Service
From an AI idea to a system your team uses every day
We design, build and ship custom AI solutions that integrate with your systems and work reliably with real data and real users — not just in a demo.
The first 30-minute call is free
WHEN YOU NEED IT
When a custom solution is the right call
Building a prototype is easy. Making a solution work reliably with real data, real users and real business systems is much harder.
An off-the-shelf tool isn’t enough
Your process has business logic, integrations, data or user roles that a ready-made SaaS product doesn’t cover.
You have several separate automations but no single system
Separate workflows start duplicating logic, and the process becomes hard to maintain, test and extend.
AI has to live inside your product or internal system
You don’t need a separate ChatGPT window — you need a feature that works directly inside your application, CRM, portal or operations system.
The project touches a critical process
When a solution handles important data, customer information or operational actions, it needs a clear architecture, tests, monitoring and defined limits of responsibility.
FIT
When this service is a good fit
You have a clear business process that an off-the-shelf tool can’t cover
You want to build an AI feature into your own product
You need to bring several systems and user roles together
A prototype has proven itself and you now need a production solution
You need a partner who can cover the process, backend, frontend, integrations and AI
If you aren’t yet sure what is worth building, we recommend starting with Analysis and preparation.
SOLUTIONS
What we can build
Custom AI applications
Internal or customer-facing web systems where AI is built into a specific use case.
AI features in an existing product
We integrate search, summaries, document analysis, classification, recommendations, content generation or another AI feature into the system you already run.
Process management systems
When the problem is bigger than a single automation, we build a system that ties together process states, users, data, integrations and automated actions.
Agentic systems
Multi-step workflows where AI agents use your data and tools, with risky actions kept under human control.
Document and knowledge systems
RAG, document classification, data extraction, semantic search and internal assistants.
Integration layers
APIs, backend services and data flows that connect AI to your CRM, ERP, accounting, Google Workspace, email or custom systems.
QUALITY
What a “production-grade” AI solution actually means
A working demo is only the start. In a real business there are more questions to answer:
What happens when the model returns a wrong result
What happens when an external system is down
How user permissions are managed
How different customers’ data is kept separate
How we can see what the system actually did
How changes are tested before release
What each AI action costs
How we swap the model when a better one appears
Who can approve a risky action
How the solution will be maintained in 6 or 12 months’ time
We treat these as part of the product and the software system, not as an add-on after launch.
PROCESS
How we work
Step 01
Defining the problem and the process
We agree which business problem we are solving, who the user is, what the current workflow looks like and how we will judge the result.
Step 02
Designing the solution
We set out the future process logic, the system architecture, data sources, integrations, security boundaries and where the AI component sits.
Step 03
Prototype or pilot
We test the main risk against real data as early as possible. If the AI part doesn’t reach the reliability needed, we find out before investing in a full system.
Step 04
Full build
We build the user interface, backend, integrations, data layer and AI logic, and test the solution as a single system.
Step 05
Deployment and bringing the team on board
We launch the solution in the live environment, train the users and monitor the first phase of real use.
Step 06
Measurement and improvement
We review usage, errors, AI quality and business KPIs, then extend only what genuinely creates value.
Considering a custom AI solution?
We will assess the process, the data, the integrations and the risks, then tell you whether to start with a prototype, an automation or a full solution.
DELIVERABLES
What you get
01
A clearly defined scope
What the system does, what it doesn’t, what the first phase covers and which features can wait for a later stage.
02
A technical architecture
Data, models, integrations, authentication, access, infrastructure and monitoring, all planned as one system.
03
A working product
Not a deck or a prototype, but an application or feature that is actually used — where that is the agreed project scope.
04
Tests and quality control
For AI features we use real test scenarios; for the conventional software parts we apply standard testing practice, scaled to the project’s risk.
05
Documentation
We document the key architecture, integrations, configuration and how the system is run.
06
A handover or support model
We can stay responsible for developing the solution, or prepare it for handover to your in-house team.
EXAMPLE
hobeehub
Administration for an activity and club business was spread across Excel, WhatsApp and assorted other tools. Rather than automating one small task, we built a full SaaS platform that brought the core workflow together. It covers registration, schedules, automated invoicing, payment integration and AI-supported features including contract generation.
This case shows the difference between a single automation and a full system: when a process spans many roles, data sets and integrations, you need an architecture that can grow with the business.
42,500+
lines of code in a full-scale SaaS
43
automated server functions
TECHNOLOGY
Which technology we use
We choose technology to fit the problem, not to chase the most fashionable model. A solution might include:
.NET or Python backend
A Next.js / React interface
PostgreSQL or another suitable database
Vector search and RAG
An n8n / Make automation layer
Models from OpenAI, Anthropic, Google or others
Cloud infrastructure
API integrations with your existing systems
Monitoring and error-tracking tools
What matters most is that the system stays understandable, testable and maintainable well beyond the first demo.
RISK MANAGEMENT
How we manage AI risk
Tests instead of gut feel
We assemble a set of real examples and check how the AI behaves on both routine and difficult cases.
A person in the loop where it matters
Not every action needs to be autonomous. For critical decisions we design in an approval step.
The model is not the whole system
Important business rules, data validation and state should not depend on a generative model alone.
Monitoring after launch
AI models, prompts and external integrations all change. The system has to show when quality or reliability starts slipping.
FAQ
Frequently asked questions
Do you build only the AI part, or the whole system?
We can build the whole solution — frontend, backend, data layer, integrations and AI features. If you already have a strong in-house team, we can take on just the AI or the integration part.
Do we have to replace our existing systems?
Usually not. We look first at how to integrate the new solution into your current environment. We only propose replacing a system when the existing architecture genuinely blocks the goal.
Is a prototype essential?
Not always, but in AI projects an early test against real data is often the cheapest way to reduce technical risk.
Will we own the code?
Ownership and handover are set out clearly in the proposal before the project starts. For custom solutions we recommend avoiding unnecessary lock-in to a single closed platform.
Can the solution work with our sensitive data?
Yes, though the architecture depends on the data classification and your requirements. We can choose EU regions, restrict access, minimise what gets transmitted and design whatever other safeguards are needed.
What happens after launch?
We can offer a support and development arrangement, or hand the system over to your team. We recommend reviewing AI features periodically for quality, cost and actual usage.
Have an AI idea? Let’s turn it into a clearly defined project.
On the first call we will assess the process, the data, the integrations and the project’s biggest risks. Then we can tell you whether to start with a prototype, an automation or a full solution.
1
You get in touch
2
A free 30-minute call
3
A clear scope and next steps