Provable behavior for AI agents
From probabilistic AI to provable AI.
ByteVerity is the flight recorder and proof layer for agentic systems. Neural systems read the world; symbolic gates decide; every action becomes a signed receipt a skeptic can verify offline.
Public receipts
We do not ask buyers to trust a claim. We hand them the trail.
ByteVerity is built for skeptical review. The tau2 submissions are public draft PRs, and the banking run is explicitly omission-aware while challenged benchmark tasks are under review. The evidence trail is visible, reproducible, and precise about what has and has not been accepted upstream.
Last checked July 7, 2026 (UTC)
Airline
Custom neuro-symbolic text submission.
ByteVerity submitted
pass^1
avg cost
50 base tasks x 4 trials
1.0 point higher pass^1 and about 9.2x lower agent-side cost than the accepted upstream leader.
Draft custom submission; not yet merged into the accepted public leaderboard.
Banking knowledge
Omission-aware run while 17 task issues were under maintainer review.
ByteVerity submitted
pass^1
avg cost
80 reviewed tasks x 4 trials
About 1.76x pass^1 and about 43x lower agent-side cost than the accepted upstream leader.
Omission-aware draft on 80 of 97 banking tasks; not a full standard accepted row.
The trust boundary
Neural to read it. Symbolic to prove it.
ByteVerity keeps the model out of the part you have to trust. The model proposes. A deterministic boundary checks the action against evidence and policy. The result becomes a receipt a skeptical buyer, regulator, or incident team can verify without calling ByteVerity.
Neural reads
The model interprets messy language, policy documents, tickets, contracts, and tool context.
Symbolic decides
A deterministic gate checks the proposed action against policy, evidence, identity, and observed state.
Receipt seals
The result is hash-chained, signed, and handed to a verifier that can run with the engine absent.
Operating model
Block. Record. Rewind. One proof substrate underneath.
ByteVerity is not a loose collection of dashboards. Every product speaks the same receipt language, so one adoption path can expand across prevention, evidence, incident response, and posture.
Stop forbidden tool calls, data releases, and policy violations before they execute.
Control, Browser Runtime, Data Gateway
Capture the causal chain across code, run, knowledge, policy, approval, data, and drift.
Lineage, Kernel, MCP Gateway
Pinpoint the poisoned document, stale memory, or exact action that changed the outcome.
Bisect, Bisect-Data, Atlas
Live proof
The verifier is the product demo.
One clean record. One tampered record. One client-side check. Press the tamper control and the proof flips red, names the break, and shows zero network requests from the verification step.
- record
- refund-incident-1947
- root
- sha256:54bc0655abbf…
- planes
- approval code conformance data knowledge policy run (7)
- signer
- ed25519 · keyid d2f381d7b0b5…
- runstep 9 — refunded an already-delivered orderproven
- knowledgeretrieved doc-1 — a stale refund-policy docproven
- policyrefund-limit rule evaluated → allowproven
- data→causesuspected-cause (uncounted)asserted · uncounted
Verified offline. 3 proven planes, 1 asserted and uncounted. 0 network requests.
This panel runs a verifier client-side. Press Tamper one byte — the proof flips to RED, names the break, and the network counter stays at 0.
Where the forgotten case costs money
Designed for decisions where probably right is still a liability.
The highest-value use cases are not generic chat workflows. They are regulated, policy-heavy decisions where a missed edge case becomes a customer harm, an audit finding, or a board-level incident.
The coverage rule that denies the sicker patient
Published payer medical policies encoded as contracts, then checked for ambiguity and cross-policy divergence.
A fraud card left live by a green answer
The verifier flags when a reference trajectory secures only one card while another confirmed-fraud card remains active.
The stale document that caused the refund
Causal rewind lands on the exact stale policy read and produces a proof the incident team can re-check offline.
About ByteVerity
Built by a quiet bench of senior data scientists, advised by operators who have governed at scale.
ByteVerity is founder-led by Mohit Mahajan and intentionally proof-led. The core work is done by a small, senior team of data scientists, research engineers, and systems builders who prefer receipts to resumes. Their work is reinforced by industry mentors with deep experience across data infrastructure, healthtech, search, enterprise security, and go-to-market.
Across the US, India, and EU.
Operators, CTOs, product leaders, and investors.
Proof a skeptical buyer can verify offline.
Compliance by construction
Evidence is produced at the moment of action, not assembled after the incident.
Every product emits the same kind of receipt, so compliance output is a rendering of operating evidence, not a dashboard narrative written later.
EU AI Act
Art. 12 (record-keeping / traceability), Art. 9, 10, 11, 14. Lineage renders the Art. 12 pack straight from the signed record.
AI-BOM
CycloneDX 1.6 ML-BOM + SPDX 3.0 AI Profile, derived from the record.
NIST
SP 800-207 (Zero Trust) and SP 800-218A (Secure SDLC for AI).
SOC 2
CC-6.1, CC-7.1, CC-8.1.
GDPR
Art. 25, 32, 44–49.
Start here
Stop shipping agents you cannot stand behind.
Run one governed workflow, seal every action, tamper the bundle, and watch verification name the break. Leave with the reliability number and the receipts to defend it.