Upload a statement of work and SpiderNet extracts every requirement with a citation to its page, confirms with you which ones are mandatory, searches your supplier base and the market beyond it, and returns an explainable shortlist where every score opens onto the evidence behind it.
Reading the scope, deciding what is mandatory and working out who could deliver it happens across spreadsheets, old records and personal memory.
Master data is incomplete and duplicated, and nobody can tell which attribute was ever actually verified.
Sourcing defaults to incumbents because discovering new qualified suppliers is manual work nobody has time for.
When a selection is challenged, the reasoning lives in a specialist’s head rather than in evidence anyone can inspect.
Reads PDF, DOCX, XLSX and scans in Azerbaijani, Russian, English or a mix, and extracts each requirement with its type, importance and a citation to document, section and page.
A requirement becomes an exclusion rule only after a person confirms it. Missing information surfaces as a gap, never as a silent rejection.
Source-specialised agents work registries, procurement pages, supplier sites and accreditation records to widen the candidate pool beyond the names you already know.
Every material attribute carries its source, date observed, method, confidence and verification status — AI-inferred data is never treated as verified fact.
Mandatory filters, taxonomy match, semantic retrieval, evidence assessment and weighted scoring stay five separate layers, so relevance is never mistaken for qualification.
No supplier can pay for visibility. Commercial relationships never influence qualification, matching, ranking or shortlist composition.
Turn a scope document into a defensible shortlist in hours, with the evidence attached.
Set taxonomy, qualification templates and scoring weights once, then reuse them on every event.
Resolve duplicates and contradictions in one place, with supplier corrections arriving in a queue.
Review and confirm the technical requirements on the scope you asked for.
Discovery, match, qualification and recommendation stay four separate questions — a supplier that looks relevant can still fail a mandatory requirement, and SpiderNet says so.
Azerbaijani, Russian, English and mixed-language scope documents, scans included, with accuracy measured per language against a golden set of historical SOWs.
Suppliers are enriched when a live sourcing case needs them, not on a schedule that spends budget on records nobody will read — and every enrichment job records its cost.
Engineered by Allmaz, an AI product studio based in Azerbaijan.
Every score opens onto the evidence beneath it: the requirement, the supplier attribute, where it came from, when it was observed and whether anyone verified it. Buyer-private data — contracts, prices, evaluations and scoring configuration — stays inside your own tenant, risk and adverse findings go to human review rather than being published automatically, and an approved shortlist is immutable.
Allmaz is the AI product studio of Smart Solutions, which built and operates Azerbaijan's unified public procurement portal — established by presidential decree and delivered as one of the country's first public-private partnerships in digital government.
Request a demo to see one of your own SOWs turned into an evidence-backed shortlist.