01
Evidence work crowds out expertise
Experts spend their time locating and preparing evidence instead of judging exceptions and risk.
NirmatAI · AI-powered compliance review
Cut the cost of complex compliance reviews. Keep experts in control.
NirmatAI links documented evidence to each requirement, drafts findings and rationales, and flags gaps, so teams review faster and more consistently. Qualified reviewers make every final decision.
A 30-minute working session on your own standard. No evidence needs to leave your environment for a first demo.
Up to 90%less processing timeControlled scenarios
Up to 8×productivity potentialControlled scenarios
Every decisionmade by a qualified reviewerTraceable to source evidence
Documentary compliance review
Human-controlledRequirements linked to evidence
Requirement review
6.1 Risk planning7 evidence references
Ready7.2 Competence4 evidence references
Ready8.1 Operational controls11 evidence references
Review9.1 Evaluation3 evidence references
Review10.2 ImprovementNo supporting evidence found
GapDecision chain
RequirementWhat must be demonstrated
EvidenceRelevant source material
AI draftFinding + rationale
Human decisionAccept · edit · reject
The review challenge
More evidence. More complexity. The same expert capacity.
As requirements, evidence and review cycles grow, teams need more review capacity without matching increases in specialist effort.
02
Complexity erodes consistency
Multiple standards, entities, sites and evidence sets make consistent interpretation hard to sustain.
03
Expert capacity is costly to scale
Teams add specialist reviewers or external support just to prepare and repeat reviews.
Who it is for
Built for the people accountable for the answer.
NirmatAI serves accountable reviewers, risk owners and the teams that supply the evidence.
Chief Compliance, Risk and Quality Officers
More review capacity from the same team, with a defensible record behind each conclusion.
Compliance, GRC and internal assurance teams
Structured requirements, connected evidence and gaps visible before an external review.
Quality and management-system leads
Consistent interpretation across standards, sites and entities, with repeatable review runs.
IT, cybersecurity, engineering and operations
Fewer evidence requests and document hunts, clearer visibility of what is missing.
How it works
From requirement to decision, in four steps.
NirmatAI takes on the repetitive evidence work. Your reviewers focus on judgment.
01
Set the scope
Select the supported standard, framework or requirement set.
02
Add evidence
Upload policies, procedures, reports, records and other relevant material.
03
Assess and prepare
NirmatAI finds the relevant evidence, evaluates coverage and prepares traceable findings and reports.
04
Review and decide
The reviewer checks the evidence and rationale, then accepts, edits or rejects each draft finding.
Core rule: AI proposes. Qualified people decide. Every proposed conclusion stays linked to its evidence, so a reviewer can open the source instead of trusting an unexplained output.
Want to see this on your own standard?
A 30-minute working session on one requirement set, with the review workflow you actually run.
Where NirmatAI fits
The evidence-assessment layer between your systems and your experts.
NirmatAI complements the governance, risk and compliance (GRC) platforms that run your program and the specialist tools that validate technical work. It replaces neither.
GRC & compliance platforms
Run the program
Controls, workflows, risks, issues, evidence collection and the system of record.
NirmatAI
Assess the evidence
Link evidence, findings and rationale to every requirement, ready for qualified human review.
Engineering & verification tools
Validate technical work
Requirements traceability, testing, calculations, models and specialist verification.
Strongest fit: teams repeatedly assessing large volumes of documented evidence against structured requirements, across several standards, entities or sites.
Evidence, control and traceability
Your evidence. Your environment. Your control.
Compliance evidence is sensitive. Deployment, traceability and reviewer accountability are product requirements, not afterthoughts.
Customer-oriented cloud deployment
Starting with Microsoft Azure and a customer-cloud, BYOC-oriented model. Architecture, access and controls are confirmed during technical qualification.
Every finding links to its sources
Reviewers can open the underlying document rather than trust an unexplained output.
Decisions stay with the requirement
Accept, edit and reject decisions create a review history a qualified reviewer can stand behind.
One boundary worth stating plainly: NirmatAI is a software product for internal evidence review. It does not issue certifications, and using it does not constitute, replace or influence any certification decision.
Supported standards
A growing list, and your own requirement sets on request.
NirmatAI is configured to defined requirement sets. New standards are added as scopes are validated.
Additional standards, regulations and internal requirement sets are configured with expert support. Tell us which framework you work to and we will confirm what can be scoped.
Support is defined by standard and scope. A standard name never implies coverage of every clause or engineering judgment, and exact coverage is confirmed before commitment.
Why NirmatAI
Faster reviews, and decisions you can defend.
Expert-built standards knowledge, requirement-level assessment and human control in one governed workflow.
Expert-built intelligence
AI grounded in expert-designed assessment methods and standards knowledge, not a general-purpose model pointed at a folder.
Assessment at requirement level
Test whether the evidence addresses each requirement, not simply whether a document exists.
Traceable expert decisions
Requirement, evidence, rationale, proposed conclusion, human decision. Every step visible.
Built by auditors. Designed for accountable reviewers.Rooted in real-world assessment practice, not theory alone.
Expert-built methodology
World Economic Forum
MINDS
MINDS verified case
A joint SGS and CertX use case, part of the MINDS Network as a verified case for 2026
The AI-augmented assurance approach behind NirmatAI is featured on the World Economic Forum MINDS platform, among the validated cases that become part of the MINDS evidence base. It shows how complex assurance workflows can become more scalable, consistent and traceable while qualified experts retain final decision authority. The feature is not WEF certification, endorsement or approval.
Read the SGS and CertX announcementFAQ
Answers before a demo.
NirmatAI's current scope, boundaries and deployment starting point.
What is NirmatAI?
An AI-powered compliance assistant that links requirements to relevant documented evidence and prepares findings and rationales for qualified reviewers.
Which standards does NirmatAI support?
ISO/IEC 27001, FSC, ISO/IEC 42001, ISO/SAE 21434, IEC 62443 and ISO 26262 are supported today, within validated documentary scope. The list is growing, and additional standards, regulations and internal requirement sets are configured on request.
What evidence can NirmatAI review?
Documentary evidence: policies, procedures, reports, records, notes, screenshots, transcripts and other text-heavy compliance material.
Does NirmatAI make compliance or certification decisions?
No. It prepares evidence links, findings and rationales. Qualified reviewers retain final authority. NirmatAI does not issue certifications, and using it does not constitute or influence any certification decision.
Is NirmatAI a continuous compliance monitoring platform?
No. It supports controlled, repeatable documentary review runs rather than continuous monitoring of compliance status.
How is NirmatAI deployed?
The validated starting point is Microsoft Azure with a customer-cloud / BYOC-oriented model. Exact architecture and controls are confirmed during technical qualification.
Start with one real review
Build the business case on your own evidence.
A controlled pilot measures what a review costs you today, then the same review with NirmatAI. The number comes from your evidence, not from a promise.
Prefer to write directly? nirmatai@certx.com
- Scope. One requirement set, one representative evidence pack, named reviewers.
- Baseline. Measure today's effort: hours, people, cycles, rework.
- Assisted run. Same review with NirmatAI. Reviewers accept, edit or reject each finding.
- Business case. Compare both runs against success metrics agreed up front.
Success metrics agreed up front
Set before the pilot starts, so the result is a decision input and not a debate.
- Reviewer hours per requirement set, before and after
- Elapsed time from evidence intake to review-ready output
- Share of AI-proposed findings accepted or accepted with edits
- Gaps found before an external or customer review found them
- Consistency of interpretation across reviewers, sites or entities
- Reviewer confidence in the traceability of each conclusion
The 90% and 8× figures above were observed in controlled scenarios and are not a guaranteed outcome. Actual impact depends on scope, evidence volume and quality, standard and workflow configuration. That is why the pilot measures your baseline first.
NirmatAI supports internal evidence review. It is not a certification decision engine and it is not a guarantee of conformity. Qualified reviewers retain final authority for all conclusions.