// the_solutions
One platform. Three ways to use it.
Veris builds one thing: a working twin of your stack and the people who use it. Every solution below is that same platform pointed at a different question, so the environment you stand up to evaluate a vendor is the environment you train your own agents in and the environment your coding agents test against.
// 00_one_platform
Build the world once. Ask it three questions.
Actors that behave like your users, twins of every system you depend on, scenarios written from your reality, an engine that keeps every run honest, and a loop that turns each failure into a fix. Those five components do not change between solutions. What changes is the question you point them at.
// 01_test_and_compliance
Know what an AI system does before you sign for it.
A working replica of production, sealed off from the real one: the same systems, the same data shapes, the same people around it. Everything you buy and everything you build runs against your actual operating conditions, so compliance, cost, and risk are on the table before the contract is signed or the traffic is opened.
- air-gapped by construction, with no network path back to production
- one environment for the AI you buy and the AI you build
- a decision packet your risk committee and your auditor can both read
Applies to
| Vendor A | Vendor B | Vendor C | In-house | |
|---|---|---|---|---|
| Policy adherence | pass | pass | fail | pass |
| Disclosure rules | fail | pass | fail | warn |
| Handles bad input | warn | pass | pass | pass |
| Escalates correctly | pass | pass | warn | fail |
| Cost per case | warn | pass | pass | fail |
// 02_training_and_loops
Ship an agent that has already failed a thousand times.
Before launch, battle-test the agent until it passes QA and compliance. After launch, every production failure comes back as a scenario it has to survive, so the agent that shipped keeps getting better instead of quietly drifting.
- scenarios generated from your code, prompts, and production traces
- root-cause analysis with concrete fixes to harness and instructions
- RL fine-tuning on verified scenarios when the model is yours to train
Applies to
// 03_agentic_sdlc
Give coding agents a stack they can safely break.
A stateful dependency sandbox. Every outbound call, whether to a database, an internal service, or a third-party API, hits a live simulated instance instead of the real system. Integration tests run inside the agent loop, so the issues get found and fixed before the pull request is ever opened.
- stateful services, not static mocks: write a row, read it back
- one full stack per agent, thousands running in parallel
- spin up, use, reset, tear down, all inside the agent loop
Applies to
// where_to_start
Which one is your problem today?
Most teams start with one and pick up the others later, because the environment is already standing.
See your stack, simulated.
Bring the systems your agents touch and the question you need answered. We will stand up the twin and show you the first results.