Use case
Keep your quoting and manufacturing knowledge — no matter who leaves.
Capture tribal expertise hidden in PDFs, scans, and archived drawings. Werk24 structures PMI, notes, and decisions so your teams can search, reuse, and automate critical know-how.
Why tribal knowledge is your biggest hidden risk
Experienced engineers carry decades of decision context in their heads — which tolerances matter for this machine, why a material was switched on that part family, how to cost a tricky weld. When they retire or move on, that knowledge disappears. What remains are PDFs, scans, and paper archives that no one can search, cross-reference, or feed into downstream systems.
What it captures — from paper to searchable knowledge
- PMI & tolerances — dimensions, GD&T, threads, surface finishes with full context.
- Process notes & decisions — free-text annotations, revision rationale, and operation hints.
- Similar-job discovery — find past parts with matching features, materials, or tolerance profiles.
- Title-block metadata — drawing number, revision, material, standard, and customer references.
Every extraction includes confidence scores. Low-confidence items route to a human review queue, so the knowledge base stays reliable.
How it works
Connect your archive, extract knowledge, and make it available across teams.
Connect your archive You
Point Werk24 at your drawing archive — file shares, PLM vaults, or mailbox inboxes. We ingest PDFs, scans, and TIFF images.
Extract & structure Werk24
Werk24 reads each drawing and returns structured JSON: dimensions, tolerances, GD&T, materials, notes, and process hints — normalized and unit-aware.
Build your knowledge graph Werk24
Link extracted data across drawings to surface similar jobs, recurring part families, and shared decision patterns. Store in your PLM, ERP, or data warehouse.
Integrate & automate You
Feed the knowledge graph into search tools, AI copilots, onboarding workflows, or trigger alerts when risky specs appear in new RFQs.
Outcomes
Preserve institutional knowledge and make it actionable across teams.
Searchable institutional memory
Find past decisions, routings, and tooling strategies by feature, material, or tolerance — not by drawing number.
Faster onboarding
New engineers get context on past work without asking colleagues who may have moved on.
Reuse what works
Surface similar jobs automatically so teams reuse proven routings, suppliers, and costing models.
Proactive risk alerts
Trigger automations when rare materials, tight tolerances, or historical quality issues appear in new drawings.
Metrics that matter
Track whether institutional knowledge is being captured, found, and reused.
- Archive coverage: % of legacy drawings structured and searchable.
- Reuse rate: How often teams find and reference past jobs.
- Onboarding time: Days until new engineers are productive on quoting or planning.
- Risk detection: Number of proactive alerts triggered before production.
FAQ
How far back can we go with legacy drawings?
Werk24 reads scans as low as 200 dpi. If a drawing is legible to a human engineer, we can usually extract structured data from it — including hand-drawn prints from the 1980s.
How does similar-job discovery work?
Extracted features (dimensions, materials, tolerances, threads) create a searchable fingerprint for each drawing. Query by feature ranges, material class, or tolerance profile to find matches.
Where does the structured data live?
In your systems — PLM, ERP, data warehouse, or a dedicated search index. Werk24 extracts and delivers; you control storage and access.
Can this feed AI or analytics tools?
Yes. Structured JSON output plugs directly into BI dashboards, ML pipelines, or AI copilots. The knowledge graph is the foundation for any data-driven initiative on your drawing archive.