Practice area ยท LLMs and agentic systems

Language models that do real work

We design, build and run large language model systems and AI agents for European organisations, from a governed assistant over your documents to multi-step agents that act inside your systems with a human in the loop.

Retrieval-augmented generationAI agentsFine-tuningDocument intelligenceEvaluation and guardrailsPrivate and on-premise

What we deliver

The full LLM stack, built for production

We are model-agnostic. We choose between commercial APIs and open-weight models on accuracy, cost, latency and where your data is allowed to go.

01

Knowledge assistants (RAG)

Assistants that answer from your documents, policies and databases with citations, access controls that mirror your permissions, and freshness pipelines that keep the index current.

02

Agentic workflows

Agents that plan, call tools and APIs, and complete multi-step tasks such as triage, reconciliation, research and case handling, with approval checkpoints wherever a person should decide.

03

Fine-tuning and model adaptation

Supervised fine-tuning, parameter-efficient adaptation and distillation of open-weight models to your domain, tone and output formats, when prompting alone is not enough.

04

Document intelligence

Extraction, classification and summarisation across contracts, invoices, forms and reports, including scanned and multilingual documents, feeding structured data into your systems.

05

Conversational interfaces

Customer-facing and internal assistants on web, chat and voice channels, multilingual by default, with clear AI disclosure and a hand-off to a person.

06

Systems integration

LLM capabilities wired into your CRM, ERP, ticketing and data platforms through secure APIs, so the output lands where your teams already work.

07

Evaluation and guardrails

Task-specific test suites, red-teaming, grounding and hallucination checks, prompt-injection defences and content policies, measured before launch and after every change.

08

LLMOps

Monitoring of quality, cost and latency; model and prompt versioning; usage analytics; and a managed path for upgrading models without regressions.

Deployment

Your data, your jurisdiction

Where the model runs is a design decision, not an afterthought. We deploy on infrastructure you control, to match your data-protection, sovereignty and cost requirements.

Private and on-premise

Open-weight models running in your own cloud tenancy or data centre, for regulated data and air-gapped environments.

Hybrid routing

Sensitive requests stay on your private models while non-sensitive tasks can use commercial model APIs, under one policy layer.

Evidence

Results, with the names left out

Our clients are protected by strict non-disclosure agreements, so we never name them. Each result is described by sector and scale only, and measured against the baseline agreed at the start.

[CLIENT-SUPPLIED] Sector and size, e.g. mid-sized EU insurer

[CLIENT-SUPPLIED] What was built, in general terms

[CLIENT-SUPPLIED] The starting problem and what changed, in two sentences, with no names, places or details that could identify the client.

Result

[CLIENT-SUPPLIED]

[CLIENT-SUPPLIED] Sector and size, e.g. mid-sized EU insurer

[CLIENT-SUPPLIED] What was built, in general terms

[CLIENT-SUPPLIED] The starting problem and what changed, in two sentences, with no names, places or details that could identify the client.

Result

[CLIENT-SUPPLIED]

[CLIENT-SUPPLIED] Sector and size, e.g. mid-sized EU insurer

[CLIENT-SUPPLIED] What was built, in general terms

[CLIENT-SUPPLIED] The starting problem and what changed, in two sentences, with no names, places or details that could identify the client.

Result

[CLIENT-SUPPLIED]

How we engage

Discovery, pilot, scale

Every engagement starts small and earns the right to grow. Each phase ends with a decision point and something you own.

1Weeks

Discovery

We map candidate use cases against value, feasibility and risk, audit the data they depend on, and define how success will be measured.

You receive

  • A ranked use-case shortlist
  • A data and risk assessment, including AI Act classification
  • A pilot scope with target metrics
2Weeks to months

Pilot

We build the chosen use case end to end on real data with a group of real users, and measure it against the metrics agreed in discovery.

You receive

  • A working system in a controlled environment
  • An evaluation report against target metrics
  • A production architecture and cost model
3Ongoing

Scale

We harden the pilot for production, roll it out, and run it, or hand it to your team with the documentation and training to run it themselves.

You receive

  • Production deployment with monitoring
  • Runbooks, documentation and training
  • A roadmap for the next use cases

Have a use case in mind?

Tell us the process, the documents or the decision you want to improve. We will tell you honestly whether an LLM is the right tool.