Frequently Asked Questions
Veris AI is a simulation platform that recreates the full "world" your AI operates in, such as users, tools, APIs, and data, inside an isolated, high-fidelity sandbox. Use it to evaluate any AI system you build or buy, to train agents before launch and keep improving them after, and to give coding agents stateful environments to verify their work, all before anything touches real systems or users.
Two things, spanning the AI lifecycle:
Veris for benchmarking: evaluate any AI system, in-house or bought, for quality, risk, and cost inside an isolated twin of your production stack, and walk into every review with evidence.
It also covers training and loops: battle-test your agents before launch, then keep improving them after by turning every production failure into a regression scenario, or into an RL reward when your model is open-source.
Veris for Agentic SDLC: give coding agents like Claude Code, Codex, or Cursor a stateful simulation of your dependencies, so integration tests run and bugs get fixed before the PR opens.
Static test sets and prompt tweaks do not capture the dynamic, multi-turn, stateful complexity of real AI systems. Veris generates thousands of realistic, simulated interactions, including adversarial users and tool failures, so whether the system is built in-house or bought, you find its problems before your customers do.
Veris works with your AI system as is, whether built in-house or bought, on any framework, model, or deployment location. Integration means pointing the system's tool and API calls at the simulated environment, and third-party or black-box systems can be evaluated through their API endpoint. Nothing changes inside the system itself: no SDK, no code changes, and no source access needed for a product you didn't build.
Quickly, and with Veris alongside you. Most engagements start on SaaS with synthetic data and an agent in a dev or test environment, and a first bench is ready in about two weeks from signing. If the platform has to run inside your VPC, that deployment usually takes a couple of days first. Book a demo and we will scope it with you.
Yes. Veris AI simulations use synthetic scenarios, if you do not want to share data. Veris AI is SOC 2 Type II compliant, meets HIPAA and GDPR requirements, and supports deployment on your own cloud (AWS, GCP, Azure) or on-prem for full data control.
Start from what you need. To know what an AI system, in-house or bought, will actually do before it ships or before you sign, and to keep an agent improving after launch, use Veris for benchmarking. To let coding agents verify their own work against stateful dependencies before the PR opens, use Veris for Agentic SDLC. Both run on the same simulation platform, so you can start with one and add the other.
Veris is licensed to enterprises, per bench or under an annual platform licence, and can be deployed into your own VPC. There is no public price list. Contact us for pricing, or book a demo and we will scope it with you.
Those tools trace what an agent did, store datasets, and score outputs. Veris is the environment the agent runs in: stateful twins of your systems, simulated users, and scenarios with verifiers that check the final state of the world rather than the transcript. Most teams keep their tracing and evals and add Veris for the part a dashboard cannot do: reproduce a failure, run thousands of variations of it in isolation, and gate a release on the result. Veris reads incidents from Langfuse, Arize, Datadog, and Braintrust to seed new scenarios.
A vendor's simulation grades the vendor's agent on the vendor's exam. It cannot compare two vendors on the same tasks, it does not compile your policies into pass criteria you sign off, and its results do not survive a switch. Veris runs every candidate, bought or built, against the same seeded twin of your systems with the same simulated users and the same checks, and keeps the evidence on your side.
Yes. Candidates connect as a container, a hosted endpoint, or through connectors such as Genesys Chat and Google CX Agent Studio, and a managed harness can stand in as a baseline. In a typical engagement the personas and scenarios are drafted from your material in the first days, candidates are connected in the first week, and the side-by-side report lands in about two weeks, with three or more runs per candidate on the identical exam.
Velocity and confidence. Engineers iterate faster because a safety net catches regressions immediately, and coding agents fix integration bugs before the PR opens. Reliability is quantified (e.g., "98% success rate on refunds") before launch, and a vendor's claims become measurable before you sign. Once in production, incidents flow back into simulation as regression scenarios, so the system keeps improving after launch.
Engineering and AI/ML teams building production-grade agents, whether at a startup shipping their first or an enterprise scaling agentic automation across departments. For Test and Compliance, the buyers also include compliance and risk officers, alongside procurement teams evaluating third-party AI before they sign.
Yes. A vendor's system is evaluated as a black box through its API endpoint. It runs inside the twin against simulated systems and synthetic records, so it never learns your production topology and takes nothing home. Vendor claims become measurable before the contract, not after the incident.
Simulation environments are isolated sandboxes with no route back to production, and the data inside them is synthesized, so no real customer records are needed. Veris is SOC 2 Type II compliant and can be deployed in your VPC or on-prem, so everything stays inside your existing controls.
Veris does not replace your testers. It changes what they review. Static golden datasets and manual QA passes cannot keep up with multi-turn, stateful agents that change with every prompt, tool, or model update. Veris runs thousands of simulations in parallel and grades every attempt, so human reviewers sample verdicts and confirm or overturn them instead of reading every session, whether you are hardening an agent you built or vetting a system you bought.
It's complementary. Real user data is ground truth but scarce and slow to collect. Veris acts as the data multiplier: you use production insights to update simulation parameters and create failure variations, which then generates 100x more synthetic training data to fix the issues you found.
Enterprises use Veris to evaluate third-party AI against their own operating conditions before signing. Banks use it to compare AI productivity tools for their employees on their own task registers. Product teams battle-test customer-facing voice and chat agents on workflows such as billing, collections, scheduling, and support. Engineering teams give coding agents stateful dependency sandboxes so integrations are verified before the PR opens. Email us at hello@veris.ai for examples relevant to your use case.
Veris fits into your existing workflow rather than replacing it. Gate every deploy in CI, pull real incidents in from your observability stack, and drive simulation environments over MCP, the CLI, or the API. Your infrastructure and tooling stay as they are.
Production is the most dangerous place to start learning. It starts off low-volume, high-risk, and high-noise. Veris AI simulations solve this cold-start problem by exposing your agent to thousands of edge cases, adversarial user scenarios, and tool failures in a controlled sandbox before a real customer ever uses the agent. Once in production, Veris reads from your production logs and traces, turning real incidents and usage patterns into new simulation scenarios, so the agent keeps improving automatically.
Veris simulates the entire environment your AI system operates in: users (with realistic personas and behaviors), enterprise tools (Email, Slack, Jira, databases, etc.), APIs, and system states, all maintaining full statefulness throughout each episode. At full scale, that becomes a twin of your production estate: the core systems, the data shapes, and the people around them, mirrored as a sealed replica.
The twin has no network route to production; the path does not exist by construction, so there is no rule to misconfigure and no exception to grant. The systems inside it are simulations, and nothing under evaluation can touch a real customer, a real ledger, or a real record.
Simulations run on synthetic data that carries the shape, logic, and messiness of yours without containing anyone's actual information. If you would rather use your own data, it runs inside your VPC under your existing controls and never leaves.
We use probabilistic personas rather than static scripts. Simulated users have unique goals, personality traits, and frustration thresholds. They can be cooperative, confused, or adversarial, so your agent needs to learn how to handle real human behaviour instead of overfitting on happy paths.
Veris tests both voice and chat agents. A voice agent can be evaluated starting from just a phone number, with simulated callers who speak, interrupt, and mishear like real ones. Chat and text agents connect through an API endpoint or a code integration. The same personas, scenarios, and verifiers apply across modalities, so a system that works in one channel is proven in the other.
Yes. Veris ships stateful twins of more than 70 enterprise and SaaS services, such as Salesforce, Stripe, Slack, Jira, Gmail, and PostgreSQL, and builds a custom twin from an API spec in 24 to 48 hours. A twin keeps state for the whole session: if your agent deletes a database row, it stays deleted for the rest of that run. That statefulness is what makes multi-step reasoning testable.
Each coding agent gets its own dependency sandbox: a full, stateful stack of databases, payment APIs, CRMs, message queues, and internal services it can spin up, test against, reset, and tear down from inside its own loop, over MCP, the CLI, or the API. Sandboxes are isolated per agent, so thousands can run destructive tests in parallel without sharing a staging environment.
We provide custom-made and user-provided success metrics (did the agent achieve the goal?), granular step-by-step rewards for Reinforcement Learning, safety flags (PII leaks, hallucinations), and full trajectory analysis of Thought-Action-Observation loops for debugging.
Every scenario is paired with a verifier that checks what the system actually did to the environment, not just what it returned. Runs are scored automatically on every execution, and each result is reproducible and seed-pinned, with the full conversation and tool-call trail behind it.
Yes. Veris slots into an agentic loop as its evaluation step: the agent acts in a simulated environment, verifiers score what actually happened, and the result feeds the next iteration. That loop can be a coding agent verifying its work before opening a PR, a CI gate scoring every push, or an RL loop where verified runs become rewards, all driven over MCP, the CLI, or the API.
Veris benches export to Harbor format, so the tasks, worlds and graders you built for evaluation can drive RL training on any compatible backend. Veris also provides full training and fine-tuning infrastructure on top of Baseten, Fireworks and other major providers.
Yes. Veris offers both prompt optimization and Reinforcement Fine-Tuning as built-in optimization capabilities within the platform, so you can go from evaluation to improvement without switching tools.
Academic environments are typically static or toy problems. Veris is built for enterprise complexity: stateful, multi-turn workflows involving messy tools (CRMs, calendars, databases) and ambiguous user intent, where business outcomes are the priority.
Veris Bench runs from GitHub Actions or any CI. The pattern most teams use is a small trial on each pull request and the full trial nightly, with the job failing when a version's pass rate drops below the floor you set. The floor is judged on the top of the pass rate's confidence interval, so noise alone does not fail a build. Coding agents run their existing test suites against a Veris sandbox the same way, on every PR.
Yes. Veris is model-agnostic. It works with any LLM powering your agent. Open-source (Llama, Mistral, etc.) or closed source (GPT, Claude, Gemini, etc.) are all supported, since we interact with your agent as a black box.
No. Veris interacts with your agent through its external interface. Your model weights, prompts, and proprietary data stay entirely within your control. But you can share your data for us to improve the agent or its underlying model or to steer the simulation as you need.
Yes absolutely. You can define custom evaluation rubrics, safety policies, and success criteria that reflect your specific business requirements and compliance needs. We turn them into scenarios and graders.
Yes. Veris turns the regulations and standards you answer to, such as ISO, SOC 2, or the EU AI Act, along with your internal policies and handbooks, into concrete simulation scenarios paired with verifiers. Your AI system runs against them at scale, coverage maps back to the clauses, and gaps surface as failed runs with evidence, so you can close them and show compliance.
Yes. Veris supports deployment on your own VPC (AWS, GCP, Azure), on-prem infrastructure, or on Veris Cloud, giving you full control over where your data lives.
For more information, contact hello@veris.ai