Evaluate Sakshi

This is the shortest honest path from “curious” to “convinced” for a team evaluating Sakshi on a real use case. It governs one agent end to end, on your own instance, and ends with a signed evidence pack you can hand to a risk or audit function. Budget about thirty minutes. The whole point is that you do not have to take our word for anything. Every decision is recomputable, the evidence bundle is signed over its exact bytes, and Sakshi never sees a raw identifier.

1. Get a writable instance

You need an instance you can write to. There are two ways in.

Request a sandbox

A private, synthetic-data Sakshi, provisioned for you, no install. Writable, so you can register agents and witness decisions. The fastest way to start.

Self-host it

Single-tenant, in your VPC or on-prem. One host, one install. Evidence never leaves your environment. The path your security team will want to see.

The public demo at https://demo.rotavision.com is read-only, so use it to look around, not to run this walkthrough. Then mint an ingest key in the console under Admin → API keys and point the SDK at your instance. See Authentication.

2. Govern your first agent

Wrap one real decision. Register the agent, then witness a decision as it happens. Identifiers you pass are tokenized at ingest, so raw PII never lands on the chain.

from sakshi import SakshiClient

client = SakshiClient("https://sakshi.your-company.internal", api_key="sks_...")

agent_id = client.register(
    "loan-decision-agent",
    owner_name="Priya Sharma",
    owner_email="priya@your-company.com",
    autonomy_tier="L1",
)

with client.witness(agent_id, client_ref="APP-2026-04471") as decision:
    decision.step("retrieve", source="credit-bureau")
    decision.step("assess", risk_band="low", score=0.92)
    decision.action(outcome="approved", amount=1_500_000, mode="auto")

Already run agents on a framework? You do not rewrite them. Add one line of middleware for OpenAI, Anthropic, Gemini, Bedrock, LangGraph, or Google ADK, or govern tool calls over MCP. See Register an agent and Witness a decision.

3. Verify the chain yourself

This is the step that matters. Recompute the chain and confirm it holds, without trusting Rotavision or even your own operators.

curl -H "Authorization: Bearer sks_..." \
  https://sakshi.your-company.internal/api/v1/chain/verify

{ "valid": true, "checked": 1842, "first_invalid_seq": null }

Tamper with a single field in any record and valid flips to false, and the response tells you the sequence number where it broke. That is the trust model: evidence, not assertion. See The decision chain.

4. Bound the autonomy

Publish an autonomy envelope so consequential actions route to auto, human review, or block by stakes and confidence, and drill the kill switch. Enforcement fails closed: if Sakshi is unreachable, an ungoverned action does not slip through. See Bound autonomy.

from sakshi import SakshiBlocked, SakshiSyncReviewRequired

try:
    client.enforce(agent_id, "approve_loan", stakes=1_500_000, confidence=0.92)
except SakshiSyncReviewRequired:
    ...  # parked for a human before it can run
except SakshiBlocked:
    ...  # the envelope or the kill switch blocked it

5. Produce the regulator evidence

This is what a risk or audit function actually wants. Read your readiness across instruments, then export a signed evidence pack that maps your live records to the obligation, clause by clause.

curl -H "Authorization: Bearer sks_..." \
  https://sakshi.your-company.internal/api/v1/vidhi/readiness

curl -H "Authorization: Bearer sks_..." \
  "https://sakshi.your-company.internal/api/v1/vidhi/packs/rbi-mrm-draft-2026/export"

Packs cover RBI Model Risk Management, DPDP, SEBI, and IRDAI. Each is Ed25519-signed over its exact bytes, so it stands on its own once exported. See Evidence packs. For fairness, the declared-first screen flags a cohort only on a threshold breach that is also statistically significant, so it does not cry wolf. See Fairness screening.

What a design-partner engagement looks like

If the evaluation lands, the next step is a design partnership: we deploy alongside you, wire Sakshi to the agents you actually run, and shape the roadmap around what you hit first. It is deepest in BFSI, where the regulatory work is furthest along. For your risk and procurement teams, the design partner brief is a single PDF covering the regulatory mapping, the security posture, and the engagement.

Talk to us about a partnership

Ways in, from a free sandbox to a production engagement in your VPC.

Sakshi is single-tenant and runs in your environment. Customer data never leaves it, PII is tokenized before anything is stored or hashed, and every record is independently verifiable, even by an auditor who does not trust us.

Getting Started

Core Concepts

Predict & Prevent

Guides

1. Get a writable instance

Request a sandbox

Self-host it

2. Govern your first agent

3. Verify the chain yourself

4. Bound the autonomy

5. Produce the regulator evidence

What a design-partner engagement looks like

Talk to us about a partnership

​1. Get a writable instance

Request a sandbox

Self-host it

​2. Govern your first agent

​3. Verify the chain yourself

​4. Bound the autonomy

​5. Produce the regulator evidence

​What a design-partner engagement looks like

Talk to us about a partnership

1. Get a writable instance

2. Govern your first agent

3. Verify the chain yourself

4. Bound the autonomy

5. Produce the regulator evidence

What a design-partner engagement looks like