SEIRO IWAMOTOFUKUOKA 33.59°N / REMOTEREV 2026.07
SHEET FIG.1 — /en/work/ai-assistant-platform
CASE STUDY § 01-1

Enterprise AI assistant platform

2020s / Engineer — all phases, requirements through production delivery / Python · React · ECS Fargate · LLM API · RAG
requirements to production
SCOPE — every layer, one person
days-to-weeks
LEAD TIME — per-feature delivery cycle
production grade
DELIVERY — not a stalled PoC

Challenge

Bringing AI into an enterprise leaves a large gap between a PoC and an environment the customer can actually use — security requirements, connection to existing document assets, and assurance of answer quality. Many engagements cannot cross that gap and stall at the PoC stage.

Design decisions

  • Run every layer — UI, API, infrastructure — per feature, removing wait time between layers
  • Place visibility of the answer’s basis (which documents it draws on) at the center of the requirements, making the AI verifiable on the customer side
  • Automate the connection between the customer’s document assets and the RAG platform — ingesting the assets without losing the meaning carried by the original structure
  • For answer-quality problems, verify the data and retrieval layers individually before tuning the generation side
UIAPI / AuthLLMRAG Store

Hover, focus, or tap a node to see the design rationale for each element.

  1. UI — visibility of the answer's basis (which documents it draws on) is placed at the center of the requirements, making the AI verifiable on the customer side.
  2. API — fitted to the customer's security requirements, raised to production grade while keeping the pace of the PoC.
  3. LLM — provider setup according to customer requirements. Generation-quality problems are isolated by layer-by-layer diagnosis before being touched.
  4. RAG store — ingestion from external storage is automated, kept searchable while preserving the meaning carried by the original structure.
FIG. 1-A — Enterprise AI assistant platform

Approach

  • Run each feature from requirements to production on a days-to-weeks cycle, delivered while meeting the customer's security requirements
  • Isolate quality problems layer by layer before addressing them. Verify before tuning
  • Handle surrounding mechanisms such as document ingestion automation the same way, from design to production
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