Find suppliers beyond your incumbents
Find suppliers beyond your incumbents with SpiderNet: a practical, on-prem approach built for Azerbaijani teams.
Find Suppliers Beyond Your Incumbents
SpiderNet is an advanced AI supplier intelligence and SOW matching platform specifically engineered for procurement teams to identify highly qualified candidates before the formal invitation to sourcing events begins. By automating the extraction of complex requirements from Statement of Work (SOW) documents and expanding the candidate pool through agentic discovery, the platform enables organizations to move beyond static vendor lists and discover the best-fit partners based on objective data. The platform operates as a multi-tenant SaaS, ensuring that buyer contracts, pricing, and scoring configurations remain strictly private to each tenant. With a first pilot in Azerbaijan evaluated against a golden set of historical SOWs, SpiderNet provides a rigorous, evidence-based approach to supplier discovery, ensuring that every match is backed by verifiable data and human-validated exclusion rules.
Strategic Advantages of SpiderNet
Eliminate silent exclusions by surfacing information gaps as unknowns rather than auto-rejecting candidates
Expand your candidate pool via agentic discovery across public registries, accreditation records, and company websites
Guarantee ranking neutrality through an architectural constraint that prevents supplier-side monetization from influencing scores
Maintain a complete audit trail with precise citations for every extracted requirement and evidence-backed scoring
Ensure data integrity by treating AI-inferred data as unverified until confirmed, with human review required for adverse findings
Accelerate screening cycles using a five-layer matching engine that separates mandatory qualification from weighted scoring
Core Capabilities for Procurement Intelligence
Multilingual Requirement Extraction
Ingests PDF, DOCX, XLSX, and scanned documents in Azerbaijani, Russian, English, or mixed languages to extract structured requirements with taxonomy mapping, importance levels, and confidence scores.
Supplier Golden Records
Centralized intelligence storing identity, capabilities, qualifications, and performance signals, where every attribute tracks its source, date observed, and verification status.
Agentic Discovery
Proactively widens the candidate pool by scanning approved directories, procurement pages, and public registries to find untapped supplier potential.
Five-Layer Matching Engine
A rigorous evaluation process combining mandatory filters, taxonomy matching, semantic retrieval, LLM evidence assessment, and weighted scoring.
Evidence-Based Shortlisting
Generates a detailed requirement-by-requirement matrix showing evidence counts, contradictions, and risk flags for an immutable, transparent shortlist.
From SOW to an Immutable Shortlist
Frequently Asked Questions
How does the system ensure data accuracy and prevent AI hallucinations?
Every material attribute carries a source, date observed, and confidence level. AI-inferred data is never treated as verified fact, and all risk or adverse findings require human review before they are published.
Can suppliers pay to improve their ranking or visibility?
No. Ranking neutrality is a core architectural constraint. Supplier-side monetization has no impact on qualification, matching, ranking, or the final composition of the shortlist.
What is the difference between mandatory criteria and weighted scores?
Mandatory criteria sit entirely outside the weighted score. They function as a binary gate to determine a supplier's qualification status before they are evaluated on a weighted scale.
How is sensitive procurement data protected in the SaaS environment?
SpiderNet utilizes a multi-tenant SaaS architecture, ensuring that all buyer contracts, pricing, evaluations, and scoring configurations remain private and isolated to the specific tenant.
How does the platform handle documents in different languages?
The system can ingest and process PDF, DOCX, XLSX, and scanned PDFs in Azerbaijani, Russian, English, or a mix of these languages, extracting structured requirements with high accuracy.
Ready to Modernize Your Sourcing Process?
Contact Allmaz to learn how SpiderNet can help your procurement team find more qualified suppliers.
Request a demo