Document intelligence
OCR, vision-language models and extraction schemas turn varied documents into records that can be checked.
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SERVICE / 02 / AI Engineering
Make the documents usable. Then put AI to work.
Build AI around your documents and operating rules, on your servers or fully offline where required.
Bring us a problemRECOGNISE THE PROBLEM
You may need this when...
- Important facts are buried in scans or handwritten notes.
- Staff need answers from an archive they cannot search.
- A model demonstration has no reliable place in daily work.
- Sensitive data needs to remain on your own infrastructure.
WHAT THE WORK CAN INCLUDE
A scope shaped around the decision.
Assistants and agents
Knowledge assistants, voice agents and agentic workflows connect answers to the information and actions they depend on.
Vision and deployment
Computer vision and production model integration, with on-premise or fully offline deployment where the problem requires it.
BEFORE THE BUILD
Start with the Walk.
We examine representative documents and failure cases. We establish what a correct answer looks like, who checks it and what must never leave the environment.
[CONFIRM: paid diagnostic]
See the FloData Method ↗FOR THE TECHNICAL EVALUATORUnder the hood
- Documents or observations
- Extraction and validation
- Model or assistant
- A reviewable answer
Document routing, extraction schemas and evaluation sets come before model selection. Access controls, human review and deployment constraints shape the design.
Illustrative approach. We select the stack after the diagnostic.
RELEVANT WORK
View related work ↗See the approach in practice.
FLODATA / CONNECTED PRODUCTSSCHEMATIC
[ASSET NEEDED: 16:10 connected product views using demo data]
CASE/03
A connected system for personal injury claims
Connected products support report discovery, clinic collections and patient retention.
Under the hood
AWS · Python · Flask · FastAPI · PostgreSQL · React · Gunicorn · Nginx · GitHub Actions
FLODATA / DOCUMENT INTELLIGENCESCHEMATIC
[ASSET NEEDED: 16:10 record search and document classification with dummy data]
CASE/04
Four decades of HR paper, made searchable
1,527 active and 178 inactive records structured for search and export.
Under the hood
OCR · Vision-language models · [CONFIRM: remaining stack]
FLODATA / LOCATION & DATASCHEMATIC
[ASSET NEEDED: 16:10 model factors dashboard using fictional records]
CASE/06
Life expectancy modelling for life settlements
Standardised medical data feeds a model with visible contributing factors.
Under the hood
OCR · NLP · Cox Proportional Hazards
A GOOD PLACE TO START