Scale & Optimize
AI Document Intelligence
Your documents contain valuable knowledge trapped in PDFs, Word files, and scanned images. We build systems that extract, understand, and make that knowledge searchable. Ask questions in plain English. Get accurate answers from your own data.
The Technology
How AI Document Intelligence Works
We use Retrieval-Augmented Generation (RAG) to ground AI responses in your actual data. Instead of relying on general knowledge, the AI retrieves relevant content from your documents before generating answers.
1. Ingest
Load documents, extract text, and parse structure from PDFs, Word, images, and more.
2. Embed
Convert text chunks into vector embeddings that capture semantic meaning.
3. Retrieve
Find the most relevant content using semantic similarity search.
4. Generate
LLM produces accurate answers grounded in your retrieved content.
Why RAG Matters
Standard LLMs hallucinate. They make things up when they do not know the answer. RAG reduces hallucinations by grounding responses in your actual documents. The AI cites sources. You can verify answers. Trust increases.
What We Build
Document Intelligence Capabilities
From raw documents to intelligent answers. Each component of the pipeline matters.
Document Parsing & OCR
Transform raw files into structured text. PDFs, Word docs, scanned images, handwritten notes. We preserve headings, tables, lists, and relationships.
- PDF text extraction with layout preservation
- OCR for scanned documents and images
- Table and form field extraction
Semantic Chunking
Break documents into retrievable pieces that fit LLM context windows. Smart segmentation preserves meaning across chunk boundaries.
- Paragraph and section-aware splitting
- Overlapping chunks for context continuity
- Hierarchical parent-child relationships
Vector Embeddings & Storage
Convert text into numerical vectors that capture semantic meaning. Store in vector databases for fast similarity search.
- OpenAI, Cohere, or local embedding models
- Pinecone, Weaviate, or pgvector storage
- Millisecond similarity search at scale
Metadata & Context Enrichment
Enrich documents with tags, categories, and structured metadata. Improve retrieval precision with contextual information.
- Auto-tagging and classification
- Document date, author, source tracking
- Metadata filters for precise retrieval
Advanced Techniques
Production-Grade RAG
Basic RAG is a starting point. Production systems need additional techniques for accuracy, performance, and reliability.
Reranking
Initial retrieval returns candidates. A reranker model scores them for relevance and reorders results before passing to the LLM. Significant accuracy improvement.
Hybrid Search
Combine semantic embeddings with keyword search. Some queries need exact term matching. Hybrid approaches handle both semantic understanding and precise terminology.
Query Expansion
Rewrite user queries to improve retrieval. Generate hypothetical answers. Expand acronyms. Add synonyms. Better queries mean better results.
Evaluation & Monitoring
Measure retrieval precision and recall. Track answer accuracy. Monitor for drift. Continuous improvement based on real usage.
Incremental Updates
Add new documents without rebuilding everything. Handle document updates and deletions. Keep your knowledge base current.
Access Control
Not everyone should see everything. Row-level security on documents. Users only retrieve content they are authorized to access.
Use Cases
What You Can Build
Document intelligence powers a wide range of applications. Here are the most common use cases we help clients build.
Knowledge Base Q&A
Internal wikis, policy documents, and procedures made searchable. Employees ask questions in plain language, get accurate answers with citations.
Customer Support AI
Support portals powered by your documentation. Customers get instant answers. Agents get suggested responses. Reduce ticket volume.
Contract Intelligence
Extract key terms, dates, and obligations from contracts. Search across agreements. Identify risks and opportunities.
Research & Analysis
Analyze research papers, reports, and market data. Surface insights across large document collections. Accelerate literature review.
Compliance Knowledge Hub
Regulations, standards, and audit documentation in one searchable system. Stay compliant with instant access to relevant requirements.
Document Summarization
Generate executive summaries from lengthy documents. Extract key points. Save hours of reading time for busy stakeholders.
Technologies
Our Tech Stack
We use proven technologies for each layer of the document intelligence pipeline. The specific choices depend on your requirements for scale, security, and deployment.
On-premises options available for sensitive data. Cloud-native options for scalability. We help you choose the right architecture.
Free Assessment
Is Your Organization AI-Ready?
Document intelligence requires good data foundations. Take our assessment to understand your readiness.
Ready to Unlock Your Documents?
Tell us about your document challenges. We will help you design a solution that turns files into answers.
Start Your Project