Understand
We review the task, users, data and existing systems. Discovery or a focused prototype resolves open questions, giving you a clear first-release scope and a way to judge success.
AI software development solutions
We build and integrate AI software into real systems. DabApps is a UK AI product engineering partner, building AI capabilities into products and operational workflows, with the integrations, controls and support they need to work in practice.
Put AI to work in your product
You may want to add an AI feature to your product, let an agent work across existing systems or help operators compare decisions using business data. We build the software that makes those capabilities useful to the people doing the work, with clear limits on what the AI can do and when someone needs to review it.
Our AI implementation service is for product and operations teams ready to build a capability into their software or workflows. We take responsibility for the data preparation, integrations, application development, evaluation, deployment and ongoing support.
Still deciding which opportunities to pursue across the business? Start with AI Transformation. For a specific feasibility, integration or evaluation question before a build, explore AI consultancy.
WHAT WE BUILD
AI integration around real tasks
AI product engineering combines models with the software, data and interfaces people use every day. We build AI automation around a defined task, such as coordinating actions across systems, comparing operating scenarios or analysing documents, and connect it to the systems where the work happens.
- AI features integrated into existing web and mobile products
- Agent integrations with defined permissions and approval steps
- Workflow automation that connects business systems
- Decision-support tools that model and compare operating choices
- Document analysis and reporting tied to source evidence
- Recommendations and draft material prepared for human review
COMMON STARTING POINTS
When an AI implementation makes sense
A useful starting point is a repeated task with accessible data and an outcome your team can check. We assess the existing workflow, the cost of getting it wrong and the work needed around the AI. That helps establish whether automation is worthwhile and how much responsibility the system should have.
- A defined AI feature ready for implementation
- A prototype that needs testing with real users and data
- Repeated tasks and decisions that need better use of business data
- Useful AI output that still needs copying between systems
- An existing product that needs AI integration and ongoing support
How we deliver AI software
Prepare
We structure the data, plan integrations and design the user workflow. Together we agree representative evaluation cases, access rules and where people need to review or approve outputs.
Build
We develop the application and AI integration, test outputs against the agreed task and check failure handling. You review working software before a controlled deployment.
Support
We monitor quality, response times and running costs, maintain the surrounding product and evaluate changes to models, prompts and data. You have a team responsible for keeping it working as usage grows.
WHY DABAPPS
AI software your team can operate and trust
With Emvelo, we prepared sensor data, developed a plant model and built an operator interface for reviewing optimisation recommendations. With ImpactIO, we designed AI outputs tied to source evidence. Both show why useful AI depends on the data, the task and how people will assess its output. We bring the same attention to permissions, evaluation and ongoing ownership when building an AI system, so your team can check its work and respond when something changes.
- Permissions limit which records AI can read and which actions it can take
- Source references help reviewers check a finding against the original evidence
- Human approval keeps consequential decisions with the responsible person
- Representative evaluations reveal errors before a release or model change
- Monitoring and audit trails help teams investigate unexpected behaviour
- Fallbacks and overrides let people continue when an AI step fails
AI delivery and the engineering behind it
AI delivery involves preparing data, integrating outputs into a usable product and supporting the software over time. These projects show those engineering responsibilities in practice.
Emvelo
We built an operator decision-support system for Emvelo, combining plant forecasts and optimisation recommendations in an operator interface.
Brace for Impact
ImpactIO says our platform analyses sustainability documents in minutes, speeding up reporting and creating a new service to sell.
Agilio iComply
More than 18,000 dental professionals rely on iComply to manage compliance across the UK. We've worked with Agilio since 2015 to help the product evolve alongside their customers' needs.
What our clients say
“We used to manually collect, read and analyse client documents to build our sustainability reports and that would take us anything from a couple of hours to a couple of days, depending on the amount. Now the platform does that in minutes and the analysis is more consistent than manual analysis.”
ImpactIO
The application around the AI matters
An AI feature still needs a useful interface, dependable data and integrations that handle failures. Our product design, web application and data engineering work supports those parts of the delivery. We keep the AI software working as it grows, including the platform people rely on around it.
Questions about AI implementation
Can you add AI to our existing product or systems?
Yes. We review the application's architecture, APIs, data and permissions, then agree where the AI capability belongs. That can mean adding a feature inside the product or giving an AI agent controlled access to existing functions. We build the interfaces, integrations and checks needed around it.
Do we need a finished specification before we start?
No. A clear task or business problem is enough for an initial conversation. We can include discovery and prototyping where needed, or work from an existing design. Before committing to a full build, we agree what a useful first release should do and how its outputs will be evaluated.
How do you check whether AI output is good enough?
We agree evaluation examples drawn from the real task, including incomplete information and cases where the system should ask for help. We test accuracy and usefulness, review failures with your team and decide which outputs need human approval. Those checks also help assess later changes to models, prompts and data.
How do you handle sensitive business data?
We establish which data the system needs, who can access it and what may be sent to an AI provider. Hosting, retention, access controls and provider terms are considered before implementation. The application's permissions must apply to AI actions as well as to people using the interface.
What affects the cost and timing of an AI implementation?
Data quality, integration access, workflow complexity and the level of evaluation and human review all affect scope. We define a useful first release around those constraints. Model usage, hosting, monitoring and support also need to be included in the running costs.
Can you support the AI system after launch?
Yes. We can take responsibility for hosting, monitoring, maintenance and continued development. Support includes the surrounding application and integrations, alongside checking AI quality and costs as usage, source data and providers change.
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