Collect
Someone chases documents across mailboxes, shared folders and colleagues, often depending on where a file happened to be saved.
A product by MindScript IT · In validation
Evidence Intelligence for Manufacturing
TraceFlow AI helps manufacturing teams structure information scattered across PDFs, emails, spreadsheets and supplier documents, connect important data points to their original evidence, and identify missing, inconsistent or outdated information.
Interactive prototype · fictional demo data · simulated extraction
Demo response readiness
3
verified
4
open items
7
requested
The upper is made of full-grain leather (78%) and recycled polyester textile (22%), supplied under contract L-18.
Supplier action
LeatherWorks Italia
Leather origin · current REACH declaration
DPP preview — demo
S-042
Accepted answers only
Demo preview — not publishedDemo preview — not scannable
The work behind the answer
A client asks for composition, origin, certificates and environmental information for one product. The same piece of information can sit in a bill of materials, a technical pack, a supplier declaration, an expired certificate and a production spreadsheet — each owned by a different person.
Someone chases documents across mailboxes, shared folders and colleagues, often depending on where a file happened to be saved.
Dates, origins, compositions and declarations are read by hand, sometimes against sources that quietly disagree.
The next client asks a similar question, and the same search starts again, because nothing durable was kept from the first one.
TraceFlow evidence layer
Structured fields · source kept · gaps identified
Client response
Digital Product Passports are creating a growing need for structured, accurate and traceable product information. TraceFlow focuses on the evidence layer behind that information.
Product thesis
The answer is the visible part. The workflow that finds, sources and verifies the evidence is the product.
TraceFlow AI is not a chatbot over your documents. It turns what it finds into structured fields, keeps each one attached to the document and location it came from, shows what is missing rather than filling it in, and requires a person to accept a value before it becomes an answer.
How it works
Client Request → Structure → Evidence → Verify → Identify Gaps → Respond
The request is turned into a checklist of fields. Everything downstream — readiness, gaps, the final answer — is measured against that list.
Demo response readiness
Response being assembled
Giulia Ferrari · Meridian Sport Retail
“Please provide the material composition, country of origin, certificates and sustainability information for AeroStep Runner, SKU S-042.”
Bills of materials, technical packs, declarations, certificates and production records are read into the requested fields. Source collection belongs here.
Demo response readiness
Response being assembled
Loaded sources
Each proposed value is shown with its document, the page or cell inside it, and the sentence that supports it. Where two sources disagree, both are shown.
Demo response readiness
Response being assembled
The upper is made of full-grain leather (78%) and recycled polyester textile (22%), supplied under contract L-18.
Proposed
The reviewer accepts, corrects or rejects each value and chooses between conflicting sources. Nothing becomes an answer without that step.
Demo response readiness
Response being assembled
Decision
Marco Rossi — Accepted without correction
Missing, expired and contradictory information becomes a targeted supplier follow-up. Whatever comes back re-enters human review.
Demo response readiness
Response being assembled
Gaps detected (4)
No document in the loaded pack covers this field. The supplier declaration states on page 4 that it covers composition only.
This declaration is valid until 31/12/2023.
No document in the loaded pack supports a sustainability or recycled-content claim for this product. TraceFlow does not fill this in.
Sole: natural rubber, vulcanised, 4 mm.
Supplier follow-up · draft
Missing product information — AeroStep S-042
“For AeroStep Runner (S-042) we need the country of origin of the leather and a current REACH declaration of conformity. Please attach the document behind each answer.”
Draft ready — sent manually by the user
A structured answer: each requested field, the value, its source, who approved it, and anything that is still outstanding.
Demo response readiness
Updated after human approval
Client response preview
Still outstanding
Anything without accepted evidence stays visible in the response instead of being dropped to reach a round number.
Client response ready — 7/7 requested items verified
Demo response readiness
Response being assembled
Giulia Ferrari · Meridian Sport Retail
“Please provide the material composition, country of origin, certificates and sustainability information for AeroStep Runner, SKU S-042.”
Inside the workspace
A static view of the same record the interactive prototype works on.
Demo response readiness
Verified required fields ÷ total required fields for the current client request.
Requested fields
Evidence source
The upper is made of full-grain leather (78%) and recycled polyester textile (22%), supplied under contract L-18.
Supplier follow-up queue
Session history
Session history only — the prototype has no durable audit storage.
DPP preview — demo
S-042
Accepted answers only
Demo preview — not publishedDemo preview — not scannable
Fictional demonstration data. Figures are derived from the fields shown, not from customer results.
Human in the loop
TraceFlow keeps four things apart: what the extraction proposed, what a document actually supports, what a reviewer accepted, and what goes into the answer. A verified answer keeps its evidence; information that is missing stays visible as missing.
A structured value is extracted. It carries no authority yet.
The value stays linked to a document, a page or cell, and a version.
A named person confirms or corrects the value. Only this step creates an answer.
Only accepted values, with their sources, go into the client response.
Sample field
Upper material composition
Full-grain leather 78%, recycled polyester 22%
A value never moves straight from extraction to the client response. Every step has to be crossed explicitly, and editing a value or replacing its evidence sends it back for review.
What makes it different
Five things a general-purpose document assistant is not built to do — described as what TraceFlow does, not as a comparison with any named product.
Answers live in named fields with a defined meaning, so the same question next quarter is an assembly job rather than a fresh search.
Each value stays attached to the document, page or cell behind it. A reader can check the answer instead of trusting it.
Missing, expired and contradictory information is raised as work to do, rather than smoothed over in a fluent paragraph.
A named person accepts each value. New evidence, including a supplier's reply, always comes back for review before it counts.
A verified field with its evidence can answer the next client, the next audit and the next system without being rebuilt.
A data model and validation rules shaped around footwear product evidence.
Reviewer decisions are the material that improves extraction rules over time.
Relationships between suppliers, materials, certificates and products become reusable across products.
Working closely with manufacturers, laboratories and industry bodies in one region.
These four areas describe an intended strategy. None is established: TraceFlow AI is a product entering validation.
First pilot
The first pilot stays narrow on purpose: one manufacturer, one product line, a controlled set of documents, and a workflow that already causes pain today.
1
manufacturer
1
product line
10
references
3–5
suppliers
30
days
Week 0
Scope, data minimisation and a measurement of how the request is answered today
Week 1
Loading 30 to 100 documents and agreeing the field model
Week 2
Human review of the proposals and identification of gaps
Week 3
Supplier follow-up and handling of what comes back
Week 4
Completed responses, a before-and-after comparison, and the decision to continue or stop
Targets defined before the pilot — not results
These figures are success criteria set before a pilot. None has been measured, on real data or otherwise.
How it is built
Companies, products, suppliers, documents and users, kept separate per customer.
PDF, DOCX, XLSX and email, with the source metadata preserved.
Structured extraction, matching between sources, and detection of contradictions.
Acceptance, correction, rejection, assignment and decision history.
Targeted follow-ups and tracking of the evidence expected back.
A structured client answer, an evidence report, and an optional passport preview.
This describes the intended architecture. Only the parts you can exercise in the prototype are built today; see the section below for what is demonstrated and what is not.
Handling your business data
TraceFlow AI is a prototype. The list below separates what the prototype actually does from what a real deployment would need, so nothing here reads as a description of live security controls.
Every value keeps the document and location it came from, and the excerpt can be opened and checked.
No value counts as an answer until a reviewer accepts it, including evidence returned by a supplier.
The prototype keeps a session history in the browser tab and loses it on reload. Durable, tamper-evident audit storage is not built.
The demo has a fixed reviewer profile and no sign-in. Authentication and role-based access are design work, not implemented behaviour.
A pilot would take only the documents needed for the agreed product scope, and agree retention and deletion in writing beforehand.
Running inside your own environment is possible in principle, and would need technical work and infrastructure agreed with your team. It is not available as a switch.
A commitment we would put in the pilot agreement. The prototype processes nothing, so there is nothing to demonstrate yet.
None of this is a certification, and a reviewed declaration inside TraceFlow is evidence about a field — not a certificate issued by TraceFlow or by MindScript IT.
Start narrow
A focused entry point: an industry with complex product documentation, where the same evidence problems repeat across products and suppliers, and where we can work closely with the people doing the job. It is where we intend to validate the workflow — not a claim of customers or completed pilots.
Expansion only makes sense once a second manufacturer can be onboarded without rebuilding the product.
Footwear in the Marche region
Starting pointManufacturers, suppliers and laboratories
A reusable product evidence layer
Other manufacturing sectors with the same problem
HypothesisWe are looking for a manufacturer, an industry partner or a support programme willing to test the workflow on a real product line, with a controlled set of documents and measures agreed in advance.
TraceFlow AI is a MindScript IT product in validation. This page describes a target workflow and an interactive prototype with fictional data — not a deployed product, a certification, or a guarantee of compliance.