Service / AI
Private LLMs, on infrastructure you control.
Open models deployed in your cloud, on-premises or air-gapped, with inference tuned for cost and latency and the security controls your auditors expect.
[ 01 ]The short answer
What is Private LLM Deployment?
Private LLM deployment runs open language models such as Llama, Qwen, Mistral or DeepSeek inside infrastructure you control: your cloud account, your data centre or an air-gapped network. Prompts and documents never reach a third-party API. We size the hardware, choose and quantise the model, tune serving for throughput, and add the access control, logging and monitoring your security review asks for. It suits regulated data, predictable high volume and strict residency rules.
What you get
- Deployed model serving stack
- OpenAI-compatible secure gateway
- Benchmark report on your tasks
- Monitoring, logging and alerting
- Operations and upgrade runbook
[ 02 ]What we build
The pieces, built to last.
- 01
Model selection
Open models benchmarked on your tasks, so you run the smallest one that clears the bar.
- 02
Optimised inference
Quantisation, continuous batching, speculative decoding and prefix caching for throughput.
- 03
Secure gateways
An OpenAI-compatible API behind SSO, per-team keys, quotas and full audit logs.
- 04
Air-gapped installs
Offline model registries, signed artefacts and update procedures for disconnected networks.
- 05
Hybrid routing
Sensitive requests stay local; permitted ones may route to a frontier API, by policy.
- 06
Capacity planning
GPU sizing and autoscaling from measured traffic, with cost per thousand tokens reported.
[ 03 ]Where it pays off
Where it earns its keep.
- 01
Healthcare and life sciences
Clinical notes and research data processed without leaving the hospital network.
- 02
Financial services
Analysis over client data inside the bank's perimeter and audit regime.
- 03
Legal and professional services
Privileged documents searched and drafted from on local models.
- 04
Government and defence
Disconnected deployments with strict supply-chain controls.
[ 04 ]How we deliver
Four steps, one team, start to finish.
The same people scope it, build it and stay after launch. Timings are typical for a first release; we agree yours after the deep dive.
- 01Week 1
Find the decision
We pick the decision or task worth automating, measure how it is done today and agree what better means in numbers.
- 02Week 1 to 2
Build the eval first
A labelled test set from your own data comes before any prompt or training run, so every change is scored, not argued.
- 03Weeks 2 to 10
Build with guardrails
Models suggest, code decides. Permissions, injection checks, human override and audit logs are part of the first version.
- 04Ongoing
Ship and improve
We launch to real users, watch cost, latency and quality per request, and feed failures back into the eval set.
[ 05 ]Stack
Tools we reach for.
Chosen per project, and we work in yours. Nothing here is a lock-in: you own the code and the accounts.
- vLLM
- SGLang
- TensorRT-LLM
- Ollama
- llama.cpp
- Kubernetes
- NVIDIA GPUs
- AWS
- GCP
- Azure
- LiteLLM
[ 06 ]Related work
Built by the same team.
[ 07 ]Further reading
Problems we found, and how we would solve them.
- Cost3 min read
Your AI bill is a routing problem
ProblemAI applications send every request to a frontier model with a long prompt, so cost and latency grow with usage while most requests never needed that capability.ProposalRoute each request to the cheapest model that passes your evaluation for that kind of request, cache shared prompt prefixes, batch what can wait, and distil a small model for the highest-volume task.Read the proposal - Governance3 min read
The EU AI Act deadlines moved. Build the evidence trail anyway
ProblemTeams read the AI Act delay as a reprieve, while the evidence the Act requires, from risk management to logging and human oversight, takes longer to build than to document.ProposalGenerate compliance evidence as a by-product of engineering: versioned data and models, evaluation in CI, request-level logs, documented human oversight and incident handling, mapped once to the Act, NIST AI RMF and ISO/IEC 42001.Read the proposal
[ 08 ]FAQ
Questions buyers ask.
For many enterprise tasks, yes, especially after fine-tuning. For the hardest reasoning, frontier APIs still lead. We benchmark on your tasks and often recommend a hybrid with routing by policy.
At steady high volume it often is; at low or spiky volume it usually is not. We model both with your traffic before you buy hardware.
Yes. Holmes, one of our products, runs with local models for offline use, and we have built air-gapped procedures for clients.
[ 09 ]More AI services
- Custom AI Model DevelopmentA model trained on your data, owned by you.
- LLM Fine-Tuning and DistillationFrontier quality at a fraction of the cost.
- AI Agent DevelopmentAgents that finish the job, inside the rules.
- AI Workflow AutomationHours of back-office work, done in minutes.
- RAG DevelopmentAnswers from your documents, with citations.
- AI Memory and Knowledge GraphsAI that remembers what your organisation learned.
- MCP Server DevelopmentYour product, usable by every AI assistant.
- Voice AI AgentsEvery call answered, qualified and logged.
- Document AI and Intelligent Document ProcessingPaperwork turned into clean, checked data.
- LLMOps, Evaluation and GuardrailsKnow your AI works before customers find out.
- Predictive ML and ForecastingDecisions made on what will happen next.
One company at a time