← Work we take onAI and data

Use AI where
it earns its place.

We use search, automation, forecasting, and computer vision when they can remove a specific piece of work or improve a decision. If a simpler rule solves the problem, we use the rule.

Talk to the team
Technologies and delivery tools

Chosen around the work.

Useful AI starts with usable data and a result that can be checked. For vision work, the stack also has to match the camera, latency, and deployment environment.

Python

Data preparation, model services, automation, and evaluation tooling.

Ultralytics YOLO

Real-time object detection, segmentation, pose estimation, and tracking on images or video.

TensorFlow

Training and serving image classification and computer-vision models when the problem calls for a custom pipeline.

OpenCV

Camera input, image preparation, geometry, measurement, and classical vision processing.

ONNX

Portable model delivery across cloud, desktop, mobile, and supported edge runtimes.

Elasticsearch

Focused retrieval across product records and approved knowledge.

scikit-learn

Interpretable forecasting and classification for bounded problems.

PostgreSQL

Structured operational data for reporting, retrieval, and analysis.

Redis

Fast queues, caching, and short-lived state around data and model workflows.

Computer vision

Turn images and video into useful operational signals.

We build bounded vision systems around a specific camera, scene, decision, and tolerance for error. The model is only one part of the work; data quality, review, deployment, and monitoring decide whether it is useful.

Ultralytics YOLOTensorFlowOpenCVONNX Runtime
01

Detection and counting

Find, classify, and count defined objects in images, recorded video, or live camera feeds.

02

Segmentation and inspection

Separate objects or regions precisely enough to support measurement, quality checks, and visible defect review.

03

Tracking and activity

Follow objects through frames to understand movement, dwell time, queues, or process events.

04

Edge and camera deployment

Package inference around the available camera, device, latency, connectivity, and privacy constraints.

How the work moves

From the real problem to a working release.

AI work starts with the decision or repeated task, then works backwards through evaluation, data quality, review, and operational ownership.

01

Define the decision and the test

We agree what useful means, how a result will be checked, the cost of a wrong answer, and when a person must decide.

02

Audit the data path

Sources, permissions, quality, freshness, labels, and missing information are assessed before choosing a model or retrieval approach.

03

Build a bounded pilot

The smallest representative workflow proves quality, latency, cost, review, and failure behaviour with real examples.

04

Operate with evaluation and oversight

Approved inputs, versioned behaviour, feedback, monitoring, and fallback routes keep the system accountable after release.

What the work can cover

Useful pieces, chosen for the problem.

Useful search

A clearer way to find information across the material the team already uses.

Workflow automation

Targeted automation for repeatable tasks with a result people can review.

Forecasting

Models that help teams assess likely outcomes without pretending uncertainty has disappeared.

Data connections

The practical work needed to bring relevant information into one usable flow.

Look for these signs

This work may be worth doing if...

  • People spend time finding the same information repeatedly.
  • A predictable manual task slows down otherwise useful work.
  • The team needs a better view of patterns in existing data.
  • Images or video contain a repeatable signal that people currently inspect by hand.
How we approach it

Define the test before choosing the model

We agree on what a useful result looks like, how it will be checked, and what should happen when the system is unsure. That keeps the technology tied to the work.

Anonymous case studies

What this looks like in practice.

These are data-foundation studies, not AI claims. They show the operational systems that make later search, automation, or forecasting work possible.

Browse all case studies
What you leave with

Useful work, not a presentation that gathers dust.

  • Problem and evaluation definition
  • Data-source and quality assessment
  • Search, automation, or model prototype
  • Human review and fallback workflow
  • Versioned service integration
  • Quality, cost, and drift monitoring
Common questions

Before the first conversation.

How do we know whether AI is appropriate?

The input, desired result, evaluation method, and cost of error must be clear. If a rule, search index, or workflow change solves the problem more reliably, we use that instead.

Can you work with private business data?

Yes, with explicit access boundaries, approved providers, retention decisions, and logging. Sensitive data is not treated as a generic prompt input.

What happens when the system is uncertain?

Uncertainty is designed into the workflow. The product can ask for more information, present evidence, route the case for review, or fall back to a deterministic path.

Other work we take on

Explore another service.

Have a specific problem?

Show us where the work gets stuck.

We will help you decide what is worth building and what can stay simple.

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