AI and automation

Automating processes and building AI into useful, measurable and safe applications.

Generative AI and LLM integration

Integrating language models into products and processes, with controlled and measurable outputs.

Designing agents that reason, use tools and collaborate on business tasks.

Grounding model answers in the organization's documents, with verifiable sources.

Finding information by meaning rather than keywords, at scale.

Reducing repetitive work by combining business rules, models and integrations.

Internal or public assistants, connected to the right sources and bounded by guardrails.

Measuring quality, cost and latency of AI systems to improve them reliably.

Connecting model providers to existing applications, with abstraction and fallback.

Preparing, cleaning and indexing the data that feeds models.

Moving from prototype to operated service: containers, inference, monitoring, costs.

Structuring instructions, tool calls and chains for stable results.

Guardrails, access control, prompt-injection protection and traceability.