Solution
MLOps&AIInfra
The plumbing that keeps AI running in production.
From £9,000Indicative. Every project is quoted to scope.
Fixed-price packages
Packages.
Clear, fixed starting prices. Pick a package or ask us to quote to your exact scope.
We build the infrastructure that keeps AI models serving reliably: model serving, monitoring, vector search and RAG pipelines that hold up at scale. The focus is production reliability, not demos.
What we do
Model serving
Deploying models behind stable, versioned endpoints that scale with demand and roll back cleanly. We handle batching, autoscaling and the latency budgets your product depends on.
Monitoring and evaluation
Tracking quality, drift, cost and latency so you know when a model is degrading before your users do. We put evaluation in the pipeline, not just at launch.
Vector search and RAG
Retrieval pipelines that ground models in your own data, with vector search tuned for relevance and freshness. We build the ingestion and indexing that keeps answers current.
Where it fits
Use cases.
Model serving
We put your models behind reliable, autoscaling endpoints with versioning and health checks. New versions can be rolled out gradually and rolled back quickly if they misbehave.
RAG and vector search
Build retrieval pipelines that ground a language model in your own documents using vector search. We handle chunking, embeddings, indexing, and keeping the index fresh.
Monitoring and drift
Track latency, errors, and input and output drift so you know when a model starts to degrade. Alerts fire before quality problems reach your users.
Capabilities
Everything it covers.
- Model serving with versioning and health checks
- Autoscaling that follows real traffic
- Vector search and retrieval pipelines
- RAG pipelines grounded in your own data
- Drift detection on inputs and outputs
- Monitoring of latency, errors, and quality signals
- Safe rollout and one-step rollback of model versions
- Reproducible pipelines from data to deployed model
Typical timeline
A deployed serving or RAG pipeline is usually demonstrable in five to eight weeks; full monitoring and scale work follow.
How scope works
We build and operate the infrastructure around your models; the quality of the models themselves still depends on your data. Ongoing compute and inference costs sit outside the build and we help you forecast them.
From £9,000
How we work
Four steps, no surprises.
Scope
We map the goal, the constraints and the risks, and agree a fixed brief before anything starts.
Design
Interfaces, architecture and the plan, reviewed with you before a line of code is written.
Build
Type-safe, tested delivery in short increments you can see and steer.
Launch & care
We ship, hand over everything, and keep it running if you want us to.
What you get
Deliverables
- Versioned model-serving infrastructure with autoscaling
- Monitoring for quality, drift, cost and latency
- Vector search index over your content
- RAG pipeline with ingestion and refresh
- Rollback and evaluation process documented for your team
Tools & technology
- Model serving
- Monitoring and observability
- Vector search
- RAG pipelines
Outcomes
What you can expect.
Models served reliably rather than run by hand
A quick, safe path to roll back a bad version
Early warning when a model starts to drift
Retrieval features grounded in your own content
Questions
Good to know.
How long does MLOps & AI Infra take?
Timelines depend on scope. Most engagements run from a few weeks to a few months, and you get a firm estimate after a short scoping call.
How is it priced?
From £9,000 is an indicative starting point. Every project is quoted to the scope we agree, with no surprises later.
Do we own what you build?
Yes. You own the source code, the design files and the accounts. There is no lock-in.
Do you maintain it after launch?
We can. Hosting, monitoring and updates are available as an ongoing plan, or we hand over cleanly to your team.
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