Six services. One outcome-driven model.

We build autonomous AI agents that reason, plan and act, not just automated scripts. Our agents handle complex multi-step workflows: lead qualification, document processing, knowledge retrieval, and process automation. Each agent is fine-tuned on your data and integrated with your existing tools.

Architecture covers single-agent systems through to multi-agent orchestration with shared memory, tool use, and human-in-the-loop escalation. Every deployment includes observability, prompt logging, and audit trails.

Deploy open-source LLMs on your own infrastructure: cloud, on-premises, or air-gapped. Zero data egress. Your prompts, documents and outputs never leave your network. Fully DSGVO-compliant by design.

We deploy and configure the full inference stack: model selection, quantization, continuous batching, KV-cache optimisation, and observability with Prometheus and Grafana.

Connect LLMs to your existing systems. We build RAG pipelines over your documents, knowledge bases and databases. Vector storage, embedding pipelines, retrieval optimisation, and re-ranking, production-ready rather than prototype-grade.

Fine-tune models on your proprietary data for domain-specific reasoning. Includes AI virtual staging with KlugStage, document intelligence, and generative content pipelines for product and marketing teams.

We map your operations, identify the highest-ROI AI opportunities, and produce a prioritised implementation roadmap with cost estimates and success metrics. Build-vs-buy analysis, governance and ethical AI frameworks, and stakeholder alignment workshops are included.

Delivered as a structured 2–4 week Discovery engagement. Output: opportunity map, ROI model, architecture recommendation, and a clear go/no-go decision at week four.

Custom software built to production standards. We build the APIs, dashboards, admin panels and integrations that sit around your AI systems, so the whole product is coherent, not just the model layer.

TypeScript everywhere. React / Next.js frontend. Node or Python backend. PostgreSQL and Redis. AWS or self-hosted. CI/CD, automated testing, and observability from day one.

Concept to shipping product in 6–12 weeks. We scope ruthlessly, cut non-essential features, and build the core loop that lets you validate with real users. Not a throwaway prototype, but production foundations that scale after launch.

Ideal for founders who want to validate an AI product idea before raising. We have shipped MVPs for PropTech, FinTech and B2B SaaS with investor-ready quality and startup-grade speed.