Proof for every
agent action.
Track agent behavior, detect behavioral drift, and produce verifiable proofs—automatically and at scale using cryptographic guardrails.
SAAKSHI in six stages: each agent carries a signed identity; actions route through a proxy policy gate to LLMs and tools; every step is recorded in order; records are hashed, signed and sealed — no edits, no backdating; sealed records link into a tamper-evident evidence chain; selective-disclosure proofs make it audit-ready, automatically and at scale.
Beyond Trust. Built on Proof.
Passport
A signed immutable record that binds an LLM configuration to its institutional owners.
Agent Activity
Continuous visibility into every agent action, LLM interaction, model usage, token consumption and per-action cost across your AI ecosystem.
Immutable Evidence Chain
Every agent action is cryptographically hashed, signed and linked into an append-only evidence structure for tamper-evident integrity.
Semantic Search
Search millions of agent interactions in seconds using meaning, intent and context — not just keywords — surfacing the most relevant actions, conversations and evidence across your entire agent ecosystem.
Zero-Knowledge Proofs
Verify the integrity of AI interactions without revealing prompts, responses, or sensitive data.
Behavioral Drift Detection
Detect changes in AI behavior continuously before they become operational or compliance risks.
No model crosses without a Passport
A signed, immutable record that binds an LLM configuration to its institutional owners. Every call passes through it, and every call comes back carrying a signature anyone can verify.
- Every model call crosses the same checkpoint — no exceptions
- Owners are named on the record, not in a side agreement
- Immutable once issued: it cannot be edited unnoticed
- Comes back signed, and anyone can verify it
No passport, no crossing — every call, every time.
Every model call — ChatGPT, Anthropic, Claude, Meta— crosses the agent’s passport and leaves with a signed PassID bound to its institutional owner.
Every agent has an identity. Every action leaves proof
Open an agent’s passport and the same two things are on the record: who it is and who owns it, and every task it was given — each one carrying its own verification.
- Issued once, with the owner and the co-signer named on it
- Every task it is given is written to the same record
- Each completed task carries its own verification
- Append-only — nothing is edited after the fact
One passport. Every task, start to finish.
Agent passport AGT-7X9F-K2L8, Analytics Agent, owned by Acme Corp and validated by Acme Risk Office: verified. Three tasks assigned — generate sales report, analyze customer feedback, prepare insights summary — each completed and carrying its own verification, on evidence chain 8f3a…c91e.
Search millions of calls by meaning
Every captured prompt and response is embedded, so the data itself is the index — describe what happened and the corpus answers, no keywords to remember.
2,41,022 recorded calls in the corpus
- Exact-keyword matches
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- Semantic matches
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Type above, or pick a suggestion — the closest recorded call appears here.
TSK-······Open in Task SearchRanked by similarity, not keywords — and every result opens its inclusion proof.
Behavioral Drift Detection
Continuous drift detection
Catch behavioural and semantic drift before it becomes an incident. Agent output is measured against its approved operating envelope continuously, without slowing the traffic it watches.
- Alerts fire before drift becomes a compliance problem
- Every response measured against its approved envelope
- Runs in real time, off the live application thread
- Compared in a privacy-preserving form, never raw content
Know the moment an agent stops behaving like itself.
- Watch — support-copilot — Drifting from baseline — cosine distance 0.19
- Watch — support-copilot — Nearing safe bound — cosine distance 0.28
- Drift — support-copilot — Drift beyond bounds — cosine distance 0.42
- Normal — support-copilot — Baseline re-anchored — cosine distance 0.11
- Normal — onboarding-agent — Within safe bounds — cosine distance 0.04
- Normal — ops-assistant — Compared against envelope — cosine distance 0.06
- Normal — docs-agent — Within safe bounds — cosine distance 0.05
- Normal — onboarding-agent — Compared against envelope — cosine distance 0.03
Priced for every stage of proof
Every deployment is scoped differently — the volume you run, the evidence you keep, the guarantees you need. Tell us what you're running and we'll price it with you.
- Scoped to your agent volume, not a seat count
- Evaluation pilots supported from day one
- Onboarding, support and SLAs agreed up front
Got questions? We’ve got answers
The plain questions teams ask before their first proof — what we keep, what it costs you in latency, and how fast you can be running.
SAAKSHI is an accountability layer for AI agents. Every time one of your agents does something — answers a customer, scores a risk, approves a payment — SAAKSHI records a cryptographic receipt for it. Months later you can prove exactly what happened, without anyone having to take your word for it.
Still have questions?
Talk to the team that built it — we usually reply the same day.