Document & Quantity Takeoff Automation AI
We'd automate your manual document and drawing reading workflows with a hybrid computer-vision plus LLM pipeline — designed around your documents, with accuracy, citations, and a human review UI built in.
Construction tender files, accounting documents, production quality reports, insurance policies — every one of them still requires a human to read, type, and verify the data trapped inside them. That work takes weeks, the error rate is high, and the repetition kills team morale. Wrong numbers flow straight into bonus calculations, payments, cost accounting, and customer invoices. Modern document AI ends that cycle: a hybrid pipeline of computer vision and LLMs can read a tender drawing, an invoice, or a scanned contract faster and more consistently than a human. The model itself is not what matters — what makes this work in production is the labelling discipline, the validation layer, and the human review UI built around it.
The Business Problems We Solve with Document AI
Construction tender takeoff still consumes 2 to 3 engineers for 1 to 2 weeks per project — every drawing is hand-counted, every quantity is hand-typed, and the deadline pressure produces costly errors.
Accounting teams type invoice fields one by one into the ERP — supplier name, tax number, line items, due date — and the error rate quietly corrupts payment runs and supplier reconciliation.
Insurance and finance back-offices spend full days reading policy PDFs and contract appendices to extract coverage limits, dates, and counterparty data; the work does not scale with portfolio growth.
Production quality reports arrive as scanned paper or photo; transcribing them into the QMS is a daily cost, and any delay blocks downstream batch release.
Existing OCR tools handle clean text but break on mixed layouts, handwritten notes, drawings, and stamps — the exact documents that matter most in real business workflows.
Our Approach
We recommend a hybrid AI architecture built on three layers that compensate for each other's weaknesses. Computer vision (YOLO v8, Detectron2, or a comparable model — chosen for your data) detects the visual elements: regions, lines, stamps, signatures, table boundaries, drawing primitives. OCR (Tesseract, Google Vision API, Azure Form Recognizer, or similar) extracts the raw text — typed and handwritten. An LLM (GPT-4 Vision, Claude with vision, or an open-source vision-language model if privacy requires) takes the raw extracted output and structures it into the target schema, flags anomalies, and produces a per-field confidence score. Around all three sits a validation layer of rule-based and ML checks that verifies the output against domain rules — VAT formulas, total-vs-line-items consistency, drawing-scale plausibility — and a human-in-the-loop UI that brings any low-confidence record to an operator with full context for one-click approval or correction.
A verified reference point we can build on: a construction-tender takeoff pipeline that compressed a process previously consuming 2 to 3 engineers for 1 to 2 weeks into seconds — a measured 1344x speedup on that project (full details in our case study). The same architectural skeleton — visual detection, OCR, LLM structuring, validation, human review — is the one we'd propose for invoice processing, insurance policy extraction, production quality report digitisation, and contract data capture. Only the labelled training data and the per-document validation rules change. We'd treat document AI as business infrastructure, not a one-off demo.
Process
Data Sampling
Manual labelling on 10 to 50 real documents from your archive. This is the system's first learning set; the labelling schema is co-designed with the domain expert, because a class defined wrong becomes the model's weakest seam.
POC Model
A working prototype in 2 to 3 weeks, with the core flow running end-to-end at 85 percent or better accuracy. The POC always runs on the customer's real documents — not stock benchmarks.
Fine-tuning
Wider training set, edge cases, layout variants, multi-language. Target accuracy 95 percent or higher; validation rules are tightened iteratively against measured failure modes.
Human Approval UI
A Next.js review interface where an operator approves low-confidence records in a single click, with the original document and the model's reasoning shown side by side. Throughput is the metric, not just accuracy.
Production Rollout
API integration with your ERP, CRM, or QMS; batch processing for archives; monitoring for drift, accuracy decay, and document-format changes. Alarms wire up before the first user touches the system.
Our Preferred Technology Stack
We typically reach for the following — adapted per project based on your document types and accuracy targets.
Sıkça Sorulan Sorular
Let's Build a POC for Your Documents
Book a 15-to-30-minute discovery call — free, no commitment. We learn your document workflow and tell you honestly where document AI will and will not deliver value.