AI & Data Engineering
Private LLM deployments, retrieval-augmented systems and streaming pipelines that put your own data to work without it leaving your control.
The problem we solve
Your data, inside your boundary.
Private model deployments, retrieval-augmented systems and streaming pipelines. We build AI that answers from your own corpus, with evaluation harnesses so you can prove it still works next quarter.
Every model call is logged, costed and attributable. A demo that cannot be evaluated is not a system, it is a screenshot.
At a glance
TYPICAL INVESTMENT
From £40,000
TYPICAL DURATION
6 to 16 weeks
TEAM SHAPE
1 architect, 2 to 4 engineers
ENGAGEMENT MODEL
Fixed scope, or a pod
HANDOVER
Runbooks plus paired operation
What you get
Deliverables, named in
the statement of work.
Private and self-hosted model deployment
Retrieval with Pinecone, Qdrant or pgvector
Agent workflows with human approval gates
Kafka, Snowflake and Databricks pipelines
Evaluation, guardrails and cost controls
How the engagement runs
Stage by stage, in the open.
STAGE 1
Discovery
Two weeks, fixed fee. We map the domain, the constraints and the integrations, and produce an architecture you own whether or not you continue.
STAGE 2
Architecture
A written technical design, a delivery plan and a costed estimate with an explicit list of what is out of scope.
STAGE 3
Build
Two-week sprints, a demo at the end of every one, and a shared board you can read without asking us what it means.
STAGE 4
Hardening
Load, security and disaster-recovery testing before launch, plus runbooks and handover sessions for your team.
STAGE 5
Operate
Managed service against an availability target, or a clean exit with everything documented. Both are supported.
Two weeks to know what
this would cost you.
The assessment is fixed fee and yours to keep. Most clients use it to get budget approved, whether or not we do the build.