Automatically interpreting technical drawings: how a Belgian manufacturer sped up its quoting and calculation process with AI
Reading drawings manually every day, retyping parameters, calculating cost. A bottleneck every custom-build manufacturer recognizes. AI can solve it, but only if it actually works on industrial drawings.
Summary
A Belgian specialist in technical fastening components had a challenge most manufacturers will recognize. Custom design requests were eating days of expert time. Reading drawings, extracting parameters, calculating cost. Repetitive, error-prone, and slow.
Litecycle ran it the way we always do: start pragmatic and build a focused PoC in a short sprint that genuinely validates the core technology. With positive results, we moved straight into production and integrated it with internal systems and processes.
The challenge
Custom design requests come in every day. Each one looks different. Emails with text, sketches, technical drawings, no fixed format. An expert reads it manually, transfers it into spreadsheets, calculates feasibility and cost.
Four bottlenecks were stacking up. Manual interpretation of inconsistent documents. Time-heavy calculation. Error-prone extraction of technical parameters, and product data in ERP and DMS that doesn’t connect automatically.
Experts spend their days on interpretation work instead of on the complex custom problems where their expertise actually moves the needle.
The approach: start pragmatic, validate fast, deliver value immediately
Our methodology runs in three phases that flow into each other without seams.
Pragmatic start. No half-day workshops on vision and strategy. Just a short kick-off to nail down scope, surface edge cases, and lock delivery criteria. What does the PoC need to prove to earn the next phase? Which drawings make up the test dataset? When do we call it a success?
High-output PoC. A short, intense build sprint. We deliberately chose an agentic architecture with three bounded AI agents.
An extraction agent that reads technical parameters from drawings using a vision model. An interpretation agent that tests that data against the company’s internal engineering rules and reference data from ERP and DMS. And a human-in-the-loop interface where an expert validates the output, with a per-field accuracy score so attention goes to the uncertain parameters. End of the PoC: a working prototype, validated against criteria locked upfront, and a grounded go/no-go moment.
Straight to production. The PoC serves as the foundation for the production version. Migration to client infrastructure, security, role-based access, monitoring, user interface, hosting. V1.0 in production, with real users on real requests.
That cadence is the whole point. Most consultancies stall between PoC and production because the PoC team gets disbanded, the production team has to ramp up from scratch, and another scoping phase wedges itself in. We keep the same team from PoC through production, with the expertise to see it through.
The result
A working system that automatically interprets technical drawings, validates extracted parameters against internal engineering rules, and feeds the output into the existing calculation process. Not as a black box, but with a validation interface where the expert sees, field by field, how confident the system is.
The difference for the calculation experts: instead of starting each request from zero, they get a pre-filled, validated baseline. Their attention goes to the spots where the system is uncertain, and to the complex custom decisions where their expertise actually counts.
The impact
Faster quote turnaround. The reading work that used to take days now runs in the background. Experts validate instead of interpret.
Expertise spent where it matters. Scarce calculation capacity goes to the genuinely complex, non-routine requests where custom is actually custom. The repetitive interpretation moves to the system.
More consistent outcomes. An AI system recognizes the same parameter the same way, no matter who created the drawing or what format it arrives in.
In-house AI maturity. The internal team now understands what agentic architectures are, where vision models are strong and weak, and how to scope AI projects themselves. That knowledge is critical.
Why this works for Belgian manufacturers
Most Belgian SMEs are AI-curious but AI-cautious. They see what’s possible, but they also know that the gap between a demo and working on your own data, in your own workflow, with your own edge cases is enormous.
Our approach bridges that gap. Start pragmatic. Build a working PoC that genuinely tests, not just shows. Green light, straight to production with the same team. Red light, a validated answer that lets management decide, with confidence, not to proceed.
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