Send a purchase order — any format, any industry. Our 7-stage pipeline reads it with Bedrock, matches every field against your catalog with a deterministic SAT classifier, and returns ERP-ready data with a trust score that means something.
GenAI reads the document. Snake cross-checks every field. The trust score is not AI-generated — it's mathematically computed.
Claude on Bedrock reads any document format — scanned PDFs, photos, handwritten notes. 7 stages: identify, analyze, extract, normalize, match, validate, route.
Every extracted field is cross-checked against your catalog. Snake exact (hash lookup) or Snake SAT (boolean classifier). Deterministic. Explainable. $0 per match.
Not AI confidence — mathematical certainty. 100% means every field matched a known entry. Drops instantly on unknown data. Auto-approve, review, or flag.
PDF, scan, or photo. Industry auto-detected. Snake matches against sample catalogs. Results in seconds.
LLMs hallucinate confidence. Snake computes it. The trust score is a weighted average of deterministic matches — not a probability, a fact.
Type a glass composition. Snake parses it live — no AI, no LLM, just deterministic matching.
Client recognized. Every article matched. Every field validated. The trust score is a contract, not an estimate. No model drift, no probabilistic decay.
Snake matched 25 part numbers in microseconds. No LLM needed for matching. Auto-approved, straight to ERP.
New client? Score drops. Unknown article? Score drops. Snake tells you exactly what's missing. After human review, add the data, retrain in 15 seconds, score jumps to 100%.
Traditional ML gives 85% confidence on a wrong answer. Snake gives 32% and tells you it doesn't know.
Step 1 gives you ERP-ready data today. Step 2 transforms your entire customer journey.
GenAI reads any document. Snake verifies every field. Trust score routes automatically.
The extraction becomes the foundation for an AI-native commercial layer.
Today, Bedrock VLM reads the document (~$0.05/doc). Our research shows that for structured industries, OCR + Snake's semantic parser can replace the VLM entirely — tokenizing raw text into glass compositions, part numbers, dimensions, and matching them to your catalog. No AI inference. Same trust score.
"4/16/4" → verre1: 4mm Float (#1004), intercalaire: Warm-edge 16mm (#99215), verre2: 4mm Float (#1004). 334ms. Zero LLM.
Send documents however you want. Get structured data wherever you need it.
Every path produces the same universal JSON. Same trust score. Same matching. The only difference is how the document arrives and where the result goes.
One EC2. Your VPC. Your data never leaves. Deploy in minutes, not months.
Self-contained AI extraction pipeline. Upload your catalog, send documents, get ERP-ready data. GenAI extraction + deterministic matching + trust scoring. Zero external dependencies beyond Bedrock.
Every component is an AWS service. Documents never leave the AWS boundary.
Haiku 4.5 + Sonnet 4.6. Vision + text. Multi-region failback.
Self-contained instances. FastAPI + gunicorn. One service, one boundary.
Models, archives, NDJSON data lake. Local-first, S3-fallback.
Email gateway. Forward a PO, get structured data by reply.
Articles, clients, extractions. Daily sync to S3 for analytics.
DNS for all subdomains. DKIM + MX for email delivery.
Scoped policies per service. Instance roles for Bedrock.
Every resource version-controlled. One deploy.sh per service.
100,000+ documents. 10 factories. Your industry is next.