FloData Analytics
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SERVICES / AI Engineering

Choose the right kind of AI. Then put it to work.

Generative, perception and predictive AI solutions built around your data and operating rules. Deployed in the cloud, on your servers or fully offline where required.

RECOGNISE THE PROBLEM

You may need this when...

WHAT THE WORK CAN INCLUDE

Three tracks. One question: what decision does this support?

01

Perception AI

Models that see, read and listen. Computer vision on imagery and video, document extraction from scans and handwriting, and speech-to-text and text-to-speech. What is seen or said becomes data you can check and use. Object and change detection · Satellite and drone imagery · OCR and document classification · Speech recognition and synthesis

02

Predictive AI

Models that anticipate. Forecasting, predictive maintenance, recommendation and risk models, including spatial prediction and pattern detection in noisy financial market data. Each output shows the factors behind it and how confident the model is. Demand and price forecasting · Failure prediction · Recommendation engines · Survival and risk models · Spatial models · Market signal detection

03

Generative AI

Models that answer and act. LLM applications, retrieval-augmented assistants, voice agents and agentic workflows that draw on your own information and act only within the limits you set. RAG over private data · Knowledge assistants · Voice agents · Agentic workflows · On-premise LLMs

Across all three tracks: cloud, on-premise or fully offline deployment, with access controls, human review and monitoring designed in from the start.

BEFORE THE BUILD

Start with the Walk.

We study representative data and the cases where things go wrong: images, sensor logs, transaction histories, call recordings or documents. We agree what a correct answer looks like, who checks it, which track fits the problem and what must never leave your environment.

See the FloData Method

FOR THE TECHNICAL EVALUATOR

Under the hood

  1. 01Images, signals, records or documents
  2. 02Preparation, labelling and validation
  3. 03Model, agent or ensemble
  4. 04A reviewable output
  • Problem framing, data audits and evaluation sets come before model selection, and we test simple baselines before complex models and keep a person in the loop where errors are costly.
  • Perception uses vision and vision-language models, OCR pipelines, and speech recognition and synthesis models.
  • Predictive uses gradient boosting, time-series and survival models, graph and spatial models, and recommender systems.
  • Generative uses open and commercial LLMs, retrieval pipelines, vector search, tool-using agents and voice pipelines.

Illustrative approach. We select the stack after the diagnostic.

RELEVANT WORK

View related work

See the approach in practice.

Illustrative plot register with a chain of title and a sector map, using fictional plots, records and dates
CASE/01

A usable system from generations of plot records

Scanned property files became a linked register, with every ownership change traced to its source and every plot on the map.

2026IndiaReal estate
Migration & ModernisationAI EngineeringLocation Intelligence
Under the hoodHide the stack

OCR and document classification · Two-pass AI extraction with validation · Chain-of-title register · Web map with plot polygons · Maker, checker and approver workflows

Illustrative booth estimate on a seat map, using fictional places, parties and figures
CASE/02

Where an election is decided, booth by booth

Electoral intelligence for a US campaign and an Indian party organisation. Estimates built from official results, mapped from the seat to the street.

2024 and 2025United States, IndiaCampaigns & public sector
Location IntelligenceAI EngineeringAnalyticsData Engineering & Automation
Under the hoodHide the stack

Official results and electoral rolls from public sources · Booth reconciliation across elections · Segment-level turnout and preference model · Bottom-up aggregation with confidence levels · Web maps from region to household · Prediction service with Excel-ready exports · Web dashboard and mobile field app

Illustrative decision view with a sample record, survival curve, estimated life expectancy and contributing factors, using sample records and values
CASE/03

Life expectancy modelling for life settlements

Medical records became a structured, explainable input to life-settlement valuation.

2024Florida, USAInsurance & financial services
AI EngineeringAnalytics
Under the hoodHide the stack

OCR and document classification · NLP-based medical factor extraction · Standardised medical and lifestyle factor set · Cox Proportional Hazards survival model · Baseline and factor-contribution dashboard · Benchmark comparison against industry and internal methods