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Independent product build

AiAdvisor — Enterprise AI Transformation Advisor

A multi-agent consulting platform that turns business documents into evidence-grounded AI opportunities, conservative ROI estimates, governed approvals, rollout roadmaps, and executive-ready reports.

Disclosure: AiAdvisor is an independent working MVP. The case study describes implemented capabilities and test coverage; it does not claim production customer adoption or realized business ROI.

The Challenge

Traditional AI transformation assessments are slow and often separate recommendations from their supporting evidence, financial assumptions, and governance requirements. AiAdvisor was built to turn mixed business documents into a transparent, prioritized transformation plan while keeping consequential decisions reviewable and human-owned.

System Workflow

  1. 01Validate and ingest PDF, DOCX, XLSX, CSV, TXT, and Markdown documents; extract, chunk, embed, and screen them for PII.
  2. 02Use a LangGraph pipeline to map business processes and retrieve relevant source evidence through pgvector-backed RAG.
  3. 03Have specialist Process Analyst, Solution Architect, Financial Analyst, Risk Officer, and Engagement Lead agents produce and challenge recommendations.
  4. 04Merge critiques deterministically, rank opportunities P1–P3 in Python, and expose the assumptions behind every savings estimate.
  5. 05Let users refine ROI inputs, approve or reject opportunities, and generate a 30/60/90/180-day roadmap that excludes rejected initiatives.
  6. 06Produce an in-app executive report and downloadable PDF, with model activity, evaluations, and consequential actions retained for review.

Human Controls

Source evidence and financial assumptions remain visible alongside each recommendation.

Financial and risk agents challenge generated proposals, while deterministic code controls final ranking and ROI calculations.

Human approve/reject decisions determine which opportunities enter the roadmap.

Workspace isolation, PII screening, audit records, provider timeouts, and resumable checkpoints bound operational risk.

Impact & Evidence

01Delivered an end-to-end product spanning document ingestion, RAG analysis, multi-agent review, prioritization, governance, roadmap planning, and reporting.
02Made AI-generated savings estimates inspectable and editable rather than presenting opaque model output as fact.
03Added a resilient seeded demo, provider fallback, bounded timeouts, and checkpoint recovery for dependable demonstrations.
04Built automated coverage across authentication, ingestion, scoring, ROI, approvals, auditing, reporting, recovery, and core UI behavior.

Artifacts