Every finding VeriTender surfaces is reproducible, citation-anchored, and tamper-evident. The methodology is engineered to meet the evidence standard regulators apply to public-procurement oversight — not the credibility standard of a typical SaaS dashboard.
Live capture from 22 jurisdictions across 7 procurement data standards.
Schema mapping and multilingual translation into a canonical tender model.
ML pattern detection across risk, collusion, fairness, and competition axes.
Human analyst review with confirm / escalate / dismiss rationale on every flag.
Hash-chained audit log and signed evidence pack export for formal disputes.
22 jurisdictions live. Sources include TED EU (eForms 2024), ProZorro (Ukraine), eOJZ/OCDS (Georgia), Bund.de (Germany), SAM.gov (United States), Find a Tender Service (United Kingdom), e-tender.gov.az plus 5 SOE feeds (Azerbaijan), the World Bank Procurement Notices API, EBRD ECEPP, ADB, ISDB, AIIB, EIB, and UNGM.
Refresh frequency: hourly for live OCDS feeds, daily for batch SOE portals, weekly for IFI pipelines. Backfill goes deep enough to compute cohort baselines (12–36 months per source).
Standards supported: OCDS, eForms 2024, ProZorro JSON, SAM.gov CDB, eUKPROC, BMI XML (Bund.de), GE OCDS extension.
Each source schema is mapped to a canonical tender model: buyer, lot, value, currency, deadline, awarded bidder, document set. Non-English titles are auto-translated via Claude Sonnet (UK→EN, AZ→EN, KA→EN, RU→EN) with the original language always preserved alongside the translation.
All money values are normalised to EUR at tender publication date so cross-country benchmarks are computed against a stable reference, not against today's spot rate.
Translation provenance is preserved — original language always available alongside the EN translation, with model version and timestamp recorded for audit.
Every tender and bidder cohort is scored on four axes:
Risk — spec irregularity, vendor-specific requirements disguised as generic. Collusion — rotational bidding, price anomaly z-score below −2, UBO overlap, shared infrastructure, document-metadata leaks. Fairness — short bid window, missing notification, narrow eligibility. Competition — cohort size, win-rate concentration, repeat-winner patterns.
Seven canonical collusion patterns: shell-company bidding, rotational bidding, price anomaly, document-metadata leak, UBO overlap, complementary pricing, shared infrastructure.
Automated scores produce flags, not verdicts. Each flag enters an analyst review queue with the underlying evidence — source documents, computed metrics, comparator cohort. Analysts confirm, escalate, or dismiss with a written rationale.
Escalation paths are defined per customer institutional context: audit chamber, donor agency, enterprise compliance. The platform never auto-routes a finding to a binding decision-maker without analyst sign-off.
No fully-automated decision-making — Article 14 of the EU AI Act respected by design.
Every flag, review action, and rationale is hash-chained into an append-only audit log, with the chain enforced at database-trigger level rather than at the application layer. Customers can export the full evidence trail per case as a signed DOCX/PDF pack — admissible in formal procurement disputes.
The explainability layer attaches to every ML score: SHAP-style top features plus human-readable rationale that an analyst, regulator, or bidder can read without ML literacy.
Article 12 (logging) + Article 13 (explainability) of the EU AI Act both addressed in the data model, not as an afterthought.
Pattern detection uses classical ML: gradient-boosted trees for risk scoring, isolation forests for outlier detection on bid-value distributions, graph algorithms for bidder-network and UBO-overlap analysis. The LLM layer (Claude Sonnet 4.5 and 4.7) is reserved for tasks where language understanding is the core requirement — translation, document-metadata extraction, and human-readable rationale on each flag.
Models are versioned with the tender release they scored. Retrospective re-scoring is possible — when a new collusion pattern is added, historical tenders are re-evaluated against it. All model versions and weights are referenced in the audit log, so a regulator can reproduce any historical score from the raw data and a published model card.
Model card per release available on request. Quarterly review with the customer's data-science team included in the Cloud Annual tier.
Three criteria, applied to every flag before it leaves the analyst queue:
Traceable — every claim points to a specific source document with cryptographic hash and retrieval timestamp.
Reproducible — any third-party reviewer with the same data and model version arrives at the same score.
Falsifiable — every flag includes an explicit "this is wrong if X" condition, allowing the bidder or buyer to challenge.
"We do not surface speculation. If we cannot point to a document, we do not raise a flag."
A 45-minute briefing covers the audit pipeline against your priority jurisdictions, the models we apply, and the evidence standard behind every finding.
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