Turn unstructured data into a dataset you can work with.
Granalis reads what only humans could read before — PDFs, scans, spreadsheets, emails, images, reports — links it with the data you already have, and turns it into a structured, comparable dataset. Every number with evidence. Every statement verified.
| Part | Operating temp. | Certification | Evidence |
|---|---|---|---|
| MCX-4021-T | −40 … +85 °C | AEC-Q100 | Datasheet Rev. 3.1 · p. 12 |
| MCX-4021-R | −40 … +105 °C | AEC-Q100 | Datasheet Rev. 2.0 · p. 9 |
| PKN-118-A | −25 … +70 °C | — | Datasheet Rev. 1.4 · p. 7 |
| PKN-118-B | −25 … +85 °C | — | Datasheet Rev. 1.4 · p. 7 |
Your most important data lives where software can’t read it.
In PDFs, scans, spreadsheets and attachments — in formats defined by your suppliers, customers and authorities, not by you. Today a human reads them. Only what’s needed right now gets captured. The rest stays inaccessible.
Only a fraction gets captured
Manual extraction doesn’t scale. What isn’t captured can’t be offered, checked or analyzed — lost business, not just lost time.
Same thing, a hundred formats
Every sender names, measures and formats differently. Without a shared structure, every comparison stays manual — and every analysis impossible.
Values without origin
Retyped data loses its source. When questions, audits or errors come up, nobody knows where a value came from or whether it still holds.
Generative AI alone doesn’t solve this: it produces plausible answers — plausibility is not reliability. Business data needs provenance, verification and structure.
A machine that turns raw data into verified data.
Granalis processes your holdings in four steps — into a living dataset that continuously delivers results: answers, patterns, alerts and actions, straight into your systems.
Capture
PDF, scan, spreadsheet, email — in the sender’s format. Every source is accepted, versioned and assigned to its case. Nothing gets lost, nothing gets retyped.
Understand
Detect the type, extract values — from text, tables and characteristic curves — and slot every value into the ontology: your target model that turns a hundred formats into one structure. Every source location comes with it: document, page, position.
Verify
Multiple AI instances compare their results against each other. Only consistently evidenced values pass — everything else is decided by an expert reviewer.
Work
Your dataset keeps working: it stays verified, grows with every document and continuously delivers results — answers, patterns, alerts, actions — right where you work. And your data remains yours: full export anytime.
Reads what you receive
Datasheets, contracts, certificates, reports — as PDF, scan or spreadsheet, in hundreds of layouts. Even diagrams and characteristic curves are interpreted, not just text.
Linked, not just extracted
A configurable ontology turns a hundred formats into one structure: synonyms merged, units converted, measurement conditions preserved. And extracted values are linked with your existing data: matched to master data, duplicates detected, contradictions flagged — up to a reliable golden record.
AI checks AI — zero-hallucination logic
Multiple AI instances read, interpret and check each other. Every value must be anchored to its source: document, page, position. Whatever isn’t consistently evidenced is decided by a human. Only verified data enters your dataset — built as architecture, not claimed as a promise.
Your data stays yours
Operated as a managed service in European data centers: strict tenant isolation, encryption, GDPR-compliant. Your data is never used for model training. Dedicated environments on request.
Every statement auditable
Not just “which document” — which page, which position, which revision, who verified it. Traceable back to the source, even years later.
Four stages — each builds on the last.
Structured, evidenced data is not an end in itself. The value grows in stages — and each stage is measurable on its own.
Efficiency
Quality
Analysis
Action
Automation with control: every action starts as a suggestion. How far Granalis acts on its own is set by your rules — not by the model.
Concrete cases, not a platform promise.
Granalis is introduced through one bounded, measurable process — not as a major project. Three worked examples, followed by more industries with the same mechanism:
Product data from datasheets
A distributor carries parts from over 150 manufacturers — each delivering datasheets in its own layout, with parameters in tables and characteristic curves. Only what already sells is captured in structured form. Granalis unlocks the entire catalog.
Supplier onboarding
Master data, certificates, self-disclosures and supporting documents arrive as PDFs and email attachments. Granalis extracts, matches against existing suppliers and detects duplicates, missing mandatory fields and expiring certificates.
Contract analysis
Terms, notice periods, price clauses and obligations sit in hundreds of contract documents. Granalis turns them into structured, evidenced fields — each clause with its source location.
Claims files
Reports, expert opinions, invoices and photos become one structured claims file: matched to policy and coverage, missing documents and contradictions flagged.
Credit & audit files
Financial statements, collateral agreements and supporting documents become verifiable fields — for credit assessment and audit, every value with its source location.
Quality complaints & 8D
Complaints, inspection reports and corrective actions are captured as one connected structure: product, batch, supplier, defect pattern, cause — analyzable across years.
Origin & compliance documents
Supplier declarations, certificates and customs documents are captured in structured form — deadlines, gaps and expired documents become visible before they block anything.
Price lists & terms
Price changes, tiers and discounts from many suppliers’ PDFs and spreadsheets become comparable — instead of sitting side by side in a hundred formats.
Catalog onboarding
Manufacturer data from heterogeneous sources becomes uniform shop attributes — new assortments go live faster, with traceable origin per attribute.
This is the baseline — not the limit.
The mechanism is universal: what works for datasheets and contracts works for every form of unstructured information — reports, events, images, video streams, signals. The same machine, the same burden of proof.
Events and news situations meet your structured portfolio: supply chain and market risks become visible before they materialize.
Sanctions, export and origin screening on a dataset with a complete evidence trail.
Patterns across thousands of claims files, invoices and applications that no single reviewer could ever see.
The question is not whether your unstructured data can be given structure — but which comes first.
The first step is a pilot — not a platform project.
We start with one process, real documents and metrics agreed in advance. You see what Granalis delivers on your own data — before you commit.
- Bounded scope: one process, defined document types, a clear timeframe
- No infrastructure project: you provide documents, Granalis provides structure — starting in days, not months
- Measurable, not a demo: success criteria are agreed up front, not explained afterwards