Investigate every incident like your best expert.
It learns your plant with every case you close.
Describe what happened and attach the evidence. Intero asks the follow-up questions a seasoned investigator would, then builds a cited causal analysis with corrective actions traced to specific causes.
You get a review-ready investigation in minutes. Your team edits and signs it before anything is final, and every closed case sharpens Intero on your plant.
Intero is graded against public CSB investigations, so you can see how its findings hold up against the official record.
The system you have wasn't built to investigate.
Your EHS platform stores incidents, routes CAPAs, and holds the audit trail. That work matters. Recording an investigation is a different job from doing one, and the AI added to those platforms mostly summarizes the report that was already written.
Intero is a system of intelligence, not a system of record.
The discipline of a real investigation, built in.
Evidence first. Every finding cited. Corrective actions that trace to a specific cause. Intero applies the method each case calls for, and a qualified person reviews and signs before anything is final.
The AI in a records platform
Summarizes the report someone already wrote.
Intero
Investigates from the evidence. It asks the follow-up questions a seasoned investigator would and names the evidence no one collected.
The AI in a records platform
Restates the root cause that was entered.
Intero
Challenges a weak root cause and rejects boilerplate like “human error” before you ever see it.
The AI in a records platform
Files corrective actions in a register.
Intero
Traces every action to a specific cause, with an owner, a due date, and a verification method.
The AI in a records platform
Asks you to take its output on trust.
Intero
Can be graded. Re-investigate a closed case cold, side by side with your own report on the one incident you know best.
Five parts to every case.
Intero runs the same disciplined investigation on every incident, from evidence intake to a signed report. It works alongside the EHS system you already use, or captures the incident itself when you need it to.
The Case File
Plain-language intake. The incident account, photos, documents, and witness evidence stay attached to one case.
The Methods
The methodology library — ECFA, bow-tie, Tripod Beta, HFACS, CAST. Intero loads the method the incident calls for.
The Causal Chain
Mechanism, proximate, and systemic causes, with every finding cited back to the evidence.
The Quality Review
An independent pass challenges weak findings and rejects boilerplate before a person sees the draft.
The Signed Report
A qualified person edits, approves, and signs. Every CAPA carries an owner, a due date, and a verification method.
Judge it on your own incident.
Send us your last closed investigation report. Redacted is fine. Intero re-investigates it cold: the engine reads only the factual account and never sees your conclusions. Then you compare the two reports side by side.
You send a closed report
Any format, any system it came from. Redact names and identifiers. The engine works from roles, never names.
Intero re-investigates, cold
Your findings are withheld from the engine. It works from the evidence alone: a causal chain at three levels, every finding cited, CAPAs traced to specific causes. It also names the evidence that was never collected.
You compare, side by side
Your report next to Intero’s. Where they agree, where they differ, and what the record was missing. You judge it on the one incident you know best.
Your report
Root causes
Contributing factors
Corrective actions
Intero · cold re-investigation
Cited causal chain
CAPAs · traced to causes
Evidence gaps named
The side-by-side view as it appears in Intero. Your conclusions are shown for comparison only. The engine never reads them.
The only safety AI that can tell you how often it's right.
The U.S. Chemical Safety Board publishes its investigations in full. We run Intero on those same incidents, cold, and check its findings against the official root cause. The grade is public, and we keep it current as the set grows.
We frame every benchmark result as “re-derived the official finding,” never as a guarantee. The published scoreboard goes live once the current set is verified.
Every benchmark case is checked against the CSB's own published conclusion.
Intero learns your plant.
Every investigation makes Intero better at one thing: your operation. As your team closes cases and records what held, what failed, and what changed, that history becomes plant-specific context for the next investigation.
Point two identical tools at the same incident today and they may answer the same way. After one has worked through a year of your cases, it asks sharper questions about your equipment, your procedures, your recurring hazards, and your earlier corrective actions. That expertise accrues to your operation, one closed case at a time.
Your recurring hazards
Intero clusters what keeps happening across your sites, so prevention aims at the real repeat offenders.
Your people’s own words
How your crew describes a hazard becomes the evidence base, never flattened into rigid forms or dropdown codes.
Outcomes your team records
Whether an action held, failed, or saw the hazard recur becomes context for future investigations.
The investigation workflow is live and demoable today. Plant-specific expertise deepens as your team closes cases and records outcomes.
Test Intero on a closed caseIf you run HSE at an industrial company and this sounds like your problem, let's talk.
The AI proposes. Your team decides.
Intero drafts the investigation. A person reviews, edits, and signs every output before it is final. Nothing the AI produces stands on its own.
Intero proposes
It drafts the findings and CAPAs from the evidence, each one cited to the reporter’s own words.
Your team reviews
You edit or reject anything. A second AI pass has already thrown out boilerplate like “human error” before you see it.
A person signs
Nothing becomes final, and no CAPA is issued, until someone on your team signs off.
Your data never trains AI models
Every output cites evidence
Every action requires human approval
AI learning is scoped to your organization