Depth from the team building the AI Agent Platform.
LLM security is not a single filter. Here is how AICLUDE spreads the responsibility across input, RAG, memory, prompt, and output — and keeps PII searchable without giving up on encryption.
Retrieval looks fine, but answers still drift. Here is why teams move from Classic RAG to Graph and Agentic RAG — and the exact mix AICLUDE runs in production.
Agents built on a single LLM call look great in demos and fail in production. Here is how AICLUDE decomposes agent execution into 8 explicit stages, and which failure mode each stage is responsible for.
Marketing used to live in campaign-sized batches: launch, review, repeat. A continuous loop — signal, draft, publish, learn — changes the unit of work from a month to an hour.
A four-month PoC with STNET (Dec 2025 – Mar 2026) running CLU Agent Station kiosks across Kagawa retail sites. Just two kiosks detected more than 6.4 million pedestrians, and nearly two out of three responded when the AI spoke first.
Storefront signage has long been a "big TV" that plays whatever was uploaded. That era is ending. The next generation of signage plans, produces, and measures its own content, on its own loop.
A hotel, a hospital, a bank, a municipal office — every visitor remembers how the first 30 seconds felt. Putting an AI concierge at that moment is less about replacing staff and more about making sure the first moment is never empty.
A skill — a specific capability an agent uses — is usually written once and frozen. That is why it gets stale. A skill that measures itself and iterates on its own weak spots gets sharper over time.
Data residency is the quiet reason many AI projects stall. A platform that runs the same way in the cloud and on your own servers removes the stall, and lets you change your mind later.