Technical delivery
Custom AI & LLM engineering
Design and build tailored RAG, intelligent search, model integrations, AI applications and production infrastructure for technical teams with complex requirements.
A custom engineering service scoped around your architecture, data, risk and operational requirements.
Where value gets lost
Operational problems this can address
Technology teams that need a tailored, production-minded AI system rather than an off-the-shelf product.
- Knowledge is difficult to retrieve reliably
- AI prototypes lack evaluation and production controls
- Models and APIs need secure integration
- Performance, cost and observability need engineering ownership
Potential scope
What a tailored solution could do
The exact scope depends on your process, data, controls and existing systems. These are capability examples, not a packaged product.
LLM and API integration
Evaluation, guardrails and human review
Model deployment and observability
Data engineering and cloud infrastructure
Custom AI applications and internal tools
Illustrative workflow
One possible operating flow
Conceptual example, adapted and tested against your real work during discovery and a pilot.
- 01Requirements mapped
- 02Architecture designed
- 03Prototype evaluated
- 04Controls implemented
- 05Production monitored
Evaluate the pilot
Measures for a go/no-go decision
No guaranteed result is assumed. Baselines and targets should be agreed before the pilot.
- Task quality on representative data
- Retrieval relevance and groundedness
- Latency and operating cost
- Exception and human-review rate
Possible connections
- Internal APIs and applications
- Data platforms and knowledge bases
- Cloud and model providers
- Identity, permissions and monitoring
Questions to resolve
What discovery should establish
Is this a ready-made product?+
No. This page describes a solution pattern. We would first validate your workflow, data, controls and business case, then design an appropriate implementation.
Where would people stay involved?+
That depends on the risk and commercial importance of each action. Sensitive, unusual and low-confidence cases can require review or be routed to a person.
How would we judge a pilot?+
We agree a baseline and use representative work to measure quality, effort, exceptions, operating cost and adoption before any decision to expand.
Have a related workflow?