Technologies

AI

LLMs, RAG, AI agents and MCP, production AI engineering embedded in enterprise software, not isolated pilots.

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AI engineers who have shipped it.

AI is part of how Axesscom builds software today. Our teams integrate large language models, retrieval-augmented generation, autonomous agents and Model Context Protocol servers into platforms that run in regulated, high-volume enterprise environments.

We do not treat AI as a demo layer. Every AI capability we ship includes monitoring, fallbacks, security boundaries and documentation, the same engineering discipline we apply to backend and frontend systems.

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Technology depth

What our AI engineering teams deliver.

Enterprise AI from prototype to production

Our AI engineers work daily with OpenAI, Anthropic, Google Gemini and open-source models. We design architectures where AI augments business workflows, document processing, customer support, content generation, forecasting and agent-driven automation, without compromising data governance.

AI capabilities we build and operate:

  • Retrieval-Augmented Generation (RAG) over private document corpora and knowledge bases
  • AI agents for ticketing, customer support, document intake and workflow automation
  • Model Context Protocol (MCP) servers exposing business APIs as structured AI tools
  • Document intelligence: classification, extraction and summarisation at scale
  • Multi-model routing with cost, latency and quality optimisation
  • Prompt engineering, evaluation frameworks and regression testing for AI outputs
  • Human-in-the-loop review workflows for high-stakes decisions
  • AI content generation pipelines (Guestastic: event copy, themes, marketing assets from flyers)
  • Demand forecasting, pricing suggestions and recommendation engines
  • Enterprise guardrails: RBAC, audit logs, PII filtering and compliance-aware data boundaries

From legal document intelligence for global law firms to event platforms like Guestastic, our AI work ships inside real products used by thousands of users, not slide decks.

How we staff

Finding the right AI engineers for your team.

We review your stack, team culture and project needs before recommending engineers, not before sending a generic CV.

01Stack assessment
02Engineer matching
03Technical onboarding
04Continuous delivery