Define
We agree the problem or decision, who will use the result and how we will judge whether it is useful.
Machine learning and optimisation services
DabApps designs and builds models and optimisation software for complex operational decisions. We prepare the data, account for real constraints and give specialists a clear way to review recommendations.
Turn data into useful answers and decisions
Teams often have data they cannot yet use to predict outcomes, spot patterns or make better decisions. Reports can show what happened, but careful modelling can help explain what may happen next or which option is worth choosing.
We combine machine learning, data engineering and optimisation to tackle these questions. That might mean a model inside an existing product, a tool that helps a team act on predictions, or software that compares options against business rules and practical limits.
Bring us a problem that matters to your business, even if the data is imperfect. A focused assessment can establish what is feasible, what needs testing and whether a simpler approach would serve you better.
THE SERVICE
What we can build
DabApps develops bespoke models and optimisation tools around a specific problem or decision. The work can span data preparation, finding patterns, forecasting, comparing options, evaluating results and presenting useful outputs inside the software your team uses.
- Predictive models for complex data and decisions
- Optimisation that compares options against business rules and practical limits
- Data pipelines for events, transactions, measurements and external inputs
- Evaluation against an agreed baseline
- Interfaces that make predictions and recommendations understandable
- Integration with existing platforms and team workflows
COMMON STARTING POINTS
When machine learning and optimisation are useful
A good starting point is a useful question or repeated decision with enough evidence to compare approaches. We look at who needs the result, what it could improve and what information is available, then define a realistic first piece of work.
- A recurring decision with a measurable cost or opportunity
- Patterns in existing data that reporting does not reveal
- Trade-offs involving time, resources, demand or risk
- An early model that needs evaluation and a usable interface
- Useful data spread across systems or difficult to analyse
How a machine learning project moves forward
Assess
We review representative data and establish a baseline. Focused research tests whether a model offers more than the current approach.
Build
We develop the data pipeline, model and software around it, including optimisation where it helps, then test outputs against agreed scenarios and exceptions.
Support
We plan integration, review, monitoring and ongoing ownership with your team so the system remains useful as the data and business change.
WHY DABAPPS
Models connected to useful software
With Emvelo, we moved from paid research into an operator decision-support system for a 100MW solar plant. It combines more than a thousand sensor streams, a predictive plant model, constrained optimisation and an interface for reviewing recommendations. A 162-day historical evaluation indicated about 5.4% modelled revenue uplift against an expected baseline.
- Models shaped around the problem, the data and the constraints
- Evidence from historical evaluation before a wider commitment
- Results that people can inspect and challenge
- Data engineering and application development alongside the model
Data and decisions in practice
Emvelo shows our custom modelling and optimisation work. Firefly shows how we turn operational data into a tool for specialists, while Greener Charge shows how forecast data can help people choose when to act.
Emvelo
We built an operator decision-support system for Emvelo, combining plant forecasts and optimisation recommendations in an operator interface.
Firefly Glow
Glow helps Firefly investigate equipment remotely, reducing travel, while customers can check performance, CO2 and fuel savings themselves.
Greener Charge
Our web app compares UK grid carbon intensity forecasts, helping EV drivers decide whether it's greener to charge tonight or tomorrow night.
The software around the model matters
Useful models depend on dependable data, a clear interface and a way to compare results with what actually happened. Our work on web applications and platforms supports those parts of a machine learning project. For a wider AI feature or agent integration, see AI product engineering. If you need advice on a specific AI idea before commissioning a build, start with AI consultancy.
Questions about machine learning and optimisation
What data do we need to start?
We need examples of the problem or decision, what information was available and what happened afterwards. Events, transactions, measurements, outcomes and relevant external factors may all matter. We review quality and gaps before proposing a model.
Does every optimisation problem need machine learning?
No. Some decisions can be improved with clearer rules, better data or a conventional optimisation method. We compare approaches against the actual decision and use machine learning where it adds useful predictive power.
How do you assess whether recommendations are worthwhile?
We agree a baseline and success measure with you, then test on representative historical conditions and important exceptions. We distinguish modelled improvements from outcomes measured after operational use.
Can the system work with our existing software and teams?
Yes. We design the data flow, interface, exports and permissions around the systems and people already involved. Predictions and recommendations can be reviewed by a person or integrated into an existing workflow.
What affects scope and cost?
Data access and quality, the complexity of the problem, integration requirements and the evidence needed to evaluate an approach all affect the work. We can begin with a focused assessment before defining a larger build.