Document ingestion and parsing
PDFs, scans, Office files, email archives and web pages turned into clean, chunked, metadata-rich text, with OCR and table extraction where needed.
RAG & Document AI · Canada
Answers from your documents, with the page behind every statement
Retrieval-augmented generation lets a model answer from your own documents instead of its training data. Done well, every answer cites its sources, respects who is allowed to see what, and handles questions whose answer is spread across several files. That is what we build, and it is what our own product Akurion does.
Why it matters
Why rag & document ai needs more than a demo
A basic chatbot over a vector database answers the easy questions and quietly gets the hard ones wrong. Real document work needs good parsing of tables and scans, hybrid search, re-ranking, entity and relationship extraction for cross-document questions, access control that mirrors your permissions, and citations precise enough to check.
What you get
- Ingestion pipeline for your document sources
- Search and answer service with citations
- Evaluation set built from your real questions
- Chat UI, API or integration into existing tools
What we deliver
Our rag & document ai services
Hybrid search and re-ranking
Vector, keyword and metadata search combined and re-ranked, tuned against real questions from your team rather than a generic benchmark.
Graph RAG for cross-document questions
Entities and relationships extracted into a knowledge graph so the system can follow links: from a supplier, to a lot, to a shipment, to a customer order.
Citations and answer verification
Every statement linked to the document and page it came from, with checks that flag answers the sources do not support.
Permissions and governance
Project and document-level access control, SSO, audit logs and data-retention rules, so people only get answers from what they are allowed to read.
Structured extraction
Fields, clauses, obligations, dates and amounts pulled from documents into a database or spreadsheet, with confidence scores and review queues.
Use cases
Where teams use it
- Contract review: uncapped liability, renewal dates and non-standard clauses
- Insurance: policy wording, exclusions and claims files
- Banking: covenant monitoring, group exposure and audit findings
- Healthcare: device recalls, policy reviews and procedure lookups
- Manufacturing: batch traceability and maintenance history
- Customer support: error codes, knowledge base and ticket history
- Financial research over SEC filings and annual reports
Kitchener · Waterloo · Cambridge · Guelph
Document AI for Waterloo Region businesses
Insurers, law and accounting firms and manufacturers across Kitchener-Waterloo hold decades of policies, contracts, drawings and quality records. We make that searchable in plain English, with every answer cited to the page, and keep it private when the content is sensitive.
Service area
Waterloo Region: Kitchener, Waterloo, Cambridge, Guelph, Elmira, St. Jacobs, New Hamburg, Baden, Ayr, Wellesley, Breslau
Nearby, in person: Stratford, Brantford, Hamilton, London, Woodstock, Milton, Toronto and the GTA
Remote: everywhere in Canada, including Ottawa, Montreal, Calgary, Edmonton, Vancouver and Halifax
Process
How a project runs
- 01
Discover
We map the workflow with the people who run it, find the highest-value automation points and agree measurable success criteria.
- 02
Prototype
A working prototype on your real data in weeks, not months, tested with real users against the agreed criteria.
- 03
Harden
Security review, evaluation suite, monitoring, permissions and integration with your systems, ready for production.
- 04
Run and improve
We hand over with documentation and training, or stay on to operate, measure and extend the system.
RAG & Document AI: frequently asked questions
What is RAG in AI?
Retrieval-augmented generation (RAG) is a pattern where the system first retrieves relevant passages from your documents, then has a language model write an answer grounded in those passages, ideally with citations back to the source.
Should we use RAG or fine-tune a model?
For answering questions from documents that change, RAG is almost always the better starting point. It is cheaper, it stays current and it can cite sources. Fine-tuning is useful for teaching a model a format, style or narrow skill, and the two can be combined.
What is graph RAG?
Graph RAG adds a knowledge graph of entities and relationships extracted from your documents. It helps with questions that require connecting facts across several files, which plain similarity search tends to miss.
Can you build on our existing Microsoft 365 or Google Drive content?
Yes. We connect to SharePoint, OneDrive, Google Drive, Confluence, file shares, databases and email archives, and keep the index in sync as documents change.
Can you build a private document search for a Kitchener-Waterloo firm?
Yes. We build cited document search over your files and can host it in a Canadian cloud region or on your own hardware so sensitive documents never leave your control.
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Learn moreFurther reading
Tell us about the process you want to automate.
A 30-minute call is enough to tell whether AI is a fit, what a first project would look like and what it would take to run it in production.