Integrate AI workflows people can rely on
We integrate retrieval, orchestration and human review into the product you already run — with clear evaluation, operational controls and a path to production.
Decide if this is the right next step
Good fit
A real workflow is already waiting
- A product, team or operational process already exists.
- Knowledge is fragmented, answers are unreliable, or review is manual.
- You have a product owner who can define a useful decision and acceptance criteria.
Bring to the brief
The context that changes the architecture
- The user workflow and the decision AI should support.
- Available knowledge sources, systems and data boundaries.
- Sensitivity, deployment constraints and the outcome you need to measure.
Receive
A build path you can operate
- An integration design with evaluation and human-review points.
- A delivery plan that separates useful automation from unsafe shortcuts.
- A production handover path: monitoring, ownership and next iterations.
When this is the right engagement
Founders & CEOs
Building an AI-powered SaaS that needs to be production-ready, not a demo. You need a team that owns the full thing.
Heads of Product
Turning an AI concept into a real product. Discovery, architecture, sprint delivery, and clear handover documentation.
CTOs & Tech Leads
Augmenting your team for a focused AI build. Senior engineers, no onboarding overhead, direct communication.
Need a dependable AI workflow, not another chatbot?
Share the workflow, the knowledge sources and the decisions the product must support. We will outline a realistic integration path.
Discuss an integration →What an integration can include
- Multi-agent system architecture & implementation
- RAG pipeline design, vector store setup, knowledge base integration
- LLM selection, fine-tuning guidance, private hosting options
- Human-in-the-loop workflows and confidence scoring
- Audit trail and full decision traceability layer
- Cost monitoring and token optimisation against an agreed baseline
- Production deployment (Kubernetes / cloud of choice)
- Post-launch partnership focused on product evolution
How we make the workflow dependable
We begin with the user decision and evidence source, then design, test and operate the workflow around them.
Problem framing
Define the exact agent tasks, success metrics, and failure modes before writing a line of code.
Architecture design
Design the agent graph, retrieval strategy, data flows, and human oversight touchpoints.
Iterative build
Sprint-by-sprint delivery with weekly agent demos against real data.
Hardening
Prompt-injection testing, guardrail implementation, cost optimisation and technical-control review.
Production launch
Deployment, monitoring dashboards, runbook, and long-term product ownership.
Shipped in production
AI Legal Multi-Agents
Multi-agent orchestration for contract analysis and legal document processing. Each agent specializes — extraction, risk analysis, summarization — and runs in parallel. Every output carries a confidence score and source citation for full auditability.
Read full case study →Technology we master
The AI and infrastructure layer we select, integrate, and operate in production — chosen for production reliability, not demos.
LLM Providers
Orchestration
Observability
Vector / RAG
Infrastructure
Controls
What we won't do
We stay focused so we can be excellent at what we do. These are the engagements we decline:
- Proof-of-concept with no committed path to production
- Data science research without a clear product outcome
- Projects where important data, review and accountability questions are deferred until late delivery
Choose the path that matches the problem
Regulated document workflow
Build a Legal AI product
For document workflows that need source grounding, human review and traceability by design.
Explore Legal AI product delivery →Existing prototype
Review an AI-built app before launch
For a Lovable, Cursor, Bolt or similar app that now needs production engineering.
Explore the production-readiness review →Make AI useful inside the workflow people already trust
Tell us which knowledge, decisions and systems need to work together. We will help you define the right RAG or AI-workflow integration.