Hours of manual work, finished in minutes
Drafted reports, pre-reviewed documents, correct audit days, validated scope coverage — that's what Certiva delivers. AI is how it's done, not why it exists. Every feature solves a real operational problem. No theater.
A multi-hour report becomes a reviewed draft in minutes
A multi-stage AI pipeline — evidence extraction, drafting, validation, assembly — ingests the audit's documents and writes a complete Stage 1 or Stage 2 report, filled into your CB's own report template. A coordinate-based assembly step places findings into the exact template cells and ticks conclusion checkboxes, across all eight standards including integrated audits. Safety checks block delivery on leaked names or unfilled placeholders. Runs in the background; every output is reviewable and editable.
Scenario
Your lead auditor just finished a Stage 2 for an integrated ISO 9001 + 14001 + 45001 audit. Instead of spending four hours writing the report from scratch, they review an AI-generated draft that's already in your template, with findings placed in the correct cells.
Without it: Auditors spend hours per report, quality drifts between people.
Supported standards: All 8 standards including integrated audits
Catch the problems your accreditation body would catch — before they do
Submit a completed report and the AI checks it against a structured accreditation-body rule profile (e.g. UAF / TÜRKAK) — nonconformity classification, mandatory clause coverage, finding-depth minimums — plus the standard's required clauses. Returns typed findings (critical / major / minor / warning) as real inline comments in the Word document. The report is reviewed before it ever reaches the accreditation body.
Scenario
A new auditor submits their first Stage 2 report. Before you send it to the reviewer, the AI flags that two mandatory clauses have no findings, one NC is classified incorrectly, and three findings lack sufficient depth. Those are exactly the problems your AB would raise at a witness audit.
Without it: Report weaknesses are found at the witness audit, as nonconformities against the CB.
Correct clause exclusions in seconds, not 30–60 minutes
When a client submits an application — say, a cloud services company with 30 employees — the AI analyzes the scope description and identifies which standard clauses are not applicable. The auditor sees a pre-populated exclusion list on the audit plan: review, adjust, confirm. AI suggestions are constrained to a system-approved candidate list and re-filtered in code, so it can only propose legitimately-excludable clauses. Suggestions only; the planner confirms.
Scenario
A cloud services company applies for ISO 27001. The AI identifies that physical manufacturing clauses don't apply, pre-populates the exclusion list, and the planner confirms in under a minute.
Without it: Planners spend 30–60 minutes per application cross-referencing clause applicability manually.
Supported standards: ISO 9001, ISO 14001, ISO 45001, ISO 27001, ISO 22000, ISO 13485
Audit days your accreditation body will accept, calculated in seconds
A deterministic engine implementing IAF MD 5 / ISO 22003-1 / IAF MD 11 tables — employee-count bands, ISO 27001 √-method, EnMS K-factor, site additions, multi-standard integration reductions (with MD 11 50% floor), 20% reporting deduction, IAF rounding. AI reads the application form into structured numbers; the math itself is deterministic and auditable. Planners can override with mandatory justification.
Without it: Hand-calculating from MD 5 tables — slow, inconsistent, frequent source of accreditation findings.
No coverage gaps — the system catches what you'd miss
Each auditor has a competence profile per standard (EA codes, scope categories, accreditation bodies, technical depth, witness records). When planning a stage, the system checks availability with a real date-overlap query and computes scope coverage — matching team EA codes against everything the audit requires, flagging exactly what's uncovered. This is deterministic rule-checking, not guesswork. AI is used only to read auditor CVs and extract qualifications.
Scenario
You assign a two-person team to a Stage 2 covering EA codes 17 and 28. The system flags: "EA code 28 (Chemicals) not covered by any team member — add a qualified TE before scheduling Stage 2." You catch the gap before the accreditation body does.
Without it: A coverage gap or double-booking is caught by the accreditation body, not by you.
Auditor qualifications extracted from CVs — no manual data entry
Upload an auditor's CV and the AI extracts their qualifications — EA codes, scope categories, standards experience, witness records, language proficiencies. The competence profile populates automatically, ready for the planner to review and confirm. No one types EA codes into a spreadsheet row by row.
Scenario
A new technical expert joins your roster. Instead of manually entering 14 EA codes, 3 standards, and 8 years of witness history, you upload their CV. The AI extracts everything into a structured profile in seconds. The planner reviews and confirms.
Without it: Manual data entry for every auditor — slow, error-prone, and nobody enjoys it.
AI built for auditors, not for demos
Every AI feature works on real certification data, and every output is reviewable and editable by your team. The AI does the heavy lifting — you keep the judgment.
Book a Demo to See the AI in Action