Public benchmarks.
Scored on someone else’s data, not on the documents and decisions you deal with.
Know how well it works,
before you rely on it.
We build tests from your own cases, score every model and agent on them, and run them again every time something changes.
Wherever the work is.
Hosted
Private cloud
On-premise
Edge
Air-gapped
What we build for evaluation.
The tests, the scoring and the record, built around your work and left running after we hand over.
-
Test sets
Real cases from your own work, labelled by people who know it, and kept apart from anything a model learns from.
-
Scoring
Every answer checked field by field against the right one, with accuracy reported at the automation rate you choose.
-
Model comparisons
Candidate models, ours or third-party, run on the same cases, each named with its licence.
-
Sign-off checks
Requests that need a person are counted on their own, so you see which ones a model would have let through.
-
Reruns on change
The same tests run again whenever a model, prompt or source changes, before the change goes live.
-
Run records
Every run kept with its cases, scores and errors, so any result can be traced back to the cases behind it.
A leaderboard is not your work.
Most AI is chosen on numbers measured somewhere else. The only test that counts is your own cases.
Demos.
A good demo shows the cases that work, not the ones that don’t.
Silent changes.
A model that passed last month can change underneath you without anyone noticing.
How an evaluation runs.
Agreed criteria first, then your cases, then a result you can act on. The same steps apply to our models and to third-party ones.
Scoping2 to 3 days
Choose one or two decision workflows.
Acceptance criteria agreed in writing before any results are seen.
EvaluationAbout 2 weeks
Candidate 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.
Private deploymentAgreed per client
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.
RerunsOn every change
Every model is tested field by field on examples it has never seen, and tested again whenever it changes.
If a change scores worse on your cases, you see it before it goes live.
Measured on your cases,
not a leaderboard.
What you get from every evaluation, whichever model it tests.
Every model named. With its licence, so you know exactly what you would be running.
Your threshold. Accuracy at the automation rate you choose, and the rest sent to a person.
Tested again on change. The same cases run before any change goes live.
Evaluations we have published.
The same method, run in the open on real work.
-
TypeSafe’s Jev, tested on real work
97.7% right on 177 scored questions, including invoices, expenses and a plant restart with an expired certificate.
Read the evaluation -
Cloudflare’s Clef, close behind
94.9% on the same 221 questions, and it caught 27 of 30 requests that needed a person to sign off.
Read the evaluation -
A small model that reads NDAs
It rules on 17 standard clauses and quotes its evidence: 84.5% on a sealed test set, running on an ordinary CPU.
Read the research -
When a spreadsheet changes its meaning
Twenty real public spreadsheets, three editions each. In 13 of 40 edition pairs, an automated reader should stop.
Read the research
Where it runs.
Tests run where the model will run, from hosted to air-gapped, so your cases never leave your boundary.
A managed environment.
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 hostedIn the cloud you use.
Deploy dedicated instances in your AWS, Azure, Google or Oracle account. Align the architecture with your cloud standards, operational responsibilities and integration requirements.
Discuss public cloudWithin your enterprise network.
Run it inside your own private cloud or VPC, with network access, storage and controls set around your boundary.
Discuss private cloudOn your infrastructure.
Run software, storage and local inference on your own servers. Plan capacity, integration, maintenance and support around the environment your team operates.
Discuss on-premiseClose to the operation.
Place intelligence near industrial sites and operational equipment, where latency and connectivity shape the design. Match the workload to the local compute available.
Discuss edgeWithout constant connectivity.
Operate local workflows without a continuous internet connection. Define how data is transferred, how updates are applied, and what must remain available offline.
Discuss offlineA physically isolated environment.
Plan for an installation with no external network connection. Local models, dependencies, data transfer and maintenance all become part of the deployment design.
Discuss air-gappedPut them to work.
Every one of these is tested this way before it is relied on.
Models
Specialist models, evaluated on your data and run inside your boundary.
Explore Models →Agents
Agents that run on their own and hand over to a person at the decisions that matter.
Explore Agents →Products
Ready-made products for recurring jobs, from documents and contracts to sustainability data.
Explore Products →APIs
Model, agent and data APIs that plug into the systems you already run.
Explore APIs →Data
Data collected, extracted and governed, with every value traced to its source.
Explore Data →Bring us the work where wrong is expensive.
Start with the operational problem. We’ll work out the data, models and agents it needs, and where it should run.
Talk to us →LondonDiscovery AI Limited · United Kingdom
HyderabadReDscvry Technology Private Limited · India
ChennaiReDscvry Technology Private Limited · India