AI Consulting Accelerator
OngoingAI-assisted consulting workflow that grounds discovery, analysis and delivery in industry standards (IREB, BABOK, BPMN, PMBOK, Scrum) via retrieval-augmented generation. The system produces structured, source-cited drafts from unstructured client input — with 13 named, versioned skills, stateful multi-round engagements, and an Organisational Memory layer that connects internal knowledge to current engagements. Locally complete and tested; AWS deployment pending.
Technologies
Problem
Business Analysts, Requirements Engineers and project managers spend time on manual framework lookup and building artifacts from scratch. Knowledge from past engagements does not flow automatically into new ones — patterns go unrecognised, and vocabulary gaps prevent business problems from connecting to relevant technical solutions.
Approach
Three-layer workflow (Discovery → Analysis → Delivery) with two memory types: semantic memory (RAG over 23 indexed frameworks, ~5,380 chunks with source citations) and procedural memory (13 named, versioned skills — invoked by name, not by similarity). An Organisational Memory layer connects five sources (frameworks, skills, projects, radar reports, competitor signals) via a single embedding call per query, with per-source caps and relevance gating that ensures honest "none found" responses.
Result
Locally complete and tested: 21 core features, 70 pytest tests, 16 golden cases in DE and EN. Covered artifacts: framework Q&A with source citations, business problem structurer, requirements structurer (BR/FR/NFR, INVEST user stories), stakeholder analysis (RACI), roadmap generator, stateful multi-round discovery, engagement lifecycle (rename, archive, delete) and report export as Markdown, Word and PDF. Deployment scripts validated; AWS deployment follows.
Learnings
Invoking skills by name rather than by similarity prevents ambiguity and guarantees consistent output contracts. The vocabulary bridge — business-language description metadata in SKILL.md combined with query-side keyword expansion — closes the business-to-technical vocabulary gap without a complex ML pipeline. Per-source retrieval caps prevent dense reports from crowding out actionable skill references. Language drift requires both server-side language detection and explicit output-language instructions in prompts.
Relevance
Demonstrates AI-assisted end-to-end product thinking, RAG architecture grounded in industry standards, clean separation of semantic and procedural memory, and AWS deployment readiness — directly relevant for consulting organisations looking to scale BA/RE processes with AI assistance.