20+
AI Apps Built
95%+
Extraction Accuracy
1M+
Documents Processed
24h
Support Response
The Challenge
Your business runs on documents — contracts, invoices, reports, emails, policies, applications, and correspondence. But 80% of this data is trapped in unstructured formats: PDFs, Word files, scanned images, and emails. Finding specific information means reading through entire documents or relying on basic keyword search that misses context. Extracting data for analysis requires manual reading and re-typing. Answering questions about your document corpus — "What are our standard payment terms across all vendor contracts?" — requires hours of manual research. This is exactly the problem large language models were built to solve.
What I Offer
I build custom LangChain-powered document processing applications that use large language models (LLMs) to read, understand, and extract information from your documents with human-level comprehension. LangChain is the leading framework for building LLM applications, providing the tools to chain together document loading, text splitting, embedding, retrieval, and generation into production-ready pipelines.
Whether you need to extract specific fields from thousands of invoices, classify incoming documents by type and urgency, summarise lengthy reports, or build a Q&A system over your entire document library, I build solutions that are accurate, fast, and cost-effective.
All solutions can run against OpenAI, Anthropic, or open-source models depending on your security, cost, and performance requirements.
Intelligent Data Extraction
Extract specific fields from invoices, contracts, applications, and forms u2014 handling varied formats, layouts, and terminology with LLM comprehension.
Document Classification
Automatically sort incoming documents by type, department, urgency, and required action. Route each document to the right workflow.
Summarisation
Generate concise, accurate summaries of lengthy documents u2014 contracts, reports, research papers u2014 at any level of detail you specify.
Q&A Over Documents
Ask natural language questions about your document library and get accurate answers with source citations. Like having a research assistant who has read everything.
Comparison and Analysis
Compare terms across contracts, identify inconsistencies between documents, and flag deviations from standard templates or policies.
Structured Output Generation
Transform unstructured documents into structured data u2014 JSON, CSV, database records u2014 ready for analysis, reporting, or system integration.
How LangChain Transforms Document Processing
Traditional document processing tools use rigid templates and rules — they work when documents follow an exact format and break when anything changes. A new vendor sends invoices in a slightly different layout, and your extraction rules fail. A contract uses different terminology for the same concepts, and your keyword search misses it. Rule-based systems are brittle by nature.
LangChain-powered document processing uses large language models that understand documents the way a human does — reading context, interpreting meaning, and handling variation. An LLM does not need the invoice total to be in a specific cell or the contract clause to use specific words. It understands what an invoice total is and what a termination clause means, regardless of how the document is formatted.
Document Processing Solutions I Build
Intelligent Invoice and Receipt Processing
Accounts payable teams process hundreds of invoices monthly, each requiring manual extraction of vendor name, invoice number, line items, totals, tax amounts, and payment terms. Different vendors use different formats, layouts, and terminology. I build LangChain pipelines that process invoices from any vendor in any format — PDFs, scanned images (via OCR), or digital documents. The pipeline extracts all required fields with 95%+ accuracy, validates the data against your vendor database and GL codes, and outputs structured records ready for import into your accounting system.
Contract Analysis and Extraction
Legal and procurement teams spend enormous time reviewing contracts — finding specific clauses, comparing terms across agreements, and ensuring compliance with internal policies. I build LangChain applications that extract key terms from contracts (parties, dates, payment terms, termination clauses, liability limits, confidentiality provisions), compare them against your standard terms, flag deviations, and generate structured summaries. Instead of reading a 40-page contract, your team reviews a one-page extraction with links to the relevant sections.
Document Q&A Systems (RAG)
Retrieval-Augmented Generation (RAG) is one of the most powerful applications of LangChain. I build Q&A systems where users ask natural language questions and receive accurate answers sourced from your document library. "What are the warranty terms in our contract with Supplier X?" "Which of our policies cover remote work?" "What did the Q3 board report say about expansion plans?" The system retrieves relevant document sections, generates an accurate answer, and cites the source documents — all in seconds.
The key to a high-quality RAG system is the retrieval pipeline. I invest significant effort in chunking strategy (how documents are split), embedding model selection (how chunks are represented), and retrieval tuning (how relevant chunks are found). A poorly built RAG system gives wrong answers confidently. A well-built one gives accurate answers with source citations.
Document Classification and Routing
Many businesses receive high volumes of mixed document types — emails with attachments, uploaded forms, scanned correspondence. Manually sorting and routing these documents wastes staff time. I build classification pipelines that identify the document type (invoice, contract, application, complaint, correspondence), extract key attributes (urgency, department, client), and route the document to the appropriate workflow or team member automatically.
Report Summarisation
Long reports — research papers, financial filings, regulatory documents, meeting transcripts — contain valuable information buried in pages of text. I build summarisation pipelines that generate concise summaries at the level of detail you specify. An executive might want a 3-sentence overview. A analyst might want a structured summary with key findings, methodology, and conclusions. The same LangChain pipeline can generate both from the same source document.
Technical Architecture
A production LangChain document processing system typically includes these components:
- Document Loaders: Handle PDFs, Word documents, Excel files, images (via OCR), emails, and web pages
- Text Splitters: Chunk documents intelligently — by section, paragraph, or semantic meaning — preserving context
- Embedding Models: Convert text chunks into vector representations for efficient similarity search
- Vector Stores: Store and search embeddings using ChromaDB, Pinecone, or pgvector
- Retrieval Chains: Find the most relevant chunks for a given query or extraction task
- LLM Chains: Process retrieved context through carefully designed prompts to generate accurate outputs
- Output Parsers: Validate and structure LLM outputs into consistent formats
Cost Considerations
LLM API costs are a real consideration for document processing. I optimise costs through smart architecture: using cheaper embedding models for retrieval, caching repeated queries, processing in batches, and selecting the right LLM for each task. Simple classification might use a fast, cheap model. Complex extraction might use a more capable (and expensive) model. The result is a system that delivers high accuracy at a fraction of what a naive implementation would cost.
Ready to transform your document processing? Contact me to discuss your documents and requirements, or book a call for a free assessment.
Why Choose Me
Production-Grade Pipelines
I build document AI systems that work reliably at scale u2014 with proper chunking strategies, embedding optimisation, retrieval tuning, and output validation. Not just demos, but production systems processing thousands of documents.
Cost-Optimised Architecture
LLM costs can spiral quickly with document processing. I architect solutions that minimise token usage through smart chunking, caching, and model selection u2014 using expensive models only where accuracy requires it.
Accuracy Validation
Every extraction and classification pipeline includes validation steps and confidence scoring. Low-confidence results are flagged for human review rather than accepted blindly.
My Process
A proven approach from concept to delivery.
Document Assessment
I review your document types, volumes, formats, and extraction requirements. I test sample documents to establish baseline accuracy expectations.
Pipeline Design
I design the LangChain pipeline u2014 document loading, text splitting strategy, embedding model selection, retrieval method, and LLM prompting u2014 optimised for your specific documents.
Build and Validate
I build the pipeline, test against a validation set of manually-verified documents, and iterate until accuracy meets your requirements.
Deploy and Monitor
I deploy the system, set up accuracy monitoring, and provide documentation and training for your team.
Technologies & Tools
Results That Speak
Client project: A property management company was manually extracting data from 500+ lease agreements, each with different formats and terms. Staff spent 40+ hours per month on data entry and could not easily answer questions about their lease portfolio.
Result: The LangChain pipeline extracts 25 key fields from each lease with 96% accuracy, processes new leases in under 30 seconds, and powers a Q&A interface that answers portfolio questions instantly. Monthly data entry time dropped from 40 hours to 3 hours (reviewing flagged low-confidence extractions).
Frequently Asked Questions
LangChain can process PDFs, Word documents, Excel files, PowerPoint, plain text, HTML, emails, and images (via OCR integration). Scanned documents require an OCR step to extract text before LLM processing. I handle the entire pipeline including OCR integration.
Accuracy depends on the document type and extraction complexity. For structured documents like invoices, 95-98% accuracy is typical. For unstructured documents like contracts, 90-95% accuracy on key field extraction. I always include validation and confidence scoring, with low-confidence results flagged for human review.
I can architect solutions using private LLM deployments (Azure OpenAI, self-hosted models) where your data never leaves your infrastructure. For cloud-based deployments, OpenAI and Anthropic both provide enterprise data handling agreements. I discuss security requirements during the assessment phase and recommend the appropriate architecture.
Typical costs range from $0.01-0.10 per document depending on size, complexity, and the LLM model used. For high-volume processing, I optimise with caching, batching, and model selection to minimise costs. This is dramatically cheaper than manual processing, which costs $2-10 per document in staff time.
Yes. The LangChain pipeline can be triggered by new documents arriving in SharePoint, Google Drive, Dropbox, S3, or any document management system. Processed data can be pushed to your database, CRM, ERP, or any system with an API.
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