AI record infrastructure
The missing audit trail for production AI.
Observability tracks health. Provider logs track calls. Hashirai tracks the decision path.
AI decisions increasingly happen across models, tools, agents, and external systems. Once workflows cross those boundaries, audit trails fragment and no single party holds a complete account. Hashirai provides an independent, verifiable system of record for what happened and why.
Immutable Proof
SHA-256: 8f92b3…ac84e
anchored_at= 2026-04-17T11:23:16Z
Built for enterprise and regulated AI workflows
Works alongside observability and orchestration tools
Supports pilots, production rollouts, and security reviews
Designed for cross-provider and agent-based systems
Hashirai is the black box for AI.
As AI systems move into real workflows, organisations need a verifiable record of what happened, why it happened, and how decisions were made.
The problem
No existing log gives you the full record of AI activity.
In production AI workflows, evidence is split across provider consoles, app logs, tracing tools, and workflow engines. Each source is useful, but none captures the full chain of decisions and actions across systems.
Fragmented by design
Provider logs, app logs, and traces serve different purposes. They rarely share one timeline, identifier model, or record structure, so critical context is lost between systems.
Cross-system workflows break continuity
Once a workflow crosses models, tools, agents, vendors, or partner systems, the record becomes divided. No single party can show an end-to-end account of what happened and why.
Monitoring is not a defensible record
Internal and vendor-native logs help with debugging and operations, but they are not an independent record. When incidents, audits, or disputes arise, teams struggle to produce evidence that is complete, consistent, and defensible.
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.
Hashirai helps teams capture and reconstruct AI activity across models, tools, and workflows, so investigations, audits, and executive reviews start from evidence instead of fragmented logs.
Unified activity record
Capture prompts, outputs, tool calls, and workflow events in one connected record structure.
One chain across application, model, and workflow layers.
Cross-system traceability
Follow a single decision path across providers, internal services, and external systems without losing continuity.
Preserve step-to-step linkage across boundaries.
Decision context
Record which rules, checks, and review states were active when actions occurred.
Keep the why attached to the what.
Investigation-ready timelines
Reconstruct incidents quickly with ordered records that show what happened, when, and in what sequence.
Reduce manual stitching during incident response.
Defensible evidence exports
Generate structured records for audit, legal, and compliance review with integrity metadata intact.
Evidence that can be reviewed beyond engineering teams.
The Hashirai Protocol
How Hashirai turns cross-system AI activity into a structured, verifiable record designed to survive scrutiny.
01
Capture events
Capture AI events wherever they occur: model calls, retrieval steps, tool usage, agent delegation, and downstream actions across providers and services.
02
Create record
Link events from different providers, services, and workflow stages into one continuous record with shared identifiers, ordering, and context.
03
Verify actions
Apply integrity metadata, cryptographic signatures, and optional anchoring so the record is independently checkable and resistant to undetected changes.
04
Prepare evidence
Export ordered, structured evidence for audits, investigations, executive reviews, and regulatory questions without rebuilding the timeline from scratch.
Infrastructure for the modern enterprise.
Four buyer contexts where traceability, defensible records, review readiness, and cross-system accountability matter most.
OPERATING CONTEXT · PLATFORM
Platform teams
Add accountable AI capture without replacing your stack. Hashirai fits alongside orchestration, observability, internal tooling, and existing deployment patterns, making it easier to add traceability, verification, and review-ready records to workflows already running in production.
INTEGRATION SCALE
Operating context
AI usage is ahead of infrastructure readiness.
Most developers already use AI in their work.
Source · Postman, State of the API 2025
Built to fit existing AI stacks.
Add verifiable traceability to the systems you already run. Keep your models, vendors, and orchestration layers, and add a review-ready record of AI activity across your stack.
- SDK- and API-first integration paths
- Works alongside your observability and tracing tools
- Designed for typed, review-ready implementations
Example capture
Active governance engine
System of record • live
Autonomous agents demand accountability.
Agents introduce delegation, non-determinism, and long-running workflows across tools and systems. Hashirai makes those workflows reviewable by preserving a verifiable record of what each agent did, what it used, and what happened next.
- Every step attributable to an agent, tool, and decision path
- Investigations that do not depend on reconstructed chat logs
- Cross-model reporting for mixed agent environments
Pricing that scales with trust, risk, and usage.
Hashirai pricing is based on AI event volume, workflow criticality, retention, and deployment requirements. Start with one high-value workflow, then expand across production systems and enterprise environments.
Bring accountability to AI systems.
Deploy AI governance with verifiable records, disciplined capture, and audit-ready reporting without slowing innovation.
Hashirai supports enterprise evaluations, pilots, and regulated production deployments.
Support stream
FAQ
What makes Hashirai different from observability tools?
Observability tools help teams monitor performance and reliability. Hashirai creates a verifiable record of what an AI system did, why it did it, and how that action moved across models, tools, agents, and workflows.
Does Hashirai replace our logging stack?
No. Hashirai sits alongside your existing stack. You can keep your current observability, tracing, orchestration, and internal logging tools while adding a clearer system of record for AI activity.
How is Hashirai different from provider logs or model-native dashboards?
Provider logs and model-native dashboards can show what happened inside their own system. Hashirai is designed for workflows that cross models, tools, agents, vendors, and internal services. It creates one consistent record across those boundaries, rather than leaving teams to piece together fragments from multiple systems.
How do you handle multi-model and multi-vendor environments?
Hashirai is built for mixed AI environments. It can capture activity across different models, providers, tools, and agent frameworks while keeping one consistent record structure.
Is this suitable for regulated enterprises?
Yes. Hashirai is designed for environments where evidence, retention, reviewability, and defensible records are required. It is particularly relevant where AI activity may later need to be reviewed by risk, compliance, legal, audit, or external stakeholders.
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 beginning with one production-critical path, validating the record model, and then extending coverage across systems, teams, and environments without replacing your current stack.
What exactly gets recorded?
Hashirai records the context needed to reconstruct 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.
Who typically uses Hashirai inside an organisation?
Hashirai is relevant to teams that need to review, investigate, explain, or defend AI-driven activity. That often includes engineering, platform, security, risk, compliance, legal, internal audit, and operations teams, depending on the workflow and deployment environment.
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.”
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