Enterprise AI governance
Govern AI with proof, not trust.
Hashirai gives enterprises immutable, traceable records of AI activity across prompts, agents, tools, models, and outputs. Create an independent system of record for production AI, compliance, audit, and operational accountability.
Immutable Proof
SHA-256: 8f92b3…ac84e
anchored_at= 2026-04-06T05:11:11Z
Built for enterprise AI operations
Designed for governance, compliance, and audit
Works across models, tools, and agent workflows
The problem
Traditional logs are fragmented and insufficient.
Current logging systems capture related events, but fail to provide a complete, independent, and verifiable record of how modern AI systems behave in production.
Fragmented internal logs
Provider logs, app logs, and workflow traces only show one part of the picture. They do not create a unified audit trail across the full AI lifecycle.
No independent proof
A system cannot act as a neutral trust layer if it controls the record. When decisions are challenged, internal logs are often incomplete, mutable, or hard to defend.
The visibility gap
As AI agents take actions across tools and systems, organisations lose end-to-end visibility into what the AI saw, generated, decided, and did.
Observability shows what AI did. Hashirai proves it.
SYSTEM_VIEW_RECONCILIATION
agent_id
'ag_underwrite_01'
action
'model_completion'
policy
'lending_v3'
request_id
'req_9f3a2b71'
timestamp
'Apr 2, 2026 14:11:03'
sig
'sig_ed25519_A1b…K9q'
- request_id
- req_9f3a2b71
- model
- claude-sonnet-4
- status
- success
- timestamp
- Apr 2, 2026 14:11:02
Cannot verify full event history
Built for the highest level of scrutiny.
Enterprise-grade tools to capture, verify, and report every AI decision with forensic precision.
End-to-end AI provenance
Create a complete record of AI prompts, outputs, actions, and decisions across models, workflows, and environments.
One coherent audit trail across prompts, tools, and outputs.
Traceable agent activity
Track autonomous agent behaviour across tools and systems, including tool selection, parameter usage, and execution history.
Structured context from first prompt to final outcome.
Audit-ready history
Generate independently verifiable records for investigations, incident review, compliance workflows, and internal governance.
Export timelines without reconstructing logs by hand.
Governance controls
Define and enforce policies around how AI systems operate, what actions they can take, and how evidence is recorded.
Approvals, denials, and exceptions—explained with evidence.
Defensible reporting
Give security, risk, legal, and compliance teams the evidence they need to explain and defend AI-driven activity.
Committee-ready narratives with consistent IDs and signatures.
Cross-provider visibility
Capture AI activity across multiple models, vendors, and orchestration layers without relying on a single provider's logs.
No blind spots across models, agents, and policy services.
The Hashirai Protocol
A rigorous trust architecture for AI accountability.
01
Capture events
Record AI inputs, outputs, tool calls, and actions through the SDK or API as they happen inside real production workflows.
02
Create record
Generate a deterministic cryptographic record for every event, creating a secure chain of custody for AI activity.
03
Verify actions
Store full data securely and maintain a tamper-evident reference that authorised parties can independently verify.
04
Support audit
Use verifiable evidence for compliance reviews, incident investigations, disputes, model governance, and regulatory reporting.
Infrastructure for the modern enterprise.
Hashirai meets teams where AI risk actually shows up—production workflows, regulated environments, and fast-moving agent platforms.
OPERATING CONTEXT · ENTERPRISE AI
Enterprise AI teams
Govern live AI workflows with evidence you can actually use, from model actions and agent decisions to cross-system investigations, escalation paths, and leadership reporting. Hashirai gives teams a clear operational record across the systems, agents, and models they already run in production.
OPERATIONAL COVERAGE
Operating context
78%
AI USE
71%
GEN AI
~33%
SCALE
25%
AGENTS
Built to fit existing AI stacks.
Drop-in capture for the systems you already run. Keep your models, vendors, and orchestration—add an accountability layer that speaks audit and engineering fluently.
- SDK and API-first integration paths
- Works alongside your observability toolchain
- Designed for typed, review-friendly implementations
Example capture
Active governance engine
System of record • live
Autonomous agents demand accountability.
Agents introduce non-determinism, delegation, and emergent behavior. Hashirai makes AI agent governance operational—grounding autonomy in verifiable records and AI policy controls.
- Every step attributable to an agent, tool, and policy outcome
- Investigations that don’t depend on reconstructed chat logs
- Cross-model reporting for heterogeneous agent fleets
From signal to defensible governance.
Bridge monitoring, audit, and compliance with one coherent model of AI accountability.
Audit and traceability
- End-to-end AI audit trail for workflows and agents
- Immutable AI records with verifiable fingerprints
- Exportable timelines for investigations
Monitoring and control
- AI observability aligned to governance objects
- Structured context for policy outcomes
- Operational visibility without log archaeology
Governance and compliance
- Enterprise AI compliance-ready evidence packages
- Policy enforcement with proof of evaluation
- Cross-model reporting for risk review
Pricing that scales with trust, risk, and usage.
Hashirai pricing is tailored to AI event volume, workflow criticality, retention, and deployment requirements. Start with a focused pilot, then expand across production workflows and enterprise environments. Hashirai is priced around the scale and criticality of your AI operations, not by user seats. Pricing typically reflects event volume, retention requirements, environment count, governance needs, and deployment complexity.
Pilot
For teams validating auditability and traceability in a live AI workflow.
- Ideal for a focused production use case
- Core event capture and verification
- SDK or API integration
- Standard retention
Best for early production workflows and design partners
Production
For teams running AI in live business workflows and expanding governance coverage.
- Broader workflow and environment coverage
- Higher event volumes
- Extended retention options
- Audit and investigation support
Best for scaling production AI operations
Enterprise
For regulated, high-volume, or high-risk deployments requiring deeper controls and support.
- Advanced governance and compliance requirements
- Custom retention and deployment needs
- Security and procurement support
- Multi-team or multi-environment rollouts
Best for enterprise-wide AI accountability
Bring accountability to AI systems.
Deploy AI governance with cryptographic proof, disciplined capture, and audit-ready reporting—without slowing innovation.
Support stream
FAQ
What makes Hashirai different from observability tools?
Observability tools are built to monitor performance, reliability, and system health. Hashirai is built to create a verifiable record of what an AI system actually did, why it did it, and how that action moved across models, tools, agents, and workflows.
Hashirai complements your logging and observability stack. It adds structured provenance, policy context, record integrity, and audit-ready history where conventional logs stop short.
Does Hashirai replace our logging stack?
No. Hashirai is designed to sit alongside your existing stack, not replace it.
You can keep your current observability, tracing, orchestration, and internal logging tools. Hashirai adds an accountability layer on top, giving you a clearer system of record for AI activity across production workflows, without forcing you to rebuild your infrastructure.
How do you handle multi-model and multi-vendor environments?
Hashirai is built for heterogeneous AI environments. It can capture activity across different models, providers, tools, and agent frameworks while preserving one consistent record structure.
That means teams can investigate, govern, and report across mixed environments without manually stitching together fragmented provider logs, app events, and workflow traces.
Is this suitable for regulated enterprises?
Yes. Hashirai is designed for environments where evidence, retention, reviewability, and operational control matter.
It helps regulated teams move from partial internal logs to structured, defensible records that support investigations, governance processes, and compliance reporting. The goal is not just more visibility, but a record that can stand up to internal review and external scrutiny.
How hard is Hashirai to integrate?
Hashirai is designed to be added incrementally. Teams can start with a focused workflow, integrate via SDK or API, and expand from there.
In practice, that means you can begin by capturing one production-critical path, validate the governance model, and then extend coverage across systems, teams, and environments without replacing your current stack.
What exactly gets recorded?
Hashirai records the context needed to understand and defend an AI-driven action. Depending on the workflow, that can include identifiers, policy state, model and agent actions, tool usage, review state, timestamps, and cryptographic record metadata.
The aim is to preserve a coherent, verifiable chain of evidence, not just isolated events.
Can Hashirai support agent workflows, not just single model calls?
Yes. Hashirai is especially useful where actions span multiple steps, tools, models, or delegated agents.
Instead of treating each event as an isolated log line, it helps teams capture the full operational path, so they can see what was triggered, what decisions were made, what tools were used, and how the workflow progressed over time.
Why does provenance matter if the model output already looks correct?
Because correctness is only part of the problem. In production, teams also need to understand how an output was produced, what policies applied, what inputs and tools were involved, and whether the action can be explained later.
Hashirai helps teams move from “the system seems to work” to “we can prove what happened.”