Capabilities

What we build.

Five capabilities that cover the distance from an unproven idea to a system your team can run without us.

LLM Application Development AI Agent Systems RAG & Knowledge Engineering Model Adaptation & Deployment AI Strategy & Technical Review

01 Products

LLM Application Development

Product surfaces built on frontier language models.

Assistants, copilots, drafting and extraction tools, and language-model features inside existing products. We handle prompt architecture, structured output, streaming interfaces, cost and latency budgets, and the fallback behaviour for when a model returns something unusable.

What you get

Working application with source and documentation
Prompt and output-schema architecture
Evaluation set and scoring harness
Cost and latency instrumentation

02 Autonomy

AI Agent Systems

Tool-using systems that plan, act and stay inside their limits.

Agents that call your APIs, query your systems and carry multi-step tasks — with the parts that matter in production: permission boundaries, human-in-the-loop checkpoints, retry and recovery paths, step-level tracing, and an evaluation harness that catches regressions before your users do.

What you get

Agent architecture with tool definitions
Permission model and approval checkpoints
Step-level tracing and audit trail
Regression suite over agent trajectories

03 Knowledge

RAG & Knowledge Engineering

Retrieval that holds up against messy, real documents.

Ingestion pipelines, document parsing, chunking strategy, hybrid semantic and keyword retrieval, reranking, and grounded answers with citations. Retrieval quality is measured explicitly rather than assumed — most disappointing RAG systems are retrieval failures wearing a model costume.

What you get

Ingestion and parsing pipeline
Hybrid retrieval with reranking
Retrieval quality benchmark
Citation and grounding layer

04 Models

Model Adaptation & Deployment

Fine-tuning, serving and the operations around them.

Dataset construction, supervised fine-tuning and preference tuning, distillation to smaller models, quantisation, and deployment on your infrastructure or a managed platform — with autoscaling, monitoring, and a rollback path that has actually been tested.

What you get

Training dataset and tuning pipeline
Benchmarked model against baseline
Deployment with autoscaling
Monitoring, alerting and rollback plan

05 Advisory

AI Strategy & Technical Review

A second opinion, written down, with no product to sell you.

Feasibility studies, build-versus-buy analysis, architecture review of an existing AI system, vendor and model selection, and cost modelling. Delivered as a written report your team and your board can both read, with recommendations ranked by expected value and risk.

What you get

Written technical assessment
Prioritised recommendations
Cost and risk model
Reference architecture

The stack we build on

Engagement models

Three ways to work with us.

Scoped build

A defined system, fixed scope, delivered and handed over with documentation and a maintenance window.

Embedded team

Our engineers working inside your team for a set period, under your planning process and code review.

Technical review

A time-boxed audit of an existing AI system, ending in a written report and a remediation plan.

Get in touch

Tell us what you are trying to build.

One email is enough to start. Describe the problem in your own words — we will come back with a technical read on whether it is worth building and how we would approach it.

Email us View capabilities

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