AI Models
Purpose-built, sovereign AI that understands your business. From open-weight models and voice AI to flexible integrations, we help you deploy intelligence that evolves with your organisation.
Model-as-a-Service
Model-as-a-Service,Delivered as an Outcome.
BasinWright gives enterprises the platform to build, govern, and scale AI with confidence.
Fraud loss, claims cycle time, procurement leakage, engineering knowledge — for regulated industries that need AI to be secure, explainable and sovereign.
The stack
Stop managing fragmented infrastructure. Start directing outcomes.
Our AI agents connect your systems, automate work, and turn data into action from one secure interface.
Data Platform
Turn raw data into governed, usable insight. We unify ingestion, processing, orchestration, APIs, and AI services into a secure data foundation built for action.
Infrastructure
The compute, performance, and resilience modern AI demands. From training and inference to storage, pipelines, and AI-native data centres, BasinWright provides infrastructure designed to scale.
The platform underneath
You buy the outcome. You keep the platform that delivers it.
The work lands somewhere: a console you operate, with your models, your data connections, your deployments and your evidence in it. Not a slide pack and a handover call.
One control plane, every use case
Fraud, claims, procurement and AI safety arrive in the same place, with one audit trail and one access model behind them — rather than six vendors and six governance stories.
Driven by API, not just by console
A REST API across the platform surface, Python and TypeScript SDKs, an OpenAI-compatible inference gateway that meters every call, and MCP so your models are reachable as tools. Anything you can click, you can automate.
Yours to keep
The estate runs in your tenancy under your keys, and what it produces — models, weights, governed corpora, decision history — stays inside it.
BasinWrightOverview
Modules
Model 360
drift · evidence
Deployments
endpoints · rollback
GPU 360
fleet · utilisation
Data Hub
stores · queries
Guardrails
screening · evaluators
Automations
workflows · triggers
Industries
Enterprise problems we solve
Not products — the decisions your organisation already makes, slowly or inconsistently, at volume. Across banking, insurance, manufacturing, energy, procurement, knowledge and AI safety.
Outcomes
Buy the outcome.
Every AI project should start with a measurable business outcome. Pick the number you want moved, and we will tell you honestly whether a model can move it — and what a realistic target looks like on your data.
Reduce fraud losses
Catch more of what matters without raising false declines on good customers.
Recover procurement spend
Find the leakage in specifications, tenders and invoices before it is paid.
Accelerate claims processing
Triage, severity and routing decided in hours rather than days.
Improve customer retention
See the accounts about to leave while there is still something to do about it.
Reduce document search time
Answers grounded in your own documents, with the passage they came from attached.
Increase engineering productivity
Institutional knowledge that answers a question, instead of a folder nobody can navigate.
Strengthen regulatory compliance
Evidence, lineage and reason codes attached to every decision, by default.
What you can buy
A catalogue of use cases, each bought for a number
Some ship today as reference implementations — a working data pipeline, a trained artifact, an evaluation report and a model card, ready to be tuned on your data. Others are fully specified designs we build to order. We tell you plainly which is which and scope the difference honestly.
Core fintech & customer intelligence
Real-time fraud scoring, churn risk, lifetime value and behavioural authentication.
- Cut fraud loss without more false declines
Credit, capital & regulatory risk
IFRS 9 ECL, Basel IRB capital, application scorecards and transaction monitoring.
- Numbers that survive challenge
Insurance
Automated underwriting, claims triage, fraud-ring detection and loss reserving.
- Shorter claims cycle time
Procurement & supply chain
Spend anomalies, supplier risk, three-way match, tender extraction and spend classification.
- Recover leaked spend
AI security & LLM safety
Injection, leakage, poisoning and agentic-abuse detection for your own GenAI stack.
- Ship GenAI that passes risk review
Knowledge & GenAI
Grounded assistants and routing over your own document estate, with citations.
- Answers with sources, not guesses
GPU-served & sovereign
On-premises embeddings, self-hosted LLM assistants and domain classifiers.
- Nothing leaves your perimeter
Not in the catalogue?
Custom model development is a productised service, not a research project: the same authoring pipeline, the same governance, the same delivery discipline.
- Scoped to an outcome like everything else
How it works
From business problem to measured outcome
Five steps, and the first one is not a model. This is the actual engagement rather than a marketing abstraction.
Business problem
The metric you want moved, agreed up front. It becomes the definition of done.
Enterprise data
Your warehouse, database, object store or stream — read where it already lives, not copied into ours.
Purpose-built AI model
Trained, tuned or calibrated on your population, your policy and your thresholds.
Enterprise AI agent
The model put to work inside the process that actually makes the decision.
Measured business outcome
Instrumented from day one, monitored for drift, retuned when it slips.
The engagement does not end at deployment — that is where it starts. A model that was accurate at go-live and silently decayed six months later has failed, even if it never threw an error.
Why BasinWright
The four things that decide whether AI survives contact with your risk committee
Accuracy is rarely what kills an enterprise AI project. These are what it dies of instead.
Purpose-built models
A reference model is a starting point, never the delivered thing. What runs in your production is calibrated on your population, your policy and your thresholds — and where the honest answer is that a problem does not need AI at all, we say so.
- Tuned on your data
- Your thresholds, versioned
- Deterministic where it must be
- LLM where it must read language
One platform
The Enterprise Intelligence Platform
One stack, operated and governed from the same place. Models as a Service is one layer of it — not the whole of it.
Requests travel up the stack; retrieved context and results settle back down. Nothing leaves the control plane on the way.
- Request travelling up the stack
- Retrieved context settling back down
Platform capabilities
What the platform is made of
Models as a Service is one capability inside the platform, not the platform. Everything here lands in the same control plane, with the same audit trail — so the sixth use case arrives where the first one did.
01 / 08
BasinWright MaaS
Models as a Service
Deploy production-ready AI models through secure APIs without managing infrastructure.
Model CatalogServerless InferenceDedicated EndpointsAuto ScalingAPI GatewayUsage AnalyticsVersion Management
02 / 08
BasinWright Compute
Enterprise GPU Cloud
High-performance infrastructure designed for training and serving AI at scale.
GPU MarketplaceH100 / H200 / B200RTX fleetMulti-node TrainingKubernetesBare MetalAuto Scaling
03 / 08
BasinWright Agents
Cognitive AI Agents
Build intelligent enterprise agents that plan, act and escalate under policy.
MemoryPlanningReasoningTool CallingWorkflow AutomationHuman ApprovalKnowledge Retrieval
04 / 08
BasinWright Studio
Visual AI Development Platform
Design, evaluate and ship AI systems without leaving one workspace.
Drag-and-dropPrompt EngineeringFine TuningEvaluationDeploymentMonitoring
05 / 08
BasinWright Knowledge
Enterprise RAG Platform
Connect the systems your organisation already runs on and make them answerable.
SharePointSAPOracleSalesforceMicrosoft 365Google WorkspaceDatabases, Files & APIs
06 / 08
BasinWright Data Hub
Unified Enterprise Data
Bring structured, semi-structured and unstructured data into one governed plane.
Structured & Semi-StructuredUnstructuredDocuments, Images, VideoSQL & NoSQLStreams
07 / 08
BasinWright Observe
Monitoring & Governance
Trace every inference, cost centre and policy decision across the estate.
Inference TracingCost AttributionDrift DetectionPolicy EnforcementAudit Export
08 / 08
BasinWright Marketplace
Models, Agents & Extensions
Procure vetted AI capability with commercial and compliance terms attached.
Vetted PublishersPrivate ListingsUsage-based TermsOne-click Deploy
Why enterprises choose BasinWright
What you are actually buying
Most organisations putting AI into production end up with a generic platform that has no models in it, or a point solution that brings its own console and its own audit story. This is neither: you are buying a target metric, the work of hitting it, and everything that work produces.
You own what we build
And the exit is written first
The model, the weights, the governed corpora it learned from and every decision it has made sit inside your estate throughout. Leaving is us stopping work rather than you extracting anything — and which artefacts transfer, in what format and on what cadence is agreed in writing before the first deployment.
Runs on your cloud
Not ours
Your tenancy, your region, your keys — or your own hardware behind your own firewall, with nothing leaving the perimeter.
Deterministic and LLM
The right tool for every decision
Reproducible models where a decision must be defensible under challenge; language models where the problem is genuinely language. Most delivered use cases are both.
One governance model
Across every AI deployment
One audit trail, one access model, one place to see what every model in the organisation is doing — instead of six vendors and six governance stories.
A catalogue, not a blank page
Reference implementations you tune
Each shipping use case arrives as a working data pipeline, trained artifact, evaluation report and model card, ready to be calibrated on your data.
Compliance mapped up front
Shortens vendor risk review
Use cases ship with the regulatory frameworks they were designed against already mapped, and fairness controls standard on scoring models rather than on a roadmap.
Worked designs
Judge the thinking before you believe the claims
There is no wall of customer logos here, because we are early and would rather say so. What there is instead is the design work: the problem shape, the architecture we would propose for it, and what we would expect to be measured against afterwards.
Deployment
Wherever your policy allows it to run
Residency, sovereignty and blast radius are usually decided before the model is. Every option below runs the same platform, with the same governance.
Ship with
PythonJava.NETJavaScriptREST APIsCLITerraformGitHub
Cloud
Hybrid
On-premises
Sovereign
Private GPU
Air-gapped
Multi-cloud
Edge
Integrations
Connected to the systems your business already runs on
Microsoft 365SAPOracleSalesforceSharePointSnowflakeDatabricksKafkaPostgreSQLServiceNowAmazon S3Google Workspace
Customer journey
How you get from first conversation to enterprise-wide
Nobody should sign up for an enterprise AI programme. They should sign up for one outcome, and then decide.
AI assessment
We look at the decision, the data behind it, and whether a model can honestly move the number.
Pilot
One use case, built on your data, measured against the target agreed at the start.
Production
Deployed into your tenancy, governed, instrumented — and owned by you.
Scale enterprise-wide
The second use case lands in the same control plane as the first. So does the sixth.
A shipping use case typically goes from kickoff to a governed, monitored endpoint in weeks rather than quarters.
Talk to us
Let's identify your first AI outcome
Book a 60-minute enterprise AI discovery session. We will come back to you with what we found, whether or not it points at us.
High-impact AI opportunitiesROI potentialData readinessRecommended implementation roadmap