Engineering AI must operate at the intersection of physical reality and regulatory precision. A dimension on a drawing isn't just a number; it's governed by a tolerance standard, linked to a datum reference frame, and must be manufactured within the constraints of a specific process. Generic language models and vector search fail here — they can't enforce geometrical relationships, can't query a CAD API, and can't explain their reasoning in terms an engineer can verify. We build AI that reads and reasons over engineering drawings with the depth of a senior engineer, integrating seamlessly with your PLM, CAD, and ERP systems, and deploying inside your sovereign perimeter.
Standard retrieval‑augmented generation (RAG) retrieves chunks by semantic similarity. It has no concept of a feature control frame, cannot understand that a dimension chain is incomplete without all reference datums, and can't flag that a material specification conflicts with the tolerance class. In engineering, a hallucinated measurement or missed constraint isn't an inconvenience — it's a safety risk.
Our approach combines three core capabilities from Power Consultancy's deep‑tech practice: Neuro‑Symbolic AI – neural networks detect symbols, lines, and regions; symbolic logic enforces ISO/ASME rules, material constraints, and tolerance stack‑up equations. Ontology Graph RAG – knowledge about part families, manufacturing processes, and standards is structured in a knowledge graph, enabling full traceability. Multi‑Sensor Fusion – when inspection data (CMM reports, laser scans) is available alongside the drawing, the system fuses multiple modalities to catch deviations that would be missed by reviewing the drawing alone.
The system is built as a multi‑agent cognitive architecture with a shared blackboard (Redis + Postgres + Neo4j).
Accepts PDF, TIFF, scanned drawings, or CAD‑native formats. Deskew, contrast normalization, resolution upscaling to 4096×4096. Returns a confidence score; low scores trigger a re‑scan request.
PDF / TIFF / CADGPT‑4V or fine‑tuned Donut segments sheet into JSON topology: title block, BOM table, views, symbol candidates, notes. Low‑confidence regions (<0.72) are re‑routed.
LayoutLMv3 / YOLOv8Queries Onshape, Autodesk, or PDM by drawing number + revision, retrieves 3D model as glTF/STEP AP242, overlays on 2D via ORB+homography. Fallback to neural 3D reconstruction.
Onshape / SolidWorksUnknown symbols auto‑cropped and queried via multimodal search (SerpAPI+Vision). Cross‑encoder VLM ranks candidates, returns ISO/ASME IDs with confidence, cached in Milvus.
Milvus vector DBTolerance Stack‑up Analyzer (sympy), Manufacturability Advisor (DFM rules), Revision Delta Agent (property graph diff). Each returns confidence and formal reasoning traces.
sympy · graph diffWhen agents disagree (e.g., Valuation pass vs. Manufacturing fail), council applies organisation‑specific weights, documents dissent, and seals output with SHA‑256.
Immutable audit trailThese failure modes are not theoretical. They recur in every deployment of standard RAG or LLM‑only systems on technical drawings.
Each domain demands a different balance of tolerance analysis, revision control, regulatory compliance, and manufacturability feedback.
Automated verification of maintenance drawings against current revision baselines; GD&T compliance to ASME Y14.5; ITAR‑compliant air‑gap deployment.
Cross‑referencing construction drawings with structural analysis models; detecting discrepancies between architectural and MEP sheets.
Supplier drawing audits; BOM cross‑validation across multiple part variants; tolerance chain analysis for safety‑critical components.
P&ID validation; automatic extraction of instrument lists and line lists; revision comparison across decades‑old drawing sets.
Design history file (DHF) compliance; full traceability from drawing to manufacturing record; FDA 21 CFR Part 11 audit support.
DFM feedback loops combining drawing review with actual machine capabilities; automated pre‑production drawing release gates.
Proprietary drawings, 3D models, and manufacturing know‑how are your most valuable assets. Vendor‑hosted AI requires you to upload them to third‑party infrastructure. We do the opposite: the entire system deploys inside your environment, under your encryption keys, with optional air‑gapped operation.
We work with a small number of engineering organisations at any given time. Engagements begin with a focused technical conversation — no sales process, no pitch deck, no obligation. Tell us about your drawing types, your review pain points, your existing toolchain, and what you've already tried. If there's a genuine fit, we'll both know quickly. If not, we will tell you honestly and help you understand what kind of partner would serve you better.
All engagements are under NDA as standard. Selective client partnerships.
Phase 1 – Drawing Inventory & Audit – We analyse a representative sample of your drawings. Phase 2 – Proof of Concept – On your infrastructure, using your drawings. Phase 3 – Knowledge Graph & Agent Customisation – Tailored to your parts, processes, and decision thresholds. Phase 4 – On‑premise Deployment – Integrated with your PLM/CAD and hardened for your security requirements. Phase 5 – Knowledge Transfer – Your team trained, ongoing model updates.
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