Document AI & RAG
Document AI, RAG Assistants & Data Extraction
Turn contracts, policies and PDFs into answers and structured data your team can use — with sources you can check.
Discuss your projectCritical knowledge is locked in documents: contracts, policies, manuals, agreements, questionnaires and reports. Finding one clause or comparing hundreds of files takes hours of reading.
We build two kinds of document AI. RAG assistants let people ask questions in plain language and get answers grounded in your documents, with references. Extraction pipelines read large volumes of documents and pull out the exact fields you need into spreadsheets, databases or your CRM.
What we deliver
Chat with your documents
Secure RAG assistants over policies, contracts and knowledge bases, with source references and access control.
Data extraction at scale
Pipelines that extract dates, parties, amounts, clauses and custom fields from PDFs and Word files into Excel, databases or Salesforce.
Batch questions & comparison
Ask the same set of questions across hundreds of documents and get one comparable table of answers.
Classification & routing
Automatically classify incoming documents and emails and send them to the right team or process.
Search that understands meaning
Semantic search with embeddings and vector databases, using tools such as Watson Discovery, MongoDB vector search or Elasticsearch.
Related case studies
All case studiesLegal
AI Contract Intelligence Platform
A platform that ingests contracts, extracts key terms with large language models and lets users ask questions across documents using OpenAI, IBM watsonx and Watson Discovery.
Hospitality
AI Email Automation for Hospitality
An AI pipeline that reads incoming guest emails, understands the request using retrieval-augmented generation over the business's own documents and prepares answers.
Healthcare & Medical Research
Healthcare AI Survey Agent
Scoping, regulatory research and a working prototype of an AI agent that turns validated clinical questionnaires into natural conversations with patients.
How we work
A clear process, from idea to production
- 01
Discover
We learn your process, systems and goals, then agree on scope, priorities and success measures.
- 02
Design
Solution architecture, data model and user flows reviewed with your team before we build.
- 03
Build & test
Iterative delivery with regular demos, automated tests and a staging environment you can try.
- 04
Launch & support
Deployment, training, documentation and ongoing support or retainers after go-live.
- ✓ Reply within 1 business day
- ✓ Client confidentiality by default
- ✓ You own the code & documentation
- ✓ Fixed-scope or monthly retainer
FAQ
Frequently asked questions
How accurate is AI data extraction?
Accuracy depends on document quality and how well fields are defined. We define each field precisely, test on real samples, measure accuracy and add human review where it matters before scaling up.
Can the assistant show where an answer came from?
Yes. RAG assistants return the passages they used so users can verify every answer.
Can it handle scanned PDFs?
Yes. We add OCR and image processing so scanned documents can be read and processed like digital ones.
Where is our data stored?
Wherever your policies require: in your own cloud tenant, on enterprise model endpoints that don't train on your data, or with open models hosted under your control.
Other services
Contact
Let’s discuss your next solution.
Tell us what you want to automate, build, or improve. We’ll help you turn the idea into a practical business system.