Services / AI Engineering
Open-weight models, retrieval and document AI deployed on infrastructure you control — private cloud, on-premises or air-gapped — benchmarked on your own tasks, secured like the rest of your systems and handed to a team that can run it.
Benchmarked on your data. Nothing leaves the perimeter. Evidence before commitment.
The business case
Contracts, patient records, financial documents, defence work, data-residency commitments — when the constraint is real, self-hosting is an engineering discipline, not a workaround. Done well, it gives you owned capacity, predictable costs and governance you can defend.
Regulated or confidential data, residency or contractual requirements, high-volume workloads where per-token costs dominate, or a customer security review that hosted AI cannot pass.
If the data is not sensitive and volume is low, a hosted provider is usually faster and better. If the problem is integration architecture rather than model placement, API development fits. We will say so in the assessment.
Where this applies
These are example situations, not pre-packaged promises or claims about past results.
Run language models, embeddings and document processing on infrastructure you control, so regulated data never leaves the boundary — cloud, on-prem or a private region.
Learn: can the same feature ship with zero third-party model calls?
Semantic search and question answering over contracts, patient records, legal files or internal knowledge, with the index and the model self-hosted.
Learn: do answers cite the source documents they came from?
Extract and classify invoices, claims, medical notes or financial documents without sending them to a hosted AI provider.
Learn: does accuracy hold on your documents, not a public benchmark?
Internal assistants that answer from company knowledge and act through controlled tools, deployed on infrastructure the company owns.
Learn: does every answer and tool action appear in the audit trail?
Deploy AI workloads in a specific country, region or customer environment to satisfy residency, sector or contractual requirements.
Learn: does the deployment pass the customer's own security review?
Move high-volume workloads to self-hosted models with predictable capacity costs, keeping hosted models where they add quality.
Learn: what does a month of the workload cost at owned capacity?
How the deployment works
Documents and requests enter through the gateway, retrieval and inference run inside the perimeter, and answers return — no prompt content, embeddings or logs leave the boundary unless you explicitly allow it. The dashed line is the perimeter you own.
Benchmarking on your own tasks, infrastructure as code, access-aware retrieval, structured logging and a cost model — before any hardware commitment.
Self-hosting for its own sake, unsized hardware purchases, models accepted without licence review, and deployments without a named operator or upgrade path.
Sizing the workload
Self-hosting everything is usually the wrong answer. We start by measuring what the workload actually needs: latency, volume, language, context length and sensitivity. Then we place each part where it belongs.
Measure token volume, request patterns, latency targets and context needs from real usage, so hardware and model choices follow evidence.
Evaluate open-weight models on your own tasks and data — quality, language coverage, licence terms and inference cost — before committing.
Decide per capability whether it runs self-hosted, in a private cloud region or via a hosted API, with the reasoning documented.
Keep the application able to swap models behind an interface, so a better model next year is a configuration change, not a rewrite.
Deployment architecture
Serving models is an infrastructure discipline: GPU (or CPU) capacity, scaling, versioning and failure handling. We build deployments that survive a model reload, a node failure and a traffic spike.
vLLM, Ollama or TGI deployments with batching and quantisation tuned for the latency and throughput the workload needs.
Private cloud (AWS, Azure, GCP, OVH, regional providers), on-premises hardware, or an air-gapped environment — matched to residency and budget.
Autoscaling or fixed-capacity planning with load profiles from the assessment, so the deployment holds at peak without over-provisioning.
Versioned model registries, canary rollouts and a documented rollback path, so model updates are controlled releases.
Data & knowledge layer
Most private AI value comes from connecting models to company knowledge — retrieval, embeddings and document pipelines, all running inside the perimeter with the same governance as the rest of the data platform.
PostgreSQL with pgvector, Qdrant or OpenSearch deployed in the same environment, with hybrid search tuned on your documents.
Document parsing, chunking and embedding pipelines with provenance, so every answer can be traced to its source.
Search respects the permissions of the person (or agent) asking, so private AI does not become a way around document ACLs.
Deduplication, stale-document handling and refresh schedules, so retrieval quality does not decay silently.
Security & governance
Self-hosting removes a third-party data path but creates an owned one. We secure prompt handling, tool access and logging with the same controls your other systems carry.
The deployment cannot reach the open internet unless explicitly granted; prompt content, logs and embeddings stay inside the perimeter.
Model endpoints, tools and indexes authenticate through your identity provider, with scoped service accounts per capability.
Structured logs of prompts, responses and tool actions with retention aligned to your policy — evidence for audits, not a liability.
Model weights, inference frameworks and dependencies reviewed and pinned, with a process for accepting updates.
Cost & operations
Self-hosted AI shifts cost from per-token billing to infrastructure and people. We size that honestly — including the electricity, the on-call reality and the upgrade path — before you commit.
Hardware or cloud capacity, energy, support and engineering time, compared honestly against hosted pricing for the same workload.
Latency, throughput, queue depth and error dashboards, with alerts that reach a named owner.
Scheduled re-benching of newer open-weight models against your tasks, so the fleet improves on evidence.
Operational runbooks, failure recovery and training for the team that will own the deployment after delivery.
Tools & frameworks
We work across the open-weight ecosystem and the serving, retrieval and operations tooling around it. These are options, not a requirement to use everything on the list.
Model families evaluated against your tasks, licence terms and language needs.
Serving stacks tuned for batching, quantisation and latency targets.
Embedding and reranking models run inside the perimeter alongside the index.
Vector and hybrid search deployed in the same environment as the data.
Application logic that stays portable across hosted and self-hosted models.
Deployment, scaling and infrastructure-as-code for the serving layer.
Perimeter controls, scoped credentials and audit trails for the AI surface.
Quality benchmarks on your data plus operational dashboards and alerts.
Model and infrastructure selection depends on your tasks, data, residency requirements and budget. We recommend after benchmarking, not before it.
Delivery approach
You work with engineers inside the delivery process. Benchmarks come from your data, and every stage is demonstrated in the target environment before it is signed off.
Profile real usage, sensitivity and constraints; identify which capabilities genuinely need self-hosting and which do not.
Output: Workload profile, sensitivity map and a placement recommendation with costs.
Evaluate candidate open-weight models on your own tasks and documents; agree licence terms and quality thresholds before any hardware spend.
Output: Benchmark report on your data, model shortlist and infrastructure sizing.
Stand up inference, retrieval and the application layer in the target environment, with infrastructure as code and security controls from the start.
Output: Running private AI deployment with dashboards and access controls.
Run quality evaluation, load tests, security review and the customer's own compliance checks; fix what fails before release.
Output: Evidence pack: quality numbers, load results and security review outcome.
Transfer runbooks, dashboards and the upgrade process to your team; establish the re-evaluation cadence for future models.
Output: Operated deployment your team owns, with a documented upgrade path.
A decision-maker, access to the target infrastructure and the data owners, your security and compliance reviewers, and the people who will operate the deployment.
Placement, model choice and hardware sizing are benchmarked and agreed in writing before build. If the evidence says hosted AI fits better, that recommendation comes first.
Handover & ownership
Everything lives in your repositories and your infrastructure from day one. Handover includes the benchmark evidence, the runbooks and the upgrade process, so the deployment improves on evidence after we leave.
Evidence on which capabilities run self-hosted, which stay hosted and why — with the cost comparison in writing.
Deployment as code, versioned models and documented failure handling — no black boxes inside your perimeter.
Access-aware retrieval, structured logging and supply-chain review, aligned to your existing policies.
Runbooks, dashboards and trained operators, so the deployment does not depend on us to stay healthy.
Delivery does not include unlimited maintenance or a service-level commitment. We can agree ongoing operations support, or hand over fully to your own team.
Related engineering services
Private AI connects to governance, evaluation and the platforms it runs on.
Questions before starting
Sometimes, and only for specific reasons: data cannot leave the perimeter, residency rules require it, or volume makes owned capacity cheaper. For general-purpose quality, frontier hosted models usually win. We tell you honestly where self-hosting is the wrong choice — that assessment is most of the value.
On some tasks, yes, and on many production tasks, no — small and mid-size open-weight models are strong for extraction, classification, retrieval-grounded answering and internal assistants. We benchmark on your tasks before you commit, and the decision is based on those numbers, not marketing.
Depends on the workload profile: latency targets, concurrency and context length. Some workloads run on CPU or a single GPU; high-volume or long-context workloads need more. The assessment sizes this against real usage — we do not sell hardware.
Yes. We have designed deployments with no internet egress, where models, embeddings, updates and monitoring all operate inside the isolated network. Updates follow a controlled import process you approve.
Open-weight licences differ — some restrict commercial scale, some require attribution. We check licence terms against your use case during model selection and document the decision, so compliance is settled before deployment, not discovered after.
No. It removes one third-party data path; the system still needs access control, logging, retention and governance like any other system handling sensitive data. We build those controls into the deployment rather than treating self-hosting as the answer.
The application talks to models behind an interface, so a new model is a benchmark-then-migrate exercise, not a rewrite. We establish a re-evaluation cadence so upgrades happen on evidence — including deciding to stay put.
Your team owns the code, the infrastructure definitions, the model registry and the operational runbooks. We train the operators during delivery and can stay available for reviews or upgrades, but the deployment should not need us to keep running.
Start with the assessment
Share the data constraint, the workload and the target environment. We will profile it, benchmark the models on your tasks and recommend placement with costs — including telling you when self-hosting is the wrong call.