We prepare your data and train models on dedicated servers.
Each version is measured on cases it has never seen.
In the cloud, on your servers or on devices on site.
We track results and retrain when the real world changes.
We prepare your data, train or adapt models and wire them into the software you already run. Then we measure how they perform and keep them working once real users arrive.
We prepare your data and train models on dedicated servers.
Each version is measured on cases it has never seen.
In the cloud, on your servers or on devices on site.
We track results and retrain when the real world changes.
Four stages, each ending with something you can see and review before the next one starts.
Review the problem, the data you hold and whether machine learning is the right tool at all.
Build a small model on real data and test it against an agreed measure of success.
Connect the model to your systems through APIs, with logging and access controls.
Watch accuracy and cost in production and retrain when the data or the business changes.
Cleaning, labelling and organising your records so a model can learn from them.
Discuss thisTraining new models or adapting existing ones to your task and terminology.
Discuss thisAdding language model features such as summaries, search and drafting to your apps.
Discuss thisWrapping models in secure APIs so other systems can send requests and get answers.
Discuss thisTest sets and scoring that show where a model is right, wrong or uncertain.
Discuss thisVersioning, retraining and release processes so models can be updated safely.
Discuss thisTracking accuracy, cost and drift (gradual decline) once a model is in daily use.
Discuss thisRunning models on your own servers or on devices where data cannot leave site.
Discuss thisDrones on defence missions cannot rely on GPS, which may be jammed or unavailable.
The drone knows where it is without GPS, using custom deep learning models and sensor fusion.
Technology: Python, C++, OpenCV, custom deep learning models, sensor fusion
Also delivered: Satellite imagery analytics for a confidential defence and disaster response client, and an autonomous air traffic monitoring system for a confidential space agency.
Delivered by Hoki and its engineering partners. Product and client names belong to their owners.
Discuss a similar projectWe pick tools around your existing systems, your team and how the product will be run.
The same engineering, shaped by the rules and realities of each sector.
Visual inspection, predictive maintenance and process models running on site.
Document extraction, risk scoring and assistants that keep sensitive data in-house.
Models that sort referrals or summarise notes, with clinician review built in.
Product recommendations, demand forecasts and better search on your catalogue.
Arrival time predictions, document reading and route or load suggestions.
Fault detection from sensor data and forecasting of load or equipment wear.
Not always. Some tasks can use an existing model with a small set of your examples. Others need more history. We look at what you hold during assessment and tell you plainly whether it is enough, and what to collect if it is not.
Yes. We can deploy on your own servers or on devices at your site, using open models where suitable. This suits organisations with strict privacy rules or limited connectivity.
It depends on the data work, the model approach and where it runs. We agree scope and price after a short discovery, and often start with a fixed prototype phase so you can judge results before committing further.
Ownership is set out in the contract, and it is typically the client. Where we use open source or third-party models, their licence terms still apply, and we explain those before work begins.
Another question? Email hello@hoki.co
Tell us what you want to predict, classify or automate and we will tell you honestly whether machine learning fits.
Share a few details about your AI engineering project. We will reply with questions and a suggested next step.
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