Back to Projects

Case file

AI-Powered Document Analysis and Retrieval System

A system that extracts, embeds, and intelligently answers complex questions about procurement and project documents at scale.

Client
Swedish Construction & Procurement
Read time
2 min

Primary solution

AI Workflows & Automation

This project is grouped under the buyer-facing solution area it most directly supports.

Capabilities in play

AI implementationRetrieval systemsAutomation

Snapshot

Applied system demo

Narrative, metrics, and interaction packaged into a compact case-study page.

Surfaces

MDX storytelling, embedded demos, and reusable product communication patterns.

Sep 30, 20252 min readAI Workflows & AutomationAI AnalysisDocument ProcessingRAGVector Search
AI-Powered Document Analysis and Retrieval System

Continue through this solution area

This case file sits inside AI Workflows & Automation.

Use the solution page to see how this project connects to related systems, capability patterns, and supporting editorial work.

Evaluating procurement documents (RFPs, tender specifications, project requirements) is a manual, time-consuming task. Project managers and bid teams wade through 50-page PDFs looking for key requirements, constraints, and risk factors—a process that takes days and is error-prone. A Swedish construction firm needed to turn this into a searchable, answerable knowledge base.

Problem

RFP documents contain dense, unstructured information buried in administrative requirements, technical specs, and commercial terms. Stakeholders need answers like "What are the must-have deliverables?" or "What are the payment terms?" but finding them means reading hundreds of pages. Key requirements are missed, risk assessments are incomplete, and bid teams lose time to manual document review instead of strategy and pricing.

Solution architecture

Multi-format document ingestion and chunking

The platform accepts PDFs, Word documents, and Excel files. Documents are parsed, chunked into semantic segments (500–1000 characters with context overlap), and stored with metadata (source, page number, section).

All chunks are embedded using a dense 768-dimensional embedding model. When a user asks a question, the question is also embedded, and the top-50 semantically similar chunks are retrieved via vector search and reranked to the top-15 by relevance.

Multi-model orchestration with streaming

A lightweight model (Gemini 2.5 Flash) structures user questions into component parts and plans search strategy. A more capable model (Gemini 2.5 Pro) synthesizes final answers from retrieved context, citing exact page numbers and quotes. Results stream back to the user in real-time.

01

Answer latency

< 30 seconds

02

Citation accuracy

98%

03

Document coverage

Multi-format (PDF, DOCX, XLSX)

04

Questions answerable

95%+ of user queries

Scheduled batch analysis

A Convex scheduler runs a 5-phase analysis pipeline: question structuring, document embedding, keyword planning (via Vertex AI batch API), retrieval and reranking, and answer synthesis. Large question sets are micro-batched to prevent timeout while maintaining efficiency.

Insight

Audit trail for compliance

Every question, retrieved chunk, and answer is logged with timestamps and source attribution, creating a complete audit trail for compliance and bid protest review.

Outcome

Bid teams now have instant answers to common RFP questions, with full source attribution. What used to take days of manual document review now takes minutes. Bid managers can focus on strategy and pricing instead of document archaeology. Risk assessment is faster and more complete, and no critical requirement is missed.

Related case files

More work in AI Workflows & Automation.

Open solution page

Related articles

Supporting reading from the same solution area.

All articles
Book an intro to scope the bottleneck, workflow, or architecture issue.Qungs builds custom software, automation systems, and applied-AI interfaces.Important updates or operational notes can be edited in src/lib/site.ts.Book an intro to scope the bottleneck, workflow, or architecture issue.Qungs builds custom software, automation systems, and applied-AI interfaces.Important updates or operational notes can be edited in src/lib/site.ts.