AI Services
AI development: RAG systems, AI agents and private AI
AI services from audit to operation, from a company that runs on agents itself. Not theory — lived practice.
Most consultancies sell theory. Interlusion sells experience. Every recommendation comes from a company that uses autonomous agents for its own development, planning, review, and operations — daily, in production, not as a proof of concept.
The difference shows in the quality of advice. Interlusion knows what actually works, what breaks at 2am, and where the real value sits. No hype. Just working systems backed by a Diplom in Computer Science, specialising in Intelligent Systems, and 25 years of engineering discipline.
AI adoption fails when it's bolted onto existing processes without understanding why. Interlusion starts with the business problem, maps the information flows, and identifies where AI creates genuine leverage — not where it sounds impressive in a slide deck.
The result: AI systems that teams actually use and that integrate with existing workflows. Practical intelligence, not science projects.
Capabilities
AI services that ship
Six service areas, each grounded in production experience. From knowledge systems to full organisational AI adoption.
- RAG & Knowledge Systems
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Retrieval pipelines that make documents, wikis and support tickets answerable, with answers grounded in real sources. Vector, hybrid and graph-based retrieval (GraphRAG), with retrieval quality evaluated. Most RAG fails at the R, not the G — so retrieval is treated as an engineering problem.
- Local & Private AI
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Open-weight models that can run entirely on the client's own hardware or private cloud, so sensitive data stays in-house.
- Agent Development & Installation
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Custom agent systems for client teams — from concept to deployment to ongoing supervision. Designed for real workflows, not demos. Includes agent orchestration, tool integration, guardrails, and human-in-the-loop oversight patterns.
- Model Fine-Tuning & Evaluation
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Adapting foundation models to domain-specific tasks with rigorous evaluation frameworks and safety guardrails included from the start. Dataset curation, training pipelines, performance benchmarking, and deployment strategies — end to end.
- AI Process Automation
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Applying AI agents to business operations — accounting, planning, review, documentation, internal workflows. Identify the highest-friction manual processes and replace them with supervised automation. The routine handled, so humans focus on judgment.
- Context & Architecture Engineering
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The reason most AI agents drift, hallucinate, and produce throwaway code is not intelligence — it's context. Interlusion designs the knowledge architectures that make agents compound: structured project context, progressive disclosure, memory hierarchies, and the onboarding infrastructure that turns a generic model into a reliable team member.
Process
From assessment to production
AI adoption requires methodology, not magic. Five phases, each with clear deliverables.
01
Discovery & Audit
Map existing workflows, identify friction points, and quantify where AI creates real leverage. Not every process needs AI — this phase separates genuine opportunity from hype.
02
Solution Design
Co-design the architecture with stakeholders. Model selection, data pipeline design, integration points, and human oversight patterns. A technical blueprint before any code is written.
03
Build & Validate
Iterative development with working prototypes at each stage. Real data, real evaluation metrics, real user feedback. No black boxes — every decision is explainable and auditable.
04
Deploy & Enable
Production deployment with monitoring, alerting, and guardrails. Team training and documentation so the system doesn't depend on external expertise to operate.
05
Optimise & Evolve
Post-launch performance monitoring, model updates, and continuous improvement. AI systems improve with data — this phase ensures they actually do.
Engagement Models
Flexible engagement, serious delivery
Whether the need is a focused sprint or a long-term partnership, Interlusion adapts to the situation.
2–4 weeks
AI Opportunity Sprint
Rapid assessment of where AI creates value in existing operations. Includes workflow audit, opportunity ranking, and a prioritised roadmap. Ideal for teams that know AI matters but need clarity on where to start.
Best for: Innovation leads, CTOs, operations teams
2–6 months
End-to-End Build
Full AI system development from architecture to production deployment. Includes data pipeline setup, model selection and fine-tuning, agent development, integration, testing, and team enablement. One partner, no hand-offs.
Best for: Product teams, enterprise engineering
Monthly
Managed AI Operations
Ongoing monitoring, optimisation, and evolution of deployed AI systems. Model performance tracking, retraining schedules, new feature development, and continuous improvement. For teams that need sustained AI expertise without a full-time hire.
Best for: Post-launch products, scaling organisations
The Difference
Practitioner, not vendor
Interlusion isn't a consultancy that read the white papers. It's a company that runs on the same technology it recommends to clients. From code generation to accounting, AI agents do the work under human supervision. The advice comes from daily, production-grade experience.
- Years engineering experience
- 25+
- Intelligent Systems specialisation
- Dipl.-Inf.
- Industry sectors delivered
- 9
- Enterprise ML features shipped
- 3
"We run on agents. We build with agents. We help you do the same."
AI-native isn't a label — it's how Interlusion operates. From code to accounting.
Common questions
Interlusion runs on the same technology it recommends. Autonomous agents handle development, planning, code review, and business operations internally — every day, in production. Advice comes from lived experience, not research papers. This is the difference between a gym that sells memberships and a gym where the trainers work out.
A workflow audit, the AI opportunities ranked by impact and feasibility, and a prioritised roadmap. Typically delivered in 2 to 4 weeks. The output is actionable — not a 100-page report that sits on a shelf.
No. Interlusion handles the technical complexity and provides training so teams can operate the systems independently. The enablement phase is built into every engagement. The goal is capability transfer, not dependency.
Interlusion is model-agnostic. Commercial APIs such as Anthropic and OpenAI, or open-weight models in the cloud or on-premises — the choice depends on the use case, data sensitivity, cost, and performance requirements. No vendor lock-in, no preferred partnerships that bias recommendations.
Security is part of the architecture, not bolted on afterwards. Code and infrastructure are aligned to the OWASP Top 10 and GDPR from day one, and Interlusion can work within existing compliance frameworks. Where data must not leave the company, open-weight models run on the client's own hardware or private cloud.
Yes — that's the explicit goal. Every engagement includes documentation, team training, and knowledge transfer. Interlusion builds systems that clients own and operate. For teams that prefer ongoing support, the Managed AI Operations retainer provides continued expertise.
Ready to go AI-native?
Whether the need is a RAG pipeline, custom agents, or a context architecture that makes agents compound — start with a conversation.