Fact0
Tamper-evident audit trails for AI agents and their actions.
Tagline
Prove every AI action.
The evidence layer for AI agents.
Pass AI security review in days.
Immutable logs for agent accountability.
The evidence layer for AI agents, not just another observability tool.
The page repeatedly emphasizes proof, chain integrity, signed exports, and security review readiness rather than generic monitoring, making 'evidence layer' the cleanest category claim.
The alternative to stitching together Datadog, CloudWatch, and custom logs for agent accountability.
Fact0 directly anticipates the 'we already use Datadog or CloudWatch' objection and offers agent-specific audit trails, replay, and compliance exports those tools do not natively provide.
The fastest way to pass AI security review in regulated workflows.
The landing page is built around procurement, SOC 2-ready reports, HIPAA audit controls, and enterprise questionnaire velocity, which makes time-to-approval a stronger pain-killer than pure debugging.
Primary user
Security or compliance lead at a company deploying AI agents into regulated workflows
ICP #1
GRC manager at a fintech using AI agents for loan decisions and payment ops
Pain
Procurement and security reviewers keep asking for proof of who approved, rejected, or overrode an automated action, and the team currently scrambles across logs and tickets to reconstruct it.
Why this solves
Fact0 gives them a tamper-evident event trail, signed exports, and control mapping so they can answer SOC 2 and customer security questions with evidence instead of narratives.
ICP #2
Staff backend engineer shipping an autonomous support copilot at a mid-market SaaS company
Pain
When the bot refunds, escalates, or edits customer records, support and engineering need a reliable replay of what happened without adding latency or invasive instrumentation.
Why this solves
Fact0’s async batching, execution DAG, replay/debug, and live feed let engineers trace every tool call and branch without slowing the agent loop.
ICP #3
Enterprise security architect evaluating AI agents for healthcare or HR workflows
Pain
They need to prove access control and auditability for sensitive data, but generic observability tools like Datadog or CloudWatch don’t provide evidence-grade, tamper-evident records.
Why this solves
Fact0 is built around actor identity, immutable logs, searchable evidence, and cryptographically verifiable exports that map directly to the controls security teams ask for.
Strengths
- +The positioning is unusually sharp: it sells proof, not just logging, and that maps well to enterprise buying behavior.
- +The demo is concrete, with real-feeling data like support copilot executions, missing critical actions, and chain height metrics.
- +It connects engineering value and compliance value on the same page, which helps justify purchase across both technical and security stakeholders.
Weaknesses
- −The homepage overloads the visitor with compliance language before clearly explaining the integration model or exact implementation path.
- −It leans on abstract phrases like 'universal fact layer' and 'digital wax seal,' which sound clever but can obscure the practical value.
- −There is too much emphasis on the dashboard and too little on how a team actually instruments Fact0 in their codebase.
- −The product seems strongest for regulated buyers, but the page does not clearly separate that ICP from broader AI engineering teams.
- −The feature list is good, but the launch story is missing a crisp before-and-after narrative around one common failure scenario.
Fix these
- Add a blunt integration section showing the exact SDKs, events, and 10-minute setup flow for Python, LangChain, or LlamaIndex.
- Replace some abstract language with one concrete workflow: an agent refunds a customer, Fact0 logs it, security exports the evidence pack, auditor verifies it.
- Create ICP-specific landing page variants for fintech, healthcare, and support ops with the exact controls and evidence those teams need.
- Show a side-by-side comparison versus Datadog, CloudWatch, and LangSmith focused on auditability, immutability, and exportable evidence.
- Make the 'missing critical actions' and 'chain breaks if history changes' concepts more prominent since they are the most differentiated proof points.
Drop-in replacement copy
Headline
Prove every AI action.
Tamper-evident audit trails for agents.
Immutable proof, not just logs
Fact0 records every agent action in an append-only fact layer with cryptographic verification. If someone changes history later, the chain breaks.
Replay failures without guesswork
See the execution DAG, inspect the last known good state, and replay agent runs at failure points. Debug faster without adding friction to the agent loop.
Evidence packs for security review
Export signed audit bundles for procurement, GRC, and compliance teams. Answer questions about who did what, when, and why with records they can verify.
Search across every agent action
Find runs by user, agent, document, outcome, or time range. Use Spotlight to ask natural-language questions over the audit trail and get to the answer fast.
FAQ
How is this different from Datadog or CloudWatch?
Those tools are great for observability. Fact0 is built for evidence: tamper-evident records, signed exports, and an audit trail designed for security reviews and compliance.
Does Fact0 slow down agent execution?
No. Fact0 is designed to capture events asynchronously so the agent loop stays fast. The audit and evidence layer happens without turning your workflow into a logging project.
What integrations do you support?
The goal is a simple SDK path for Python and common agent frameworks like LangChain and LlamaIndex. The first setup should be quick enough to instrument one workflow in minutes.
Can auditors verify the logs?
Yes. The chain is cryptographically signed with Ed25519, and exports include a digital seal so reviewers can verify integrity without trusting screenshots or manual summaries.
Who is this for first?
Teams shipping AI agents into regulated workflows: fintech, healthcare, HR, support ops, and internal tools where proof matters as much as uptime.
Built Fact0: tamper-evident audit trails for AI agents. Every tool call, every branch, every failure. Append-only logs, signed evidence exports, replay/debug views. For teams that need to answer security reviews with proof, not screenshots.
Datadog is great for monitoring. It is not an evidence layer for AI agents. Fact0 gives you immutable agent logs, cryptographic verification, and exportable proof packs for auditors, GRC, and security review.
Spent 3 weeks making agent logs hard to lie about. Ed25519 signatures. Append-only fact chain. Replay from failure points. If a history changes, the chain breaks. That is the point.
The hardest part of agent observability is not seeing events. It is proving they were not altered later. Fact0 was built for that: signed audit trails, evidence exports, and a chain that makes tampering obvious.
Security teams do not want another dashboard. They want to know who did what, when, what it touched, and whether it succeeded. If your agent touches customer data, you need evidence, not vibes.
One missing agent action can turn a 10-minute answer into a 3-day scramble. Fact0 keeps the full trail, searchable by user, agent, document, time, and outcome. So procurement stops blocking the rollout.
Agent refunds customer. Fact0 logs the actor, tool call, records touched, success/failure, and the full execution DAG. Later, security exports a signed evidence pack and verifies it in minutes.
When the support copilot fails halfway through a workflow, Fact0 shows the exact branch, state, and last good action. No guessing. No digging through three tools. Just replay, debug, and ship the fix.
If your AI agent touches payments, loans, patient data, or HR records, you already know the problem. Fact0 exists for the teams that need agent accountability before they can scale the workflow.
Engineers want replay and debugging. Security wants immutable evidence. GRC wants exportable controls. Fact0 does the annoying part all three care about, without slowing the agent loop.
Angle: evidence layer vs observability
Most AI observability tools answer one question: What happened? Regulated teams need a different question answered: Can you prove it happened, and that it was not changed later? That is why I built Fact0. It creates tamper-evident audit trails for AI agents: append-only logs, cryptographic verification, execution DAGs, replay/debug views, and signed evidence exports. This matters when an agent refunds a customer, edits a record, or touches sensitive data. Security reviews do not care that the dashboard looked fine. They care about actor identity, exact actions, outcomes, and evidence they can trust. Fact0 is the evidence layer for AI agents. Not just another logging tool. Not just another dashboard. If you are shipping agents into fintech, healthcare, HR, or support workflows, I’d love to hear what your audit trail problem looks like.
Angle: before-after security review
Before Fact0: - A support agent issues a refund - Engineering has logs in one place - Security has tickets in another - Procurement asks for proof - Everyone spends half a day reconstructing the story After Fact0: - Every agent action is logged - Every action is cryptographically signed - The trail is searchable by user, agent, document, and outcome - Evidence exports are ready for review The point is not just visibility. The point is being able to answer audit questions fast without slowing down the agent. That is the workflow I wanted to make boring. If you are the person getting pinged when compliance asks, I’m curious: what is the most painful question you have to answer today?
Angle: implementation clarity
A lot of AI infrastructure products stop at the demo. The real question is: how hard is it to instrument in a codebase people actually use? For Fact0, I wanted the answer to be boring: - capture agent actions asynchronously - keep the execution path visible - make replay and evidence export available when needed - avoid slowing the loop that the product team cares about I think teams buy this category faster when the setup is concrete, not mystical. So we’re pushing hard on SDKs, clear event models, and the exact integration path for Python, LangChain, and LlamaIndex. If you build agents for real workflows, I’d love feedback on what the first 10 minutes should look like.
No visuals for this kit yet.
Tagline
Tamper-evident audit trails for AI agents
Description
Log every AI agent action, verify it cryptographically, and export signed evidence packs for security reviews. Search, replay, and debug agent runs without slowing execution.
Maker's first comment
I built Fact0 because I kept seeing the same failure mode: the agent did something important, and nobody could reconstruct it cleanly when security, compliance, or support asked for proof. Existing logging tools were fine for visibility, but not for evidence. Fact0 is my attempt to make that boring. Every agent action is appended to a fact layer, signed, searchable, and exportable. If history changes, the chain breaks. If a workflow fails, you can replay it. If an auditor asks for proof, you can hand over a sealed evidence pack instead of a screenshot graveyard. I’d love feedback from people shipping agents in regulated workflows: what’s missing from the setup, the SDKs, or the evidence export flow? That’s where I want to spend the next iteration cycle.
Pinned maker comment
Looking for feedback on two things: 1) whether the integration path feels obvious for Python/LangChain/LlamaIndex, and 2) whether the evidence export is actually the format security teams want.
Meta
Your AI agent touched customer data.
Hypothesis: regulated teams will pay for proof, not just observability. Fact0 logs every agent action, signs the trail, and exports evidence packs for security reviews. Built for fintech, healthcare, HR, and support automation.
Google Search
AI agent audit trail software
Hypothesis: teams searching for audit logging for AI agents want something evidence-grade, not generic logs. Fact0 provides append-only audit trails, cryptographic verification, replay/debug, and signed exports for compliance and procurement.
Reddit Promoted
If your bot can refund money
Hypothesis: builders in regulated workflows are tired of stitching together Datadog, CloudWatch, and custom logs. Fact0 gives you tamper-evident agent logs, execution DAGs, and evidence packs so you can answer audit questions fast.
Subreddits
r/SideProject
Show the exact before/after of an AI refund workflow and how the evidence pack answers audit questions.
Rules: Be transparent that it is your product. Focus on the build, the problem, and the technical details. No hard selling in comments.
r/indiehackers
Tell the story of building an evidence layer for AI agents after seeing security review pain firsthand.
Rules: Share metrics, lessons, and specifics. Avoid generic promo. People respond to concrete founder experience.
r/microsaas
Explain the niche: tamper-evident logs for AI agents in regulated workflows, and why narrow beats broad.
Rules: Keep it relevant to micro-SaaS builders. No spam. Real product feedback and positioning discussion only.
r/EntrepreneurRideAlong
Document the launch and early customer discovery around fintech, support ops, and compliance leads.
Rules: This is for build-in-public style updates. Be useful, honest, and specific about what you learned.
r/SaaS
Discuss selling into security, compliance, and engineering buyers with an infra product that needs trust.
Rules: Add value for SaaS operators. Keep the post educational and avoid pure self-promotion.
Communities
Post a build log about the first regulated customer workflow and ask for feedback on onboarding and positioning.
Launch with a technical angle: cryptographic audit trails for AI agents, plus a short explanation of the tamper-evident chain.
Time the launch for a Tuesday or Wednesday, then spend the day replying fast to every technical question.
Cold outreach template
Hey {firstName} — saw you’re working on {context}. If your AI agents ever touch sensitive data, Fact0 gives you tamper-evident audit trails, replay, and signed evidence exports. If you want, I can show you the exact workflow we use to answer security review questions in minutes. Worth a look?
Product Hunt timing
Launch Tuesday or Wednesday at 12:01 AM PST. That gives you the longest runway for UTC + US traffic, and a full day to respond to comments before attention drops.
Indie Hackers post ideas
- 01I built tamper-evident audit trails for AI agents because Datadog wasn't enough
- 02What regulated teams actually ask for when they want proof of AI actions
- 03How I’d instrument an agent workflow in 10 minutes without slowing the loop
Competitor alternatives
Current tone of voice
Confident, security-first, and slightly provocative. The page leads with “Your AI works. Can you prove it?” and “Pass enterprise security reviews in days, not months,” which sounds like a product built for skeptical buyers, not hobbyists.
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