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AI development company

AI Development Company for Vision, Language and Documents

We build AI into real software: computer vision, natural language processing and document understanding that pull structured data out of images, text and files, and put it to work in your systems.

Book an AI scoping callSee what we have shipped

What AI development means here

Most useful business AI is narrower than the headlines. It reads a photograph of a passport and fills in the record. It turns a scan into fields. It classifies an incoming message. It turns satellite imagery into a number someone can act on.

The model is rarely the hard part. The hard part is the data around it: getting clean input, handling the cases where the model is unsure, and writing the result into the system that needs it.

We build AI as part of working software, with evaluation and a fallback for when it is wrong, not as a demo.

What we build

AI features that do one job well inside a real product.

Computer vision

Models that read photographs and images, such as capturing identity documents and extracting structured data from them.

Document intelligence

Contracts, forms and scans turned into structured, searchable data you can ask questions of.

Natural language processing

Classification, extraction, summarisation and routing of messages, tickets and call transcripts, including in more than one language.

Generative AI features

Drafting, summarising and answering questions over your own data, grounded in your records rather than the open web.

Imagery and geospatial analysis

Pipelines that turn satellite and aerial imagery into indices and trends that non-specialists can act on.

Evaluation and fallback design

Accuracy measured on your own data, confidence thresholds, and a human review path for the cases the model is unsure about.

AI that is measured, not assumed

A model that is right in a demo and wrong in production is worse than no model. We measure it on your data.

  1. 01

    Evaluated on your real data

    Accuracy is measured on your own documents, images and messages, not on a benchmark that looks nothing like your work.

  2. 02

    Confidence thresholds and human review

    Low-confidence results go to a person instead of straight into the record, so errors are caught before they spread.

  3. 03

    Grounded in your records

    Generated answers draw on your data and point back to it, so a person can check where an answer came from.

  4. 04

    Data handled deliberately

    We design around where your data is allowed to go and which models see it, and agree that before building.

  5. 05

    Cost and speed designed in

    Model choice, batching and caching are decided with cost and response time in mind, so a feature that works does not become expensive to run.

AI we have put into production

Each runs inside a larger working system.

Operations portalNorthgate PortalPassport and Emirates ID captured from a photograph with serverless vision, instead of being retyped.Read case studyGeoTech / data intelligence platformGeoVerdantSatellite imagery turned into six vegetation and moisture indices with trend tests, from a boundary drawn on a map.Read case studyConTech / AI SaaS platformBuildWise AIAI-assisted budgeting and takeoffs for homeowners and small builders who were running projects in spreadsheets.Read case study

How an AI build runs

  1. 01

    Define the job

    About a week. We pick one task the model must do, collect real examples of it, and agree how accuracy will be measured.

  2. 02

    Prove it on your data

    A working prototype on your real inputs, with accuracy measured, before the surrounding system is built.

  3. 03

    Build it into the product

    Confidence thresholds, review queues, logging and write-back, so the model sits inside a working process.

  4. 04

    Monitor and improve

    A first feature is typically live in three to four weeks. We track accuracy in production and improve it from real cases.

AI development, answered

It builds AI features into working software: choosing and testing models, preparing the data, wiring them into your systems, and making sure the result is measured, monitored and safe to rely on.

Reading documents and photographs, such as capturing identity documents or extracting fields from scans, and analysing imagery, such as satellite or site photographs. In each case it replaces manual retyping or specialist analysis.

That depends on your data and the task, so we measure it rather than promise it. We test on your real examples during the build, set thresholds, and route uncertain results to a person.

We usually start with existing models, because they are faster and cheaper to prove. We train or fine-tune only when your data and accuracy needs show a general model is not enough.

We agree that before building. Which models see your data, where they run and what is stored are design decisions, made with your constraints in mind.

The audit takes about a week and a first AI feature is typically live in three to four weeks. Cost depends on the task, the data and the integrations. We scope it after the audit, so you know what you are buying before you commit.
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Show us the documents nobody wants to retype.

Thirty minutes with an engineer. You leave with a view on what AI can read reliably, and where a person should stay in the loop, whether you hire us or not.

Book an AI scoping call