iKeeLabs
An architecture practice for data, integration and AI
iKeeLabs is a company co-founded by Huu Huy Truong and Lorenzo Caraffini. Its work centres on enterprise, data and AI architecture: cloud-native data platforms, enterprise integration and applied AI services. The Architecture Lab shares educational reference architectures, while Expertise brings together practice areas and sourced insights.
Architecture Lab studies are illustrative, not client deployments. Projects presents RevNLabs Dynamic Pricing, a component being designed for a property management system.
- 01Architecture LabEducational reference architectures. Illustrative studies, not client deployments.
- 02Our TeamThe architecture practice and its co-founders, Huu Huy Truong and Lorenzo Caraffini.
- 03ExpertiseData, integration, cloud and applied AI topics from Huu Huy Truong's resume.
- 04ProjectsRevNLabs Dynamic Pricing, a component being designed to anticipate demand and guide rate decisions. The figures are illustrative.
- 05ServicesCo-founder-led AI adoption support for small businesses, delivered remotely in English or French with optional onsite sessions.
- 06ContactHow to reach the co-founders.
01 · Selected studies
Architecture Lab
A short selection from the lab. Each study walks through one reference architecture: how its parts connect, the design decisions and the trade-offs.
Reference architecture - illustrative, not a client deployment
01 · LLM & Agentic AI
How Enterprise RAG Actually Works
Follow enterprise documents from permission-aware indexing to evidence-based answers, and see why abstention belongs in the design.
Intermediate
02 · LLM & Agentic AI
From Prompt to Action: Anatomy of an AI Agent
See how a proposed tool call becomes an authorized business action through deterministic checks, approval and validated feedback.
Intermediate
03 · LLM & Agentic AI
Multi-Agent Systems Without the Hype
See when a coordinator and scoped specialists earn their cost, and when one bounded agent should write the result.
Intermediate
04 · LLM & Agentic AI
An AI Copilot Inside a Business Process
Follow a support ticket from an authorized draft to a human-approved update, and stop the write when the proposal is rejected or invalid.
Intermediate
02 · Our Team
Co-founders
Projects
Insights
Architecture Decisions: Balancing Cost, Latency and Control
Cost, latency and control pull against each other in every AI system. A useful decision record names the one a use case favors, what it gives up, and the measurement that would reopen the choice.
Where Deterministic Workflows Should Bound AI Agents
An agent earns its place where the path cannot be written down in advance. Authorization, limits, validation and approvals belong in deterministic code around it, so the model works inside a boundary it cannot change.
Why AI Architecture Starts with Evaluation
A component built on a language model has no testable specification until its evaluation exists. Writing the cases and the acceptance bar first turns model, retrieval and prompt choices into measured architecture decisions.