Case Study #1Contract Document AutomationEnd-to-End AI Architecture & Engineering

Contract Drafting Assistant

From 60 minutes to 2: AI document extraction and centralized drafting with side-by-side human validation.

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Manual drafting takes about 60 minutes; with AI it takes about 2 minutes (96.7% faster), saving ~145 hours per month (projected) at 150 documents per month.

IMPACT SIMULATION · V-1

60 min → ±2 min

Both sides run on the same clock. Then the manual side is fast-forwarded to the end.

Manual

Reading PDF & retyping field by field

With AIDone in 02:00

Vision OCR extraction + side-by-side verification

Time per step: manual vs AI

Select template & prepare input documents5 min → 15 s
Read source multi-page PDFs15 min → 35 s
Extract and transfer data to draft30 min → 0 s (auto-filled)
Validate financial totals & bank accounts10 min → 40 s
Finalize & apply custom clauses10 min → 30 s
96.7% faster
~145 hours saved / monthprojected
150 docs / mo

Business Impact

Figures labeled 'projected' or 'estimated' reflect business model targets rather than realized metrics.
measured
±60 min→±2 min

Drafting turnaround time per document

96.7% faster (±30× acceleration)

estimated
12–20%→<1%

Initial draft typo rate (letters, amounts, accounts)

Achieved via side-by-side verification interface

projected
150 h/mo→5 h/mo

Monthly staff hours spent drafting

~145 hours/month saved (equivalent to 1 FTE) based on 150 docs/mo

measured
Dispersed local files→100% centralized

Template standardization

measured
None→100% logged

Operational audit trail

The Problem

Procurement teams previously drafted formal contracts and appointment letters by manually parsing through dozens of PDF pages—official memos, vendor bids, and meeting minutes.

Staff had to hunt down scattered clauses, manually recalculate financial totals, verify vendor bank accounts, retype letter numbers, and translate numbers into Indonesian currency words (kalimat terbilang). The entire manual pipeline was slow, tedious, and prone to costly transcription errors on critical legal documents.

The Solution

We architected a streamlined, human-in-the-loop pipeline:

  1. Source Document Ingestion: Staff upload source PDFs directly into the platform.
  2. Vision & LLM Extraction: An orchestration pipeline powered by vision OCR and structured LLM extractors parses all critical entities (vendor credentials, letter identifiers, breakdown of pricing, and bank accounts).
  3. Side-by-Side Verification: Users review extracted parameters side-by-side with the original PDF on a dual-pane interface before committing any changes.
  4. Automated DOCX Assembly: Standardized DOCX drafts are generated dynamically from centralized enterprise templates.
  5. In-Browser Document Editing & Audit: Generated drafts can be polished in an integrated online document editor, with every validation and modification tracked in an audit log.

Business Impact

Procurement staff previously spent ~60 minutes manually cross-checking multi-page PDFs to retype vendor details, bank accounts, and currency words, resulting in typographical errors on 12–20% of initial drafts. Compressing turnaround to ~2 minutes through side-by-side verification and centralized templates is projected to save ~145 hours of labor per month (assuming 150 documents monthly, equivalent to 1 FTE, projected) while ensuring every modification is captured in an operational audit trail.

Engineering Principle

Human-in-the-Loop by Design

AI should never write final legal commitments autonomously.

The significant drop in errors did not stem solely from raw LLM benchmark accuracy—it was driven by UX that forces split-screen verification. By showing the extracted values next to the source PDF, humans verify in 40 seconds what previously took 10 minutes of manual checking.

Behind the Decision

Behind the Decision

We kept the AI out of the final document on purpose. The AI only extracts the entities; people verify them side by side with the source PDF, and the DOCX draft is built from a centralized template. Errors dropped because of that verification step, not only because of a smarter model. Ask me how we designed it.

Interested in shipping similar systems?

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