Machine Learning and AI
AI that runs on your data, in your environment.
On premise AI, fine tuned language models and AI assisted tools for support, sales and clinical teams.
We deploy AI where the data cannot leave the building. That means running models on your own hardware, tuning them on your own documents and keeping personal data inside your network.
Where AI meets customers we build the surrounding workflow: routing requests, drafting replies, summarising calls and giving staff a tool they can check before anything is sent.
What is included.
The work this service is scoped around. Anything outside it is agreed in writing before we start.
Air-gapped and on premise model deployment
Models served on hardware inside your network with no outbound dependency.
Fine tuned domain language models
Training on your own documents so answers use your terminology and policy.
Automated PII detection and scrubbing
Identify and redact personal data before it reaches a model or a log.
Intelligent ticket and load routing
Classify incoming work and send it to the right queue or engineer.
AI powered CRM automation
Summarise threads, draft follow ups and keep records current.
Call centre intelligence
Transcribe calls, score outcomes and surface coaching points.
AI powered applications
Search, extraction and drafting built into the tools your staff already open.
Healthcare diagnostics support
Assistive checks over clinical data that keep the clinician in the decision.
Scope it, price it, decide.
Tell us what you are trying to do. We come back with the options, what each one costs and the order we would do the work in.
- On premiseModels served on hardware inside your network
- Fine tunedTrained on your documents and your terminology
- ScrubbedPersonal data removed before it reaches a model
- ReviewedA person stays in the decision, and can check the working
What we build with it.
AI where it earns its place: work that was slow, repetitive or simply not being done.
Private assistants
Models answering from your own documents, on your own hardware, with citations.
Document intelligence
Extraction, classification and search over paperwork that is still in filing cabinets.
AI CRM automation
Threads summarised, follow ups drafted and records kept current by themselves.
Chatbots and ticketing
Assistants that answer from policy and route to a human when they cannot.
Analytics that explain
Reporting that says what changed and why, over your own operational data.
Detection that learns
Triage and correlation layered onto your SIEM so analysts see the alerts that matter.
Contact centre intelligence
Calls transcribed, outcomes scored and coaching points surfaced from real conversations.
Clinical decision support
Assistive checks over patient data that leave the clinician making the call.
Where the models run.
The deployment is a policy decision before it is a technical one, so we start from what your data is allowed to do.
- Air gapped
- On your hardware, with no outbound dependency at all, for data that cannot leave the building.
- Private cloud
- Inside your cloud tenancy, with the network, keys and logging under your control.
- Hybrid
- Open models for sensitive work, hosted models where policy allows, one workflow over both.
- Hosted providers
- Commercial APIs where they are the right answer, with the data handling written down.
How an AI project runs.
The demonstration is the easy part. We build the thing that survives a Monday morning.
- 01
Use case and data
Agree the work to remove, and check the data that would feed it actually exists.
- 02
Evaluate
Build the test set before the model, and measure against it rather than by impression.
- 03
Build the workflow
The model is the small part: retrieval, permissions, review, escalation and the interface around it.
- 04
Operate
Accuracy and cost watched over time, with the tuning that follows real use.
Where it runs.
Inference on hardware you control
Test sets built before the model is chosen
Questions we are asked.
Does our data train someone else’s model?
Not on an on premise deployment, and not on a private cloud tenancy. Where a hosted provider is used, we write down exactly what is sent, what is retained and what is not.
Which models do you use?
Whichever fits the job and the policy: open models served on your hardware, commercial providers where they are allowed, or a combination. The workflow around the model is usually the larger piece of work.
How do you stop it making things up?
By grounding it in retrieved documents you already hold, citing the source, scoring answers against a test set built before the model was chosen, and keeping a person in the loop for anything consequential.
Also under Engineering.
Tell us what you need to build, protect or automate.
A 30 minute call with an engineer, not a sales script. You leave with a clear next step, whether or not we work together.