AI has genuine practical applications in software engineering and business automation. It also has an enormous amount of hype that leads to wasted investment. We help organisations identify where AI actually adds value, build working implementations, and integrate them into existing systems and workflows - without the buzzword bingo.
What we deliver
LLM integration and RAG systems. We build applications that use large language models effectively: retrieval-augmented generation for knowledge bases, conversational assistants grounded in your data, document processing and extraction, and content generation pipelines. We handle the full stack - embedding generation, vector storage, prompt engineering, output validation, and guardrails.
AI-assisted development workflows. We help engineering teams adopt AI coding tools (Copilot, Cursor, Claude) effectively - establishing guidelines for when AI assistance is valuable, reviewing AI-generated code for quality and security, and integrating AI into development workflows without degrading code quality.
Intelligent automation. We build automation systems that use AI for tasks that previously required human judgement: document classification, data extraction from unstructured sources, anomaly detection, intelligent routing, and natural language interfaces to existing systems.
Chatbots and conversational AI. We build conversational interfaces that actually work - grounded in your knowledge base, connected to your backend systems via function calling, and designed with proper fallback handling, conversation management, and human escalation paths.
Evaluation and testing. AI systems need different testing approaches. We implement evaluation frameworks that measure response quality, factual accuracy, latency, cost, and safety - running automated evaluations against test datasets to catch regressions before they reach users.
Our approach
We start with the problem, not the technology. If a rule-based system or a simple search solves the problem, we'll say so. AI is the right tool when you need to handle ambiguity, natural language, unstructured data, or tasks where the rules are too complex or numerous to enumerate.
We prototype quickly - typically within days - to validate whether an AI approach is feasible for your use case. This avoids the common trap of spending months on infrastructure before discovering that the model can't actually do what you need.
What we don't do
We don't train foundation models. We don't build AI for its own sake. We don't promise that AI will replace your workforce. We integrate existing models (OpenAI, Anthropic, Azure OpenAI) into practical applications that solve specific problems.
Technologies
Azure OpenAI, OpenAI API, Anthropic Claude, Semantic Kernel, LangChain, vector databases (Azure AI Search, pgvector, Pinecone), embedding models, Azure Functions for orchestration, .NET for application layer.