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Services

What we do, in plain terms

Four service lines, usually combined inside one project: models fitted and evaluated for the task, agents that carry out the workflow, pipelines that keep the data current, and advisory so the client’s team can own the result.

01

Applied AI Models

Selecting, adapting and evaluating foundation models for a specific business task — and getting them into production.

  • Model selection and benchmarking against the client’s own data and success criteria
  • Prompt design, retrieval (RAG) and fine-tuning where it measurably pays off
  • Evaluation suites that score accuracy, cost and latency before anything ships
  • Deployment on Amazon Bedrock or the client’s preferred platform

02

AI Agents & Automation

Agent systems that carry out multi-step business workflows — with tool access, guardrails, and a person approving what matters.

  • Agent workflows that read, decide and act across the client’s business systems
  • Tool integration through open protocols such as the Model Context Protocol (MCP)
  • Human-in-the-loop checkpoints for consequential actions
  • Monitoring, logging and cost controls from day one

03

Data & Research Pipelines

Continuous pipelines that turn documents, filings and web sources into structured, reviewable output.

  • Ingestion and extraction from documents, filings and web sources in English and Chinese
  • Monitoring and summarisation that flags what changed and why it matters
  • Structured outputs delivered into the client’s data warehouse or workspace
  • Citations preserved end to end so every result can be checked

04

AI Advisory & Enablement

Practical help deciding what to build, what to buy and what to leave alone.

  • Use-case discovery, prioritised by business value and feasibility
  • Architecture, model and cloud-cost planning
  • Governance: data handling, access control and review processes
  • Hands-on enablement so the client’s team can run what we build

How we engage

Work is scoped under a written engagement agreement that sets out the business problem, the deliverables, the evaluation criteria and the fee. Systems are deployed in the client’s own cloud account, and the client owns what we build for them.

Principles

Principles before volume

The same four commitments sit behind every service line above. They are operating requirements, not slogans.

01

Evaluate before deploy

Every model and agent is measured against the client’s own data before it touches production.

02

Human in the loop

Consequential actions wait for a person. Automation speeds up the work; it does not remove accountability.

03

Your data stays yours

Systems run in the client’s cloud tenancy. We do not train on client data.

04

Cost is a design constraint

Token spend, latency and infrastructure cost are tracked from the first prototype.

A business problem that AI might solve?

Tell us what is slow, manual or unanswered today. We will say whether it is a fit and what it would take.

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