Structured AI adoption
AI Consultancy
From use-case discovery to production deployment — with evaluation frameworks, guardrails, and governance your compliance team can actually approve. We move organisations from "AI pilot" to "AI in production" without the chaos.
At a glance
6 wks
Discovery engagement
22+
Use cases scored per project
60%
Avg research time reduction
RAG
Core architecture expertise
Governance frameworks your compliance team can adopt
ROI modelling before any build begins
Works with OpenAI, Anthropic, Azure OpenAI, open-source LLMs
What we do
The full AI adoption lifecycle — not just the exciting parts.
Most AI projects fail in production because discovery, governance, and evaluation were skipped. We cover all of it.
01
Use Case Discovery
Structured workshops to surface, score, and prioritise AI opportunities by ROI potential, data readiness, and technical feasibility. Output: a prioritised backlog with business cases.
02
Data Readiness Assessment
Audit of data quality, completeness, labelling gaps, and access controls. Identifies what data preparation is needed before any model work begins.
03
RAG Pipeline Design & Build
End-to-end retrieval-augmented generation systems — chunking strategy, embedding selection, vector database setup, retrieval tuning, and prompt engineering.
04
Evaluation Frameworks
Custom evaluation harnesses — faithfulness, relevance, groundedness, and task-specific metrics. Regression test suites so you can deploy with confidence and catch regressions early.
05
Guardrails & Safety
Output filtering, hallucination detection, PII redaction, topic-based refusal, and human-in-the-loop escalation patterns for regulated industries.
06
Governance & Enablement
AI governance playbooks, model monitoring dashboards, acceptable use policies, and team enablement workshops so your organisation can operate AI responsibly at scale.
Technology
Model-agnostic. Stack-agnostic.
We don't push a specific vendor. We recommend the right model and infrastructure for your data sensitivity, latency requirements, and budget.
OpenAI / GPT-4o
Anthropic Claude
Azure OpenAI
Open-source LLMs (Llama, Mistral)
Pinecone / Weaviate / pgvector
LangChain / LlamaIndex
RAGAS / custom evals
Guardrails AI
MLflow / Weights & Biases
AWS Bedrock / SageMaker
Our process
From pilot to production — without getting stuck.
Most AI initiatives stall after the first demo. Our process is designed to move fast through discovery and deliver production systems that keep working.
1
Discovery & Prioritisation 2–4 weeks
Stakeholder interviews, data audit, use case workshop, and ROI scoring. Output: prioritised AI backlog with feasibility assessment and a recommended starting point.
2
Architecture & Proof of Concept
Design the system architecture — model selection, retrieval strategy, data pipeline, and guardrail approach. Build a working PoC to validate assumptions before committing to full build.
3
Build & Evaluate
Iterative development with continuous evaluation. Every sprint includes regression tests against your eval harness so quality doesn't regress as you add features.
4
Production Deployment
Hardening, load testing, cost optimisation, monitoring dashboards, and human-in-the-loop escalation paths. We deploy to your environment and document everything.
5
Governance & Ongoing Monitoring
Drift detection, model performance tracking, policy reviews, and team enablement so your organisation stays in control of its AI systems long-term.
FAQ
Common questions
Yes — that's exactly what the discovery engagement is for. Many of our best projects start with "we think AI could help but don't know where to start." We surface concrete use cases, assess your data, and give you an honest view of what's feasible and what isn't.
Not necessarily. For sensitive data, we can architect solutions using Azure OpenAI (which has stronger data residency commitments), on-premises open-source LLMs, or private inference endpoints. Data handling is a first-class design constraint, not an afterthought.
Retrieval-Augmented Generation (RAG) lets an LLM answer questions based on your own documents and data — not just its training data. If your use case involves answering questions about internal policies, product documentation, or proprietary content, RAG is usually the right approach. It significantly reduces hallucinations and lets you audit what the model retrieved.
We use a combination of retrieval grounding (RAG), output evaluation (faithfulness metrics), output filtering (guardrail layers), and human-in-the-loop escalation for high-stakes decisions. We also build regression test suites so you can detect when hallucination rates change after model updates.
Yes. We deliver a governance playbook covering acceptable use, data handling, human oversight requirements, audit trail design, and incident response for AI systems. This is typically co-developed with your legal and compliance teams during the governance phase.
Ready to explore AI for your organisation?
Start with a discovery conversation. We'll tell you honestly what's feasible, what the risks are, and what a realistic first step looks like.