All services
Practice 03
AI Engineering
LLMs wired into real systems, with real guardrails.
Not generic AI consulting. Production LLM integration, private deployments, MCP-based tooling and agents that operate inside your existing platform.
How we position it
“Ask your AI assistant to investigate a failed deployment, inspect the logs, identify the likely cause and open a remediation PR.”
Capabilities
LLM integration in production apps
Private & local LLM deployments
MCP-based integrations
AI agents with tool access
AI inside existing SaaS products
AI-powered developer tooling
Internal AI assistants
AI infrastructure & serving
LLM security & prompt hardening
Evaluation and regression harnesses
AI automation of ops workflows
Data access patterns & retrieval
What you get
AI on top of your platform
Assistants that speak to your IDP, CI, cloud accounts and observability stack through governed MCP tools.
Private by design
Self-hosted or VPC-bound models where regulation, data residency or client contracts require it.
Measured, not vibes
Evaluation suites, cost ceilings and audit trails so an AI feature can be operated like any other service.
Typical engagements
- →AI opportunity mapping across your engineering workflow
- →Production LLM feature build inside your product
- →Internal assistant and MCP tool platform