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.
Three things, spanning the AI lifecycle:
Testing and compliance: 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.
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.
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. No rebuild, no SDK to adopt, no lock-in.
Immediately. Coding agents can spin up a Veris dependency sandbox in seconds. For testing agents and other AI systems, start from just a phone number or an API endpoint, or add a code integration for deeper simulation. And as a full environment sandbox, Veris deploys into your VPC or on Veris Cloud within a day.
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, use Test and Compliance in the Production Twin. To keep an agent improving before and after launch, use Training and Loops. To let coding agents verify their own work against stateful dependencies before the PR opens, use Agentic SDLC. All three run on the same simulation platform, so you can start with one and add the others.
Pricing is usage-based and depends on the volume of simulations and the complexity of your AI system's environment. Contact us for details.
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 AI replaces human in the loop and static golden datasets; simulation is the best place to test and train AI systems. Veris runs thousands of parallel simulations 24/7 and instantly evaluates more sessions than a human team can review in a week, 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 the Production Twin to evaluate third-party AI against their own operating conditions before signing. Product teams battle-test and train customer-facing agents on complex workflows such as scheduling, sourcing, and support. Engineering teams give their coding agents stateful dependency sandboxes so integrations are verified before the PR opens. Email us at hello@veris.ai for case studies 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. We provide a suite of mocked enterprise tools (e.g. Email, Slack, Jira, Postgres, etc.) that maintain state throughout the session. If your agent deletes a database row, it stays deleted for the rest of that session. This statefulness is critical for testing multi-step reasoning.
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.
RL requires state, action, and reward; Veris provides the environment to generate all three. We output structured traces compatible with RLAIF, RLHF, and Reinforcement Fine-Tuning pipelines, and you can generate thousands of rollouts, score them programmatically using Veris verifiers, and fine-tune open-source models via PPO, DPO, GRPO, or similar methods.
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 is designed to be the "integration test" for agents. You can trigger simulation runs on every pull request over MCP, the CLI, or the API, and block the merge if the success rate drops below your defined threshold, preventing regression before it reaches production.
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