Generative AI,
shipped as software.
RAG, agents, fine-tuning, structured extraction, evaluations and guardrails, engineered with cost, latency and safety envelopes designed in from day one.
The interesting question is never 'can we use an LLM here', it is 'does using one change the customer outcome?' The programmes that ship value are the ones that answer that honestly, and design against it.
Frame
Use case sharpened, evaluation sketched, model and pattern (RAG, fine-tune, agent) sized against real data, before anything is built to last.
Working sessions to define what 'good' looks like, audit the data and constraints, and sketch an eval set. Output is a phased plan grounded in evidence.
Sometimes framing kills a use case. That's the highest-return work we could have done.
Add an LLM to a stable rule-based step, and you usually make it slower, more expensive and less reliable.
Prototype
A rigorous prototype against real data and a real eval, not a demo. We size cost, quality and pattern before we productionise.
Prompt engineering as a versioned discipline. Retrieval tuned to your domain. Agents where planning and tool use genuinely help, never as the default.
Evals run in CI and gate deploys. Nothing user-facing ships without them.
Productionise
The full stack, retrieval, orchestration, guardrails, observability, human-in-the-loop, integrated with your platforms and pipelines.
Layered guardrails proportional to blast radius. Cost and latency instrumented per surface. Serving choices tuned against real numbers.
Documentation, model cards and compliance evidence produced alongside delivery, aligned to EU AI Act, ISO/IEC 42001 and NIST AI RMF.
Operate
Drift, cost, latency and quality monitored as ongoing engineering disciplines. Prompts and models are versioned; regressions are caught in CI.
Online evaluation from real users, thumbs, edits, escalations, feeds the eval set. The eval set keeps up with reality rather than freezing on day one.
Knowledge transfer is designed in. The systems your team runs after we scope down are the ones your team helped build.
Toolkit
Framework, platform and vendor selection is driven by the shape of the work and the team who'll own it, not by whichever ecosystem is loudest this year. The list below is representative, not prescriptive.
- Models
- Anthropic · OpenAI · Hugging Face · vLLM
- Frameworks
- LangChain · LangGraph · LlamaIndex
- Retrieval
- Pinecone · Weaviate · pgvector · Elasticsearch
- Evaluation
- LangSmith · Langfuse · Custom eval harnesses
- Serving
- Bedrock · Azure OpenAI · Vertex AI · Kubernetes
Selected work
What clients ask us first
01RAG or fine-tuning, how do we decide?
The two solve different problems and are often combined. RAG when knowledge changes, needs citation, or lives behind an access boundary. Fine-tuning when the task itself needs to change, tone, structure, reasoning style, latency profile.
02How do you evaluate open-ended outputs?
Task-specific eval sets from your own data, programmatic checks, LLM-as-judge calibrated against human graders, adversarial red-team sets. Evals in CI, gating deploys. Online evaluation feeds the offline set.
03How do you handle hallucination, prompt injection and safety?
Layered defence proportional to blast radius. Input and output guardrails, retrieval grounding, structured output, tool scoping, red-teaming. Nothing goes to production without a threat model.
04How do you manage cost and latency?
As real engineering budgets. Cost per outcome and p95 latency envelope per surface. Routing between providers, caching, batching, smaller distilled models where quality allows, self-hosted where volume earns it.
Let's discuss your generative-AI programme.
Share a brief overview of the use case, we'll arrange an introductory conversation with the relevant practice leadership.