Generative AI, agents and LLMs, built for production.
From RAG and agentic workflows to private LLMs and governance, we build the AI your business can actually run, secure and afford.
Every kind of AI your business is asking for.
Generative AI is a stack, not a single product. These are the capabilities we design, build and run.
Generative AI applications
Text, code, summaries and drafted content built into the products and workflows your people already use, on models chosen for the job.
RAG & enterprise search
Retrieval-augmented generation grounded in your own content: chunking, embeddings, hybrid search and reranking, with answers that cite their sources.
AI agents & agentic workflows
Agents that plan, call tools and complete multi-step work, with bounded permissions, audit trails and a human checkpoint where the stakes call for one.
Copilots & assistants
Domain copilots for support, engineering, finance and operations, wired to internal systems and measured on work completed rather than chats opened.
Private & sovereign LLMs
Open-weight and hosted models run inside your own boundary, on private endpoints or your own GPUs, when data residency or sovereignty rules out a public API.
Fine-tuning & model adaptation
Parameter-efficient fine-tuning, distillation and prompt optimisation, used only where retrieval and prompting have stopped improving the result.
Multimodal & document AI
Vision, speech and document understanding: extraction from scans and forms, call summarisation, and multimodal search across files and media.
MLOps & LLMOps
Model registry, prompt versioning, CI/CD for AI, tracing, drift detection and cost controls: the operating discipline that keeps a system useful after launch.
Agentic AI that completes work, safely.
An agent is a model with tools, memory and a goal. The hard part is not the loop, it is the boundary around it: what it may touch, who approves what, and how every step is recorded. We build multi-agent systems on LangGraph, LangChain and the Model Context Protocol, with permissions scoped like any other service account.
- Multi-agent orchestration
- Tool use & function calling
- MCP servers
- Long-term memory
- Planning & reflection
- Human approval gates
The AI capabilities on your roadmap, delivered.
From foundation models to guardrails, we have a delivery approach for each. Ask us about any of them.
Models
Retrieval & knowledge
Agents
Quality
Safety & governance
Operations & cost
Why most AI programmes stall before production.
The model is rarely the issue. Data access, security, evaluation, cost and governance decide whether AI scales.
Data access
The prototype works because it can read everything. Apply the access rules that actually exist and a good share of the answers disappear.
Security boundary
Sending regulated data to a public endpoint is a conversation with your CISO that you are going to lose, and should.
Evaluation
Without a golden set, whether a change improved things gets decided by whoever is most senior in the room. That is not a quality process.
Cost
The pilot was priced on pilot traffic. Nobody modelled what happens when the whole company starts using it on a Monday morning.
Governance
Three weeks from now someone will ask why the system gave a particular answer. If you cannot reconstruct it, that is a finding.
Operations
Providers deprecate models, latency moves, quality drifts. These systems need an on-call rota like anything else in production.
From proof of value to production, with clear gates.
We validate the use case early, then build, harden and run it with evaluation and governance at every stage.
Prove or kill it
A two-week evaluation against real data with a defined success metric. Most ideas should die here, cheaply — and we will tell you when yours is one of them.
Build the foundation
Model gateway, private endpoints, retrieval pipeline, secrets and identity. The unglamorous layer that decides whether anything reaches production.
Harden and govern
Evaluation harness, guardrails, prompt and output logging, cost ceilings, red-teaming, and a model risk position your auditor accepts.
Run and improve
Monitoring for drift, latency, spend and quality — with a feedback loop that improves the system instead of just reporting on it.
A reference architecture for production AI.
- GovernanceL4
Model risk · lineage · output logging · retention
- OrchestrationL3
Agents · tools · routing · human checkpoints
- RetrievalL2
Ingestion · embeddings · reranking · permissions
- FoundationL1
Gateway · private endpoints · identity · secrets
Every AI system we deliver is built on the same four layers. Skip one and the system works in a demo and fails in production — usually on the day someone asks who can see what.
- AI readiness & governance
- LLM platforms, retrieval & agents
- Evaluation, observability & cost
Where AI creates measurable value.
We start from the business workflow, not the model, and focus on the patterns that deliver the strongest return.
Knowledge assistants
Retrieval over your own documents, policies and tickets — with permissions enforced at retrieval time, not bolted on afterwards.
Document & claims processing
Extraction, classification and routing across high-volume document flows, with confidence thresholds and human review where it matters.
Customer operations
Triage, summarisation and drafted responses inside existing workflows — measured on deflection and handling time, not novelty.
Engineering acceleration
Coding agents, test generation and migration tooling wired into your pipelines, with review gates that stay mandatory.
Risk, fraud & compliance
Anomaly detection and control monitoring where explainability is a regulatory requirement, not a nice-to-have.
Operational intelligence
Incident summarisation, log triage and capacity forecasting — AI applied to the infrastructure we already run for you.
Security, governance and compliance built in from day one.
Enterprise AI is approved by risk, security and legal teams. We design for all three before the first prompt is written.
Prompt injection & jailbreaks
Untrusted text is treated as untrusted: input filtering, privilege separation between model and tools, and adversarial testing before release.
Data protection
Regulated data is redacted or kept inside your boundary, retention is set per use case, and prompts and outputs are logged for audit.
Hallucination control
Answers are grounded in retrieved sources, checked against them, and declined when the evidence is missing.
Governance & compliance
A model inventory, risk tiering and documented controls, mapped to the frameworks your regulator and auditors already ask about.
Model-neutral, by design.
We are not tied to a vendor. We pick the model and the platform that fit the workload, the data residency requirement and the budget — and we build so you can switch.
Models and platforms we work with
- Anthropic
- OpenAI
- Gemini
- Meta Llama
- Mistral
- Amazon Bedrock
- Azure AI Foundry
- Vertex AI
- Hugging Face
- LangChain
- Ollama
- vLLM
- Kubernetes
Why neutrality matters here more than anywhere else
Model capability and pricing move every few months. A system wired directly to one provider is a rewrite waiting to happen. We put a gateway in front, keep prompts and evaluation portable, and treat the model as a replaceable component — because it is.
Ready to take AI into production?
Talk to our AI team about your use case, your data and your governance requirements.