Most AI agencies build in Python. That works fine for greenfield projects, and badly for the thousands of companies running established .NET, Node or PHP systems, who end up maintaining a second stack, a second deployment pipeline and a second team just to add a chatbot.
We build AI inside the software you already run. Same repository, same CI/CD pipeline, same cloud subscription, same engineers. Where Python genuinely is the better tool, for example model training or a specialised library, we use it behind a clean API rather than forcing a rewrite.
We have delivered .NET and Azure systems for the World Health Organization, a fintech platform, and an automated recruitment system for NITI-KSA. AI is an extension of that engineering work, not a pivot away from it.
What is RAG, and does my business need it?
Retrieval-Augmented Generation lets a language model answer questions using your documents rather than only its training data. You keep your content in a searchable index; when a user asks something, the system retrieves the relevant passages and the model answers from those, with citations.
It is the right approach when you need accurate answers over information that changes: policy documents, product catalogues, support knowledge bases, contracts. It is usually a better first step than fine-tuning, because it is faster to build, cheaper to run, and you can update the knowledge base without retraining anything.
LLM integration into existing applications
Adding AI features to software you already run: summarisation, drafting, classification, semantic search and natural-language querying of your data. In .NET we build with Semantic Kernel and the official model SDKs, deployed alongside your existing services, so there is no second platform to operate.
AI-powered search and recommendations
Replacing keyword search with semantic search that understands intent. Vector embeddings, hybrid retrieval and re-ranking, surfaced through your existing Angular, Vue or React front end.
AI-assisted legacy modernisation
Migrating .NET Framework applications to .NET 10, using AI tooling to accelerate code analysis and translation, with human review on every change. Unsupervised AI migration produces subtle bugs that surface in production months later; reviewed AI-assisted migration is simply faster engineering.
How do you keep AI accurate, safe and affordable?
Every production system we ship includes an evaluation suite that runs against real questions on every change, so accuracy is measured rather than assumed. We add guardrails for prompt injection and data leakage, confidence thresholds that hand off to a human, and per-user and per-day usage caps so a bug or an abuser cannot run up a surprise bill.
- Evaluation harness with a golden question set, run in CI
- Source citations and audit logs for every answer
- PII redaction and role-based access to retrieved content
- Model-agnostic design, so you can switch provider without a rewrite
- Cost dashboards and hard usage limits