ML, Computer Vision & Cloud MLOps Services
The fundamental technology behind all the other aspects is custom machine learning models, vision systems used for detection and inspection, and the GCP/AWS infrastructure which ensures that they remain fast, are continuously monitored, and are affordable when in production.
When this is the right answer
There is data science which has been carried out on a laptop but has never reached production stage—there are no pipelines, no monitoring, and no retraining.
Visual inspection, reading documents, counting and checking—repetitive perception tasks which vision models are now able to carry out consistently.
The growth of AI workloads is outpacing the architecture designed to support them, and there is no one responsible for balancing performance against cost.
What we build
Custom ML models
Models for forecasting, churn prediction, making recommendations, and carrying out classification together with natural language processing — all based on your data — and with honest baseline comparisons so that you know the model outperforms the simple alternative.
Computer vision systems
Object detection, tracking, visual quality inspection, OCR and document parsing, as well as video analytics—these are deployed either in the cloud or on edge devices.
MLOps pipelines
For machine learning models, continuous integration and continuous deployment enable automated training, versioning, deployment, monitoring, detection of drift, and retraining—thus ensuring that accuracy is maintained when the data changes.
Data engineering
The pipelines used for all these processes—ingestion, transformation, and warehousing on BigQuery and the modern GCP/AWS data stack.
Cloud architecture & migration
GCP-first as a Google Cloud Partner, with deep AWS expertise — architectures, migration strategies, and hybrid configurations designed specifically for AI workloads.
Cost optimization
Adjusting the computing resources, combining requests, using caching, and reviewing the architecture in such a way that cloud expenditure is reduced without having to reduce capabilities.
Where this earns its keep.
Every engagement runs spec-first, agent-accelerated, human-owned.
A written specification that has been approved by the client prior to any coding taking place — covering the architecture, the edge cases, and the acceptance criteria.
The engineers arrange for the AI agents to carry out the specification in parallel, taking weeks rather than quarters.
The evaluations, tests, and security checks are carried out together with the build, and humans examine every decision that is important.
The system has been deployed to your GCP or AWS, complete with monitoring, production evaluations, and retraining loops.
From the portfolio.
Video analytics that reveal and remove assembly-line bottlenecks
LineSense is an AI-powered video analytics platform which constantly monitors workstations and converts motion data into objective and unbiased insights, providing manufacturing teams with a factual, up-to-date view of where time is being lost on the production line.
Tools we reach for.
Chosen for each project based on evaluations and fit — never out of habit.
Questions, answered.
Should we build on GCP or AWS?
Can vision models run on edge devices, offline?
How do you keep models accurate over time?
We already have a data team — how do you work with them?
Can you migrate our legacy ML setup to modern MLOps?
Have a use case in mind? Let's pressure-test it — free.
A 30-minute call with our engineers, not a salesperson. You'll receive an honest assessment of feasibility, a preliminary architecture, and a clear next step.
Book a free AI consultation