Intelligence without Action is just a novelty.
Most AI pilots fail because they treat AI as a magic box. They get chatty, hallucinating toys.

Reliable AI for Complex Environments
We bring Generative AI out of the sandbox and into your core architecture. Laava focuses on deep integration: embedding intelligent agents directly into your existing stack to automate complex workflows and modernize legacy systems. We don't build isolated chatbots; we engineer resilient, scalable AI infrastructure that executes work rather than just talking about it. No throwaway POCs, just secure, auditable impact.
Core services
AI Integration & Orchestration
Embed LLMs and agents into your existing tools and APIs (ERP/CRM/ITSM/warehouse systems). • Event-driven flows, not cron spaghetti • Idempotent, testable components • Versioned prompts and policies
Data & Metadata Architecture
Make your data findable, trustworthy, and governed so AI can use it safely. • Schemas, lineage, and entity modeling • ETL/ELT pipelines with validation and contracts • Access controls, redaction, and PII handling
Retrieval-Augmented Generation (RAG) & Knowledge Search
Connect knowledge across docs, tickets, code, and databases. • Fit-for-purpose chunking & embeddings • Hybrid retrieval (semantic + keyword + metadata filters) • Cited, auditable answers with confidence signals
AI Agents for Operations
Task-specific agents that coordinate with your systems, not just chat. • Scoped responsibilities, explicit tools, safe guards • Human approval steps where required • Full telemetry for actions and outcomes
Cloud & MLOps for AI Workloads
Infrastructure that keeps latency low, costs sane, and security tight. • Containerized services, autoscaling, secrets, and key management • Prompt/config registries, feature stores, model gateways • Observability: traces, metrics, red-teaming, drift detection
Selected work
FAQ
Ready to integrate AI into your operations?
Let's map your systems and identify the highest-ROI flows.
We'll review your architecture, data, and constraints, then propose a focused pilot with timeline and budget.