Evidence and Traceability Doc.
AI traceability documentation is one audit trail across your tools. As a Dhaka-based company, we build that trail for international companies, one AI system at a time, at a fixed price per system: records that connect every AI decision to a system version, the model and data versions behind it, the tests run on it, who approved it and what changed since. A documentation lead and an engineer read your repositories, registries and tickets, and your owner approves every record.
One exit at each step. No long-term commitment at any of them. We start with the one AI system whose evidence would take longest to find if a customer, an auditor or a regulator asked tomorrow: the scoring model, the support chatbot, the vendor model nobody versioned. Where each record lives is mapped, the links that are missing are listed, and the trail is costed in your numbers.
You get a written verdict — an evidence map first, one system’s audit trail, approval and change records first, a law-specific file with the trail under it, or a governance inventory before any trail. If nothing is in scope, you stop here and keep the gap review.
30 minutes 01 Scope · free call
2 weeks 02 Diagnose · $1,500, half credited to the pilot
4 weeks 03 Pilot · $4,000 fixed
Per system 04 Production · from $6,000, quoted after the pilot
Quarterly 05 Managed Ops · optional, cancel any quarter
What is included in AI traceability documentation?
AI traceability documentation hands over six records: an evidence map, a version and lineage record, test and evaluation evidence, approval records, change records, and a retention and access schedule with the handover. Every record cites the identifiers the others use, so a reviewer can walk from a decision back to its data without asking anyone.
Evidence map
Every evidence object for the system, from code, model and dataset versions to test reports, approvals, change tickets and logs, listed with where it lives, its owner and its identifier.
Version and lineage record
One record per release naming the system, model and dataset versions behind it, their sources and preparation steps, and the vendor model it depends on, readable without a pipeline.
Test and evaluation evidence
Every test run, evaluation result, bias check and human review tied to the version it examined, with its acceptance threshold and the person who read the result.
Decision and approval records
Who approved each release, model change or dataset swap, when, against which version and evidence; approvals in chat and email move into the trail with their dates.
Change records
A change log per system: what changed between two versions, why, who asked, who approved and which tests were re-run, so a shift in behaviour is explained, not remembered.
Retention, access and handover
A retention schedule per evidence object, access rules for who may read or change it, version-control duties named on your side, a handover session, then optional quarterly reconciliation.
Not included: Logging systems or machine learning operations (MLOps) tooling · running your pipelines · certification, or calling any system audit-ready.
Which AI traceability record do you need first: lineage, test evidence, approvals or the change log?
AI traceability documentation is four objects that cite each other, and the evidence review says which one you lack first. The version and lineage record is the spine; test evidence and approval records attach to it; the change log keeps it true after release; the evidence map says where each one lives. All four sit inside our AI services.
Lineage record
The release-to-model-to-data record, when nobody can say exactly which data version trained the model that made a given decision.
Test evidence
Test and evaluation results tied to the version they examined, when reports exist in five tools and none names a release.
Approval record
Dated sign-offs against a named version, when releases are approved in chat and nobody can show who said yes.
Change log
The record of what changed between versions and why, when the system is live and behaviour has shifted without a paper trail.
How does AI traceability documentation link one decision back to its data?
AI traceability documentation links a decision to its release, the release to its model version and the model to its datasets, then attaches tests and approvals. The sample pilot below does it for a credit-scoring model whose records lived in a repository, a model registry, a ticketing tool and email; yellow marks where a person signs.
Pilot log · credit-scoring model
The trail at that step · its result
Scope signed
The system, its releases and its owner.
Lineage recordTest evidenceApproval recordChange log
SystemCredit-scoring modelReleases6 in scopeOwnerNamed
4
Evidence objects in scope1 system · 6 releases
Evidence located
Where each record lives today.
RepositoryModel registry5 test reports4 approval emails
14 of 22
Links present before any work8 missing, listed with owners
Links built
One lineage record per release.
Decision→Release→Model version→Dataset version
Tests attachedApprovals attached
8
Missing links requested and closedfrom the named owners
Owner sign-off · a person signs
Your owner follows every link.
Decision→Release→Model version→Dataset version
Owner sign-offEvery link resolves, and the approval is logged with its date.
22 of 22
Links that resolveapproval logged with date
Handed over
The trail stays in your own systems.
Trail approved→Your systems→Change log open→Retention dated
Handed overchange log open · retention schedule dated
Click a step, or a number below, to switch the result
Every quarter, each release since the last review is checked against its lineage record, tests and approvals.
Illustrative example. Click a step to see the trail at that point. Yellow marks where a person signs; every item traces to an evidence object, a version identifier, an approver and a date.
How does AI audit trail documentation get built, from scope to Managed Ops?
AI audit trail documentation gets built in five steps, each an exit, because traceability fails when it starts with a tool and ends with a dashboard nobody reads: a free scoping call, a two-week evidence review, a four-week pilot linking one system’s records, production for further systems, then quarterly Managed Ops. Every step ends with a signed record.
01 30 min · free
Scope A call about the system, who is asking for evidence and by when, which tools hold its code, models, data, tests and approvals, and who owns it. You leave with a diagnostic quote.
02 2 weeks · credited
Diagnose Every evidence object located with its owner and identifier; each link between versions, data, tests and approvals marked present, partial or missing; gaps assigned to owners; the pilot priced in writing.
03 4 weeks · fixed price
Pilot One system’s trail built: a lineage record per release, test evidence and approvals attached to their versions, the change log opened, missing records requested, weekly review, owner sign-off on the last day.
04 Per system · quoted after pilot
Production Further systems in the evidence review’s order, each with its own trail, plus retention and access rules per evidence object, a change procedure your teams follow and a handover session.
05 Quarterly · optional
Managed Ops A quarterly reconciliation: every release since the last review checked against its lineage record, tests and approvals, gaps listed with owners, retention applied, the change log signed. Cancel any quarter.
Who builds your AI audit trail, and with what?
A documentation lead who owns the evidence map and the link list, an engineer who reads repositories, model registries, pipelines and test tooling, and a reviewer who follows every link.
Communication A weekly review call and a shared channel
Delivery Your repositories, registries, ticketing and document system
QA Second-person check of every link; versioned; approver named
Retention A retention schedule and access rule per evidence object
Boundary We create records; we do not run your pipelines or certify
Ownership Every record, schedule and template in your name
Which AI traceability documentation do you need first: an evidence map, one system’s trail, approvals, a law file or an inventory?
The first AI traceability documentation depends on which of five situations you are in; five questions show which fits. Nothing findable means an evidence map; records that do not link, one system’s audit trail; releases approved in chat, approval and change records; a regulator asking, a law-specific file; no list of AI systems, AI governance documentation first.
1. Is there a list of your AI systems in production?
2. Can you name the model and data version behind a given output?
3. Where do test and evaluation results live?
4. Are releases and changes approved by a named person?
5. Is anyone asking for the evidence right now?
Which one do you need? Answer five questions.
One system’s audit trail as the pilot
Records that exist but do not cite each other are the normal starting point: the most exposed system gets a full trail (a lineage record per release, tests and approvals attached, a change log) in four weeks.
A first estimate; the evidence review confirms it.
How the verdict is decided
No list of systems → the governance inventory first
A regulator or a law asking → a law-specific file with the trail under it
Nothing findable → an evidence map first
Releases not approved by a named person → approval and change records first
Otherwise → one system’s audit trail
Why choose us as your AI traceability documentation company?
An AI traceability documentation company is judged on whether a stranger can follow the trail from decision to data, not on its tooling. Article 12 of the EU AI Act requires high-risk AI systems to log events over their lifetime; logs become evidence only when linked to versions, tests and approvals. We build the links, one system at a time.
Without a linked trail
!!!!!
- Test reports in five tools, none naming the release they tested
- Approvals in chat threads that nobody can find under audit
- A model in production whose training data version nobody can name
- A dashboard that shows metrics but cannot show who changed what
With EICRA
Pilot report · Credit-scoring model
Evidence objects4 of 4Links resolved22 of 22Gaps closed8Approvals recorded6VerdictTrail accepted
Illustrative example
- One lineage record per release, from decision back to data version
- Every test and approval attached to the version it belongs to
- Gaps listed by evidence object with the named owner who closes them
- A change log and retention schedule that keep the trail true
Is it safe to outsource AI traceability documentation?
Outsourcing AI traceability documentation is safe when access is read-only and your owner signs every record, because the real risk is who can change a record and who sees your code and data. As a Bangladesh-based company, we work inside your systems under an NDA, read records, never production data, and change nothing without a ticket. Reviewed By Eicra.com team
Which AI traceability agreements are signed, and when?
Non-disclosure agreement (NDA) — mutual, signed before any repository, dataset description, test report or approval record is shared with anyone.
Data processing agreement (DPA) — processor terms under Article 28(3) of the General Data Protection Regulation (GDPR) and the equivalent national law, for personal data in datasets, logs or examples.
International data transfers — standard contractual clauses or the transfer instrument your jurisdiction requires, signed before personal data moves.
Access — read access to repositories, model registries, pipelines, test tooling, ticketing and your document system; no write access to production, no credentials of ours.
Certifications — listed only when held; none are claimed, and we do not certify your system or call it audit-ready on this page or in any proposal.
What AI traceability controls, ownership and rework terms apply?
Your accounts Every record, schedule and template lives in your repositories and document system from the first draft; nothing is stored on our side.
Document ownership All intellectual property (IP) in the records is assigned to you in the contract; you may hand them to counsel, a customer or an assessor at any time.
Boundary We design the evidence structure and create records; we do not implement logging systems, run your pipelines, certify your system or give legal advice.
Approval No record is final until your named owner has approved that version; approvals, dates and versions are recorded in the trail itself.
Rework Free when a link fails its agreed acceptance list within thirty days of handover; new systems or tools are priced first as a change request.
What proof do you get before you pay for AI traceability documentation?
Before you pay for AI traceability documentation, you get evidence instead of promises: a two-week evidence review that gives you your own link-by-link gap list, a pilot trail signed by your owner before production is quoted, and a free 30-minute scoping call. Client case studies with figures follow as clients give permission to name them.
2 weeks
For the evidence map and gap review, with every link between versions, data, tests and approvals marked.
4 weeks
To one AI system’s audit trail, built from your own records and signed by your owner.
30 days
Of free rework when a link fails its agreed acceptance list after handover.
Case studies: client results with numbers are added here as clients give permission to name them. Ask on the scoping call for references in your industry.
What do buyers ask about AI traceability documentation?
How much does AI traceability documentation cost?
Our AI traceability documentation is priced per system, never per hour, and each price is on the price cards at the top: a two-week evidence map and gap review ending in a written verdict, half credited to the pilot; one AI system’s audit trail, built, linked and signed by your owner; further systems quoted after the pilot.
What is AI traceability documentation?
AI traceability documentation is the set of linked records that lets a reader move from any AI decision back to the system version that produced it, the model and dataset versions inside it, the tests run on it, who approved its release and every change since. It is an audit trail across tools, kept as records your owners sign.
What should an AI audit trail contain?
Seven things, each carrying an identifier the others cite: the system version and release date; the model version; the dataset versions with their sources; test results tied to the version they examined; approval records with names and dates; change records saying what changed and why; and the retention and access rule for each. Less is a folder, not a trail.
How does AI traceability connect data, models and decisions?
Through identifiers: every dataset version, model version and release carries one, and every test report, approval and change record cites the identifiers it concerns. A decision log entry names the release, the release names its model version, the model version names its training and evaluation datasets, so a reviewer walks from decision to data in three steps.
Is traceability documentation the same as a technical file?
No. A technical file is what one law requires for one system, and we draft those as EU AI Act documentation, not here; traceability documentation is the cross-system trail such a file draws its evidence from. You can hold a trail with no law in sight, but no file is credible without one, so the trail comes first.
Start with a free 30-minute scoping call or the two-week evidence review.