We have the data. Who builds the model?
Custom models
From your data to a working model, for teams that don’t have AI specialists of their own.
Talk to us →Introducing Resolve, our fast decision-making model. Meet Resolve →
We build our own models, train them for you, and deploy third-party models when they fit.
Each one is built for one kind of work, and scored on that work before it is trusted with it.
Reads business documents and makes the call: pay or hold, on time or late, who signs off.
View modelDecides whether two records are the same company, and matches automatically only when the evidence is there.
Our own document reading models: complex documents rebuilt as structured data.
Finds personal and sensitive data in documents, so it can be redacted before it leaves your control.
Rules on 17 standard NDA clauses and quotes its evidence: 84.5% on a sealed test set, running on an ordinary CPU.
Read the researchBuilt from the start, or trained on your material. Third-party models are tested on your work too, before we deploy them.
Every model is tested field by field on examples it has never seen, and tested again whenever it changes.
Open models, chosen and tested on your own cases, then run privately in your environment. Decisions above your threshold are automated, and the rest go to a person.
Choose one or two decision workflows.
Acceptance criteria agreed in writing before any results are seen.
Candidate open models run on 200 to 500 of your labelled cases. We report accuracy, accuracy at your automation rate, calibration, speed and errors by class.
Measured on your cases, not a public leaderboard. Every model named with its licence.
The chosen model is served privately, with your threshold, review routing and audit log.
Your cloud or a private endpoint. Your data is not used for training without written consent.
Optional and paid. We train a model adapted to your cases.
Ships only if it beats the chosen model on your held-out cases.
The AI goes to where the work is. Ours, yours or third-party: we deploy any model in any of these, from hosted to air-gapped.
Start with a hosted service when speed of adoption and managed operations are priorities. Agree data handling, access and service requirements as part of the engagement.
Discuss hostedDeploy dedicated instances in your AWS, Azure, Google or Oracle account. Align the architecture with your cloud standards, operational responsibilities and integration requirements.
Discuss public cloudRun it inside your own private cloud or VPC, with network access, storage and controls set around your boundary.
Discuss private cloudRun software, storage and local inference on your own servers. Plan capacity, integration, maintenance and support around the environment your team operates.
Discuss on-premisePlace intelligence near industrial sites and operational equipment, where latency and connectivity shape the design. Match the workload to the local compute available.
Discuss edgeOperate local workflows without a continuous internet connection. Define how data is transferred, how updates are applied, and what must remain available offline.
Discuss offlinePlan for an installation with no external network connection. Local models, dependencies, data transfer and maintenance all become part of the deployment design.
Discuss air-gappedThe finished model belongs to you, and it stays yours wherever it runs.
Yours to keep. It runs on your servers, in the cloud or fully air-gapped.
Trained on your data only. What it learns from your documents is never used for anyone else.
Runs where you choose. Hosted, private cloud, on-premise or air-gapped. You decide.
Models power the rest of what we build. Each of these is built on the same tested foundations.
Agents that run on their own and hand over to a person at the decisions that matter.
Explore Agents →Ready-made products for recurring jobs, from documents and contracts to sustainability data.
Explore Products →Model, agent and data APIs that plug into the systems you already run.
Explore APIs →Data collected, extracted and governed, with every value traced to its source.
Explore Data →Tests built from your own cases, rerun every time a model changes, so you always know how well it does your work.
Explore Evaluations →Start with the operational problem. We’ll work out the data, models and agents it needs, and where it should run.
Talk to us →