Make company knowledge usable
Make documents, policies and specialist knowledge searchable and citable.
AI solutions for businesses
The right AI solution starts with your current situation.
Extend existing software, improve a business process or build a new custom application: we choose the technical approach based on the problem to solve, not on whichever AI tool is currently most fashionable.
Start with the problem, not tool hype
Use existing systems where sensible
Define clear quality and control points
Plan for operations and continuous development
Three typical routes
Which starting point describes your project?
The technical solution differs significantly depending on whether you are extending an existing system, building a new product or using AI to support the engineering process itself.
Existing systems
Integrate AI into existing software
Add RAG, assistants, document processing or automation to existing business software, portals and processes.
View integrationNew custom solution
Build new AI software
Develop custom applications, portals or SaaS products from architecture and UX through to AI capabilities, backend and operations.
View servicesEngineering with AI
Use AI deliberately in software development
Use AI for research, implementation, testing and documentation while architecture, review and responsibility remain with the team.
View our approachFrom problem to architecture
Common tasks – and the technical direction that usually fits.
Not every problem needs an agent, a RAG system or a new product. A combination of conventional software logic, APIs and targeted AI components is often the stronger solution.
Automate recurring work
Combine rule-based workflows with AI steps for text, documents and variable decisions.
Use tools and systems autonomously
Build agentic systems with explicit permissions, tools, limits and approval points.
Integrate models into products
Connect LLMs through controlled APIs, structured outputs, fallbacks and monitoring.
Build a new web application
Develop a business application, customer portal or SaaS solution with AI as a useful product capability.
Validate an idea properly
Use real data and measurable success criteria to test whether an AI idea works technically and commercially.
Decision guide
Which AI solution fits which task?
RAG, AI agents, LLM integration and automation are different technical approaches. The deciding factor is the job the system needs to do.
| Starting point | Typical approach | Learn more |
|---|---|---|
| Search your own documents and company knowledge | RAG, semantic or hybrid search | RAG systems |
| Integrate OpenAI, Claude, Gemini or another LLM into software | LLM and API integration | LLM integration |
| Automatically prepare documents, emails or variable cases | AI process automation plus deterministic workflow logic | Process automation |
| Handle multi-step tasks with tools and flexible decisions | Constrained AI agent with explicit permissions | AI agents |
| Build a new business application, customer portal or AI SaaS product | Custom AI web application | AI web applications |
| Validate an idea, model quality or technical feasibility first | AI prototype, proof of concept or MVP | AI MVP |
Decision logic
Understand the process first, then choose the technology.
We deliberately separate tasks that conventional software can solve reliably from tasks where generative AI, semantic retrieval or agentic logic offers a real advantage.
Questions we answer before implementation
- Which user task or business process should improve?
- Which data and systems already exist?
- Where are errors tolerable – and where are they not?
- Which decisions must remain with people?
- Which privacy, security and operational requirements apply?
- How will we measure quality and business value?
Process
From current situation to production solution.
A structured process reduces poor technical decisions and helps ensure an AI project goes beyond a convincing demo.
1
Understand & prioritise
Assess the process, users, data, systems and commercial objective together.
2
Architecture & validation
Define the technical approach, quality criteria and a sensible first scope.
3
Build & integrate
Bring software, AI components, interfaces, permissions and monitoring together.
4
Operate & evolve
Monitor quality, review usage and improve the solution deliberately.
FAQ
Questions about AI solutions.
Which AI solution is right for our business?
That depends mainly on whether you want to extend existing software, automate a process or build a new custom application. We start from the current situation and desired outcome, not from a specific model or tool.
Does AI always require new software?
No. Integrating AI into existing systems is often the better option. New software becomes relevant when current applications cannot support the required processes, roles or integrations in a sensible way.
How do you decide between RAG, agents and conventional automation?
We look at the data, process logic, tolerance for errors, permissions and integration points. Rule-based steps should usually remain rule-based; AI should be used where flexible interpretation or generation actually adds value.
Can an AI solution be introduced in stages?
Yes. A focused pilot or MVP can test a technical and business assumption with real data before a broader rollout.
Let’s turn your AI idea into a concrete software project.
In an initial conversation, we clarify the objective, current situation and sensible next steps.
To get started, your objective, existing systems and the main open question are enough.