You send raw data. Paradigm returns a versioned dataset with cryptographic provenance, an EU AI Act evidence pack, and verification recipes that run against tools we don’t control.
We hold your data for the processing window, then delete it.
When did the training data change? What was the model’s confidence on that decision? Can you replay last Tuesday’s inference pipeline exactly?
If a regulator, auditor, or client asks — silence is expensive.
We build the infrastructure that makes the answers automatic.
Custody bounded by contract · raw inputs deleted within 30 days of acceptance
A dataset construction and verification instrument. It validates in six tiers, builds provenance in four layers (SHA-256, Merkle, C2PA, SLSA), signs with OMS-compatible signatures, and exports EU AI Act Articles 9–15 evidence.
We operate it. Customers receive the output, and the recipes to check that output without asking us.
Article 50 applies from 2 December 2026. Annex III high-risk obligations apply from 2 December 2027. Both dates are confirmed.
Provenance cannot be backfilled. Records that were not captured when a dataset was built cannot be reconstructed at audit time. Compliance work on training data therefore starts at dataset construction, not at conformity assessment.
Vision-transformer-based computer vision for automated laptop quality assessment. A defect taxonomy derived from manufacturer warranty policy (Customer Induced Damage). PASS/FAIL classification with explicit confidence scoring, abstention threshold, and evidence capture.
Written problem definition. Constraints. Risk profile.
Minimal verifiable pilot. Evaluation criteria agreed upfront.
Controlled deployment. Deterministic pipelines from day one.
Measure against agreed metrics. Evidence, not assertion.
Scale what passes. Iterate or discard what doesn’t.
Written problem definition. Constraints. Risk profile.
Minimal verifiable pilot. Evaluation criteria agreed upfront.
Controlled deployment. Deterministic pipelines from day one.
Measure against agreed metrics. Evidence, not assertion.
Scale what passes. Iterate or discard what doesn’t.
Engagement model: Written scope, milestones, acceptance criteria, and verification plan are agreed before work begins. Four tiers define scope and price; every tier produces the same five artefacts.
We work with teams in regulated industries — logistics, manufacturing, finance, healthcare — who need AI systems that survive audit.
Static Signal LTD
Company No. 16826101
71–75 Shelton Street, Covent Garden
London WC2H 9JQ