Tool 01 · RFP Intake

Document Intelligence

Upload any RFP, SoW, or tender document — up to 400 pages. A²AI extracts the full document structure, identifies requirements, and flags uncertain items for consultant review.

AI Model
LayoutLMv3
Fine-tuned on
SAP RFPs · SoWs · Tenders
94.2%
Section F1
91.6%
Table Extraction
400pg
Max Document
Upload your RFP or tender document
PDF · DOCX · XLSX · TXT — drag & drop or click to browse

Live Demo

Extract Document Structure

Upload a document above, then click to analyse. The model extracts every section, identifies requirements, and assigns confidence scores. Low-confidence items are triaged for consultant review.

Structure Extraction
Upload a document to begin analysis
Requirements Identified
Requirements appear here after extraction

AI Architecture

LayoutLMv3 — Multimodal Document Understanding

LayoutLMv3 processes text, layout positions, and visual features simultaneously — enabling structure-aware parsing far beyond text-only models.

2D Layout Encoding
Each token receives both text and spatial position embeddings — x/y coordinates, width, height. This lets the model understand table structures, columns, and page layout natively.
SAP Domain Fine-tuning
Pre-trained on 500+ SAP RFPs, Statements of Work, and tender documents. Fine-tuned with SAP-specific vocabulary: modules, RICEFW types, delivery phases, and compliance terms.
Confidence Calibration
Platt scaling applied to raw model outputs produces well-calibrated probabilities. Items below 0.65 confidence are flagged for human triage — reducing false positives while capturing borderline requirements.
Why LayoutLMv3 over GPT-based extraction?
Generic LLMs hallucinate structure they don't see. LayoutLMv3 is deterministic, grounded to spatial positions, and produces traceable confidence scores per section — essential for defensible RFP analysis in client-facing scenarios.