unit economics, the data dependency, and the change cost — and we kill the cases that will not survive a finance review.
- 02
Will the data foundation actually carry the model?
Models are easy. Pipelines, lineage, freshness, and governance are not. We audit the foundation before we promise the headline — and we rebuild it before it bottlenecks the rollout.
- 03
Can the model be operated, not just shipped?
We design the eval harness, the guardrails, the drift telemetry, and the incident shape on day one. An AI system that cannot be operated is a press release with a bug.
- 04
Will the frontline actually use it?
Adoption is the model output that matters most. We design the workflow, the disagreement path, and the confidence narrative so the new system is the path of least resistance — not a parallel one.
Act III · The matrix
The full AI delivery surface, from value case to live operation.
Applied AI delivery
02
- Agentic systems & copilots
- RAG & knowledge graphs
- Vision & multimodal
- Forecasting & decision models
- Eval, guardrails & safety
Data foundations
03
- Lakehouse architecture
- Streaming & CDC pipelines
- Feature & model platforms
- Data contracts & lineage
- Master data & identity
Automation & workflow
04
- Process intelligence
- Workflow orchestration
- RPA → agentic transition
- Frontline operating consoles
- Human-in-the-loop design
Governance & risk
05
- Data & AI governance
- Model risk & controls
- Privacy by design
- Regulatory alignment
- Audit-ready paper trails
Operate & evolve
06
- Drift & quality telemetry
- On-call & incident shape
- Re-training pipelines
- Adoption metrics
- Continuous value reviews
Act IV · How clients buy this
Three ways to engage, all of them surviving the CFO.
Shape 01
The Value-Case Read
4–6 weeks · fixed
We assess your AI ambition against operating reality. You leave with a prioritised, costed, finance-defensible list of AI bets — and the ones we recommend you stop funding.
Shape 02
The Lighthouse
12–16 weeks · milestone-priced
We pick the highest-leverage value case and ship it end-to-end — model, data, surface, governance, telemetry — into live operation. One programme. One number to defend.
Shape 03
The AI Operating Partnership
12 months+ · retained
We hold the AI portfolio alongside the in-house team — value case management, governance, evals, and re-prioritisation. The function gets sharper every quarter.
Act V · The proof
AI initiatives that shipped — and stayed shipped.
Recent outcomes from value-case-led AI mandates across enterprise, financial services, and regulated public-interest workloads.
01
4.2×
median throughput lift on operations re-platformed onto our agentic and decision systems
02
11 wks
average time-to-first-production for greenfield AI surfaces, end-to-end
03
99.97%
adoption-after-handoff across the AI rollouts we have run in the last 24 months
04
63%
median reduction in human-in-the-loop touches on the workflows we automate
Sign-off