Proof-Based Design for the Agentic AI Era
Give agents a specification — not just a prompt.
Bevisera helps teams turn requirements, constraints, policies, and domain knowledge into machine-checkable specifications that humans and AI agents can share. Verification, tests, audits, and proofs become outputs of that foundation.
Intent + specification + evidence
The problem
AI makes specifications more important, not less.
Enterprise teams are adding agents to code, operations, and workflows, but intent still lives in prompts, documents, tickets, and reviewer memory. Bevisera starts earlier: make the meaning explicit enough for agents to use and humans to judge.
Prompts express intent, but they rarely preserve meaning as systems evolve.
Documents, tickets, and reviews leave critical knowledge scattered.
Human review becomes mechanical when reviewers must rediscover the specification each time.
How it works
Human-with-proof, not just human-in-the-loop.
The checker answers: does this satisfy the specification? The human answers the harder question: is this the right specification for the work, the risk, and the organization?
- 01Capture
requirements, domain concepts, assumptions, policies, and constraints.
- 02Specify
turn intent into agent-readable, machine-checkable specifications.
- 03Check
derive assertions, tests, proof obligations, and review evidence.
- 04Review
humans evaluate assumptions, scope, and consequences — not vibes.
Specifications are assets — once intent is explicit, teams can generate checks, evidence, traceability, and proof work from the same source of truth.
What becomes checkable
From intent to evidence.
Bevisera does not start by promising to prove every AI output. It starts by making the specification precise: what concepts mean, what assumptions are in force, what constraints matter, and what evidence a reviewer needs. From there, teams can derive tests, assertions, proof obligations, audit artifacts, and proofs where they are appropriate from the same semantic foundation.
Differentiation
From prompts to specifications.
- L1Prompt-only
intent lives in prose, examples, and memory.
- L2Documents and guardrails
knowledge is recorded or checked, but remains scattered.
- L3Specification infrastructure
meaning becomes machine-checkable and usable by agents.
Prompts express intent. Documents preserve fragments. Guardrails check selected behavior. Specifications express meaning in a form agents and tools can use.
Why Lean
A serious foundation for shared semantics.
We do not lead with Lean, but we do not hide it. Modern agents can increasingly read and write Lean, and its ecosystem gives Bevisera a precise substrate for machine-checkable specifications without asking customers to become proof engineers.
open-source, precise, and inspectable, with artifacts that can be checked independently. No vendor lock-in.
a candidate language for specifications, domain concepts, skills, knowledge representation, proof obligations, and proofs.
recent AI-reasoning work — including DeepMind's AlphaProof, Harmonic, and DeepSeek-Prover — builds on Lean.
Why now
AI raises the cost of implicit meaning.
The EU AI Act (Regulation 2024/1689) is one sign of the broader shift toward risk management, human oversight, technical documentation, and reviewable evidence for AI. Bevisera is not a substitute for compliance work; it helps teams build clearer machine-checkable artifacts for engineering review, audit, and oversight.
Industries
For enterprise software where meaning matters.
Bevisera starts where prompts and review are no longer enough: domain-heavy systems, regulated workflows, internal platforms, and AI-assisted development that needs durable specifications.
Pumps, valves, process plants.
Mobile platforms, manipulators.
Avionics, flight systems.
Grid, generation, storage.
Underwriting, compliance gates.
Decision support, triage.
Work with us
Building something that has to be right?
We're working with a small number of design partners. If your team is using agents where intent cannot stay trapped in prompts, let's talk.
Request early access.
early access by email
Tell us where agents touch requirements, code, or review, and where a machine-checkable specification would change the process. We'll follow up from a Bevisera address.
Please avoid sending system-confidential details by email. A secure intake flow will be added later.