Not an AI demo, but something that works with your real software, data and people.
The problem and the desired direction are clear. Now it has to be built properly. We take on architecture, AI, integrations and production-grade engineering as one whole and hand it back over afterwards.
When is this offer a fit?
When the desired outcome is clear enough to scope a professional implementation.
Have an AI agent built
You know which task or workflow the agent has to perform. Now it needs to be designed well, connected and made reliable enough for real use.
View use caseHave an internal AI assistant developed
You want to give employees faster access to internal knowledge, documents and systems through an assistant that fits your organisation.
View use caseAutomate a business process with AI
The process is clear and contains work that could be smarter or largely automatic. Now it has to become a reliable technical workflow.
View use caseAdd AI to existing software, an app or SaaS
The product already exists. Now you want to add AI functionality without losing sight of the existing architecture, user experience and reliability.
View use case
Afterwards you have...
Working software instead of a demo
The agreed functionality works end to end within the real technical environment.
Reliable integration with what is already there
Data, systems, permissions and human processes are part of the solution.
A product you can hand over
Architecture, code and key choices are documented well enough to continue independently.
What is included in this offer?
The scope is defined up front. Then you get a fixed price for the agreed result.
What we do
The exact work differs per engagement, but we take responsibility for the complete technical result.
- Making scope and success criteria concrete
- Designing the architecture
- Implementing AI functionality or an agent flow
- Building integrations with existing software and data
- Setting up authentication, authorisation and data flows properly
- Tests, evals, monitoring and error handling where relevant
- Deployment and production setup
- Documentation and technical handover
What you get
The deliverables are defined per scope and are part of the fixed price.
- Working agreed functionality
- Production-grade implementation
- Required integrations
- Relevant tests and quality checks
- Technical documentation
- Deployment and handover as scoped
Out of scope
No unbounded consultancy and no feature list that keeps growing afterwards.
- Functionality outside the agreed scope
- Ongoing retainer as a standard component
- External model, API and hosting costs
- Additional work without a new explicit scope and price
How it works.
- 01
Scope + success criteria
We make exact enough what needs to work to agree on responsibility and price.
- 02
Architecture + integrations
We determine how AI, data, existing software, permissions and users come together.
- 03
Build + test
We implement the hard part and also test errors, edge cases and reliability.
- 04
Go live + hand over
We bring the solution to production in a controlled way, document it and hand it over.
Why should you believe this?
AI only becomes truly valuable when it works together with existing software, data and business processes. That is simply serious software engineering.
View casesThe same people advise and build
Advice and execution sit with the same technical people.
Integrations are core work
We don't treat APIs, databases, auth and existing systems as an afterthought around the AI.
Production and handover are part of it
The work isn't done when the demo works, but when the solution is manageable.