Anyone can demo AI. We put it into production.
Almost every stalled AI project we open up is stuck on the same handful of things, and none of them are the model. This is the layer underneath that decides whether it ships.
What actually stops these projects.
None of this shows up in a demo, which is exactly why it is still unsolved by the time someone asks for a production date.
Data access
The prototype works because it can read everything. Apply the access rules that actually exist and a good share of the answers disappear.
Security boundary
Sending regulated data to a public endpoint is a conversation with your CISO that you are going to lose, and should.
Evaluation
Without a golden set, whether a change improved things gets decided by whoever is most senior in the room. That is not a quality process.
Cost
The pilot was priced on pilot traffic. Nobody modelled what happens when the whole company starts using it on a Monday morning.
Governance
Three weeks from now someone will ask why the system gave a particular answer. If you cannot reconstruct it, that is a finding.
Operations
Providers deprecate models, latency moves, quality drifts. These systems need an on-call rota like anything else in production.
We would rather kill it in week two than month six.
Most AI ideas should not survive the first stage, and finding that out cheaply is worth more than the build. The ones that do survive get built properly.
Prove or kill it
A two-week evaluation against real data with a defined success metric. Most ideas should die here, cheaply — and we will tell you when yours is one of them.
Build the foundation
Model gateway, private endpoints, retrieval pipeline, secrets and identity. The unglamorous layer that decides whether anything reaches production.
Harden and govern
Evaluation harness, guardrails, prompt and output logging, cost ceilings, red-teaming, and a model risk position your auditor accepts.
Run and improve
Monitoring for drift, latency, spend and quality — with a feedback loop that improves the system instead of just reporting on it.
The four layers under every production AI system.
- GovernanceL4
Model risk · lineage · output logging · retention
- OrchestrationL3
Agents · tools · routing · human checkpoints
- RetrievalL2
Ingestion · embeddings · reranking · permissions
- FoundationL1
Gateway · private endpoints · identity · secrets
Every AI system we deliver is built on the same four layers. Skip one and the system works in a demo and fails in production — usually on the day someone asks who can see what.
AI readiness & use-case selection
Which workflows repay automation, which don't, and what it will cost.
Private LLM platforms
Model gateways, private endpoints, data residency, logging and access control.
RAG & knowledge systems
Ingestion, chunking, embeddings, vector search, reranking and evaluation.
Agentic workflows
Tool-calling agents with human checkpoints, tracing and hard permission boundaries.
MLOps & inference platforms
Training and serving infrastructure, model registry, GPU capacity planning.
AI governance & assurance
Model risk, data lineage, prompt and output controls, and red-teaming of AI systems.
Evaluation & observability
Golden datasets, regression suites, tracing, drift detection and quality scoring in production.
AI cost engineering
Routing, caching, context budgeting and model selection — spend that scales sub-linearly with usage.
Where AI earns its cost.
We start from the workflow, not the model. These are the patterns that repay the investment most reliably.
Knowledge assistants
Retrieval over your own documents, policies and tickets — with permissions enforced at retrieval time, not bolted on afterwards.
Document & claims processing
Extraction, classification and routing across high-volume document flows, with confidence thresholds and human review where it matters.
Customer operations
Triage, summarisation and drafted responses inside existing workflows — measured on deflection and handling time, not novelty.
Engineering acceleration
Coding agents, test generation and migration tooling wired into your pipelines, with review gates that stay mandatory.
Risk, fraud & compliance
Anomaly detection and control monitoring where explainability is a regulatory requirement, not a nice-to-have.
Operational intelligence
Incident summarisation, log triage and capacity forecasting — AI applied to the infrastructure we already run for you.
Model-neutral, by design.
We are not tied to a vendor. We pick the model and the platform that fit the workload, the data residency requirement and the budget — and we build so you can switch.
Why neutrality matters here more than anywhere else
Model capability and pricing move every few months. A system wired directly to one provider is a rewrite waiting to happen. We put a gateway in front, keep prompts and evaluation portable, and treat the model as a replaceable component — because it is.
Have an AI project that's stuck?
Most are stuck on infrastructure, not intelligence. Thirty minutes is usually enough to find out which.