AI & AutomationDemos ship. Production doesn’t. We build the harness that gets AI live.
Your data has edge cases the demo never hit. Your APIs return inconsistent responses. Your users do things the prompt didn’t anticipate. Building AI for production means designing for failure — not just for the happy path.
No evals, no guardrails, no cost controls, no audit trail. Fine for a prototype. Unacceptable in a compliance-sensitive or customer-facing environment. Everyone wants to press a button and get the result. The model is the easy part. The infrastructure it runs on is where projects stall.
The use case is clear. Leadership has signed off. But the engineering work to connect the model to real data, real APIs, and real users keeps getting deprioritized. We take that work off your core team’s plate — and own it through go-live.
We train a dedicated AI agent on your system documentation, API specs, and data schemas. The agent writes the integration code and generates the test suite.
In 9 out of 10 cases, the real system behaves differently from the documentation. Our engineer identifies the gaps, contacts the relevant team, and resolves them. The agent handles the predictable work. The engineer handles the judgment calls.
We stay through go-live — including nights and weekends when it matters.
p99 halved, errors eliminated, RPS throughput +27% on same resources.
Error handling, retries, and fallbacks built in from sprint one.
Token usage, model selection, and caching designed from the start.
Audit trails, guardrails, and human-in-the-loop where the environment demands it.
Not a demo dataset, not a static knowledge base.
Evals, runbooks, and monitoring your engineers can maintain without us.
Customers onboarding cut from 2 weeks to 4 hours. Built for a compliance-sensitive environment with full audit trail and human escalation paths.
2 wks → 4 hrs
Production AI
Compliance environment
Graph UI capable of rendering and navigating hundreds of thousands of nodes — enabling data stewards, engineers, and analysts to explore complex enterprise data relationships. AI-powered false positive filtering for data qualification. AI agents determining column content through data intersection analysis — tasks previously requiring human review.
100k+ nodes visualised
AI false positive filtering
Enterprise scale
UK Predictable latency under concurrent AI/platform load. p99 halved, error rate eliminated, RPS throughput increased 27% on the same infrastructure.
p99 halved
0% error rate
+27% RPSconnecting language models to your APIs, databases, and document stores
retrieval systems that surface the right context from your real data
automated evaluation pipelines so you know when model quality degrades
replacing manual processes with AI-assisted flows that have proper error handling
task-specific agents with auth, state management, and observability built in
visualising complex data relationships at enterprise scale (100k+ nodes)
model selection, prompt caching, and token budgets that don’t surprise you
false positive filtering, column content detection via data intersections
approval flows, escalation paths, and override mechanisms for regulated environments
Scope confirmed after 1–3 sessions.
We write the spec – no homework on your side.
One well-defined AI task: one LLM integration, one automated workflow, one agent with a specific scope. The problem is clear, the boundaries are clear.
What’s includedAI that touches multiple systems — RAG pipelines, agents connected to real data, workflow automation across services. Requires data layer work, compliance considerations, or multiple integration streams.
What’s includedMulti-agent systems, enterprise data intelligence, AI on top of complex regulated infrastructure, or long-term AI programmes. Dedicated team, on-call during go-live.
What’s includedAudit existing data, APIs, and integration points the AI will touch
Define AI behaviour, edge cases, guardrails, success criteria, compliance requirements
Model integration, data pipeline, harness infrastructure, evals setup
Eval runs, edge case validation, compliance checks, joint sign-off
Production deployment, monitoring setup, handover docs delivered
Every AI system we deliver has error handling, retries, observability, and evals from day one. Not bolted on after something breaks in production.
Evals, runbooks, model configs, prompt versions — yours from day one. No ongoing dependency on us.
AI projects are especially prone to scope creep. We define behaviour, edge cases, and success criteria before build starts. Scope changes go through a change request.
We’ve delivered AI in regulated environments. Audit trails, guardrails, and human-in-the-loop aren’t optional extras — they’re built in from the start.
The engineers on the scoping call are the engineers who build. No bait and switch.
Our tech director owns every AI delivery. Not an AI consultant who recommends a framework and leaves. An engineer who commits and stays accountable through go-live.
Most AI implementations stall in requirements — not because the model can’t do it, but because nobody defined what “working” actually means in production. We define that with you.
Start with a scoping sessionNo SoW from your side. No pitch deck. No follow-up sequence.
30 minutes. We’ll tell you exactly how we’d approach your AI challenge — architecture, scope, timeline.
No SoW required. No pitch deck. No follow-up sequence.
No SoW required. No pitch deck. No follow-up sequence.