AI-Powered Data & Consulting Platforms

Two production-grade AI systems — grounded, cited, cost-aware
Luciano · Bridging Data · Data Engineering · Business Analysis · Requirements Engineering
Positioning

From data engineering to AI-assisted consulting

  • Two complementary systems on one shared, reusable platform.
  • AI Platform — autonomous knowledge & intelligence platform, live on AWS.
  • AI Consulting Accelerator — AI-assisted BA/RE/PM workflow, live as a private instance.
  • Common thread: every output is grounded and source-cited — never invented.
  • Principle: AI assists, humans decide.
Project 1 · AI Platform

Autonomous knowledge & intelligence platform

  • Cloud-native on AWS (eu-central-1) — 6 phases complete, 5 of them live in the cloud.
  • Collects, classifies and indexes signals across tech, competition & regulation.
  • One shared infrastructure for every app: ingestion, index, retrieval, agents, scheduling.
  • 18 deployed agents (Collector → Analyzer → Reporter → Indexer → Notifier).
  • Operating cost known & monitored: ~USD 96/month (Aug 2026), dominated by fixed costs.
AI Platform · Live

What runs in production

  • Public RAG Demo — semantic search + grounded, cited answers.
  • Technology · Regulatory · Competitor · Token Price Radar — scheduled Lambda pipelines.
  • Knowledge Platform — Agent Center, AI Chat, Skills Hub, Reports dashboard.
  • Content pipeline — LinkedIn drafts for human review.
  • Reports auto-indexed into the vector store after every run.
AI Platform · Enterprise

Corporate LLM — enterprise-ready

  • Dedicated, isolated stack: separate encrypted RDS, own IAM role.
  • Cognito authentication + RBAC + API Gateway JWT authorizer.
  • Per-user audit logging; GDPR Art. 17 on-request deletion.
  • Compliance-first — self-assessment before implementation (not an audit):
  • NIST AI RMF ~70% · GDPR/DSG ~75% · AWS Well-Architected ~85%.
  • Database on demand: restored in ~6 min from an encrypted snapshot.
Project 2 · Consulting Accelerator

AI-assisted consulting workflow

  • Grounded in frameworks: IREB, BABOK, BPMN, PMBOK, Scrum, EU AI Act, NIST.
  • A workflow across three layers: Discovery → Analysis → Delivery.
  • Turns unstructured customer input into structured, cited drafts.
  • Live as a private single-admin instance (Cognito, no public sign-up).
  • Language lock DE/EN; every output: "AI-generated draft — requires human review."
Consulting · Capabilities

From a question to a structured analysis

  • Framework Q&A (RAG) with source citations.
  • 17 named, versioned skills — invoked by name, not by similarity.
  • Including: AI readiness, AI governance (EU AI Act), compliance maturity, value articulation.
  • Requirements (BR/FR/NFR), INVEST user stories, quality flags against IREB.
  • Engagements: multi-round discovery → synthesis → export as Markdown / Word / PDF.
Consulting · The senior edge

Pattern recognition & Organizational Memory

  • Understand before solutioning: deeper questions across multiple discovery rounds.
  • Pattern Library — surfaces 0–2 resembling project archetypes "to validate", deliberately late.
  • Organizational Memory — connects existing internal knowledge (skills, projects, radar)
  • as cited references; it connects knowledge, never invents it.
  • Quality eval: 24 test cases (DE/EN) — language, disclaimer, citations, optional LLM judge.
Shared foundation

One reusable AI platform

  • Shared `aiplatform` package: loaders, chunker, embedder, vector store, LLM abstraction.
  • One pgvector store, scoped by `app_name` — a cross-source index, no new infrastructure.
  • No vendor lock-in: LLM provider swappable (OpenAI / Anthropic).
  • Serverless (Lambda + API Gateway), SHA-256 dedup — only changed content is re-embedded.
  • Bilingual (DE/EN), grounded, cited, auditable.
Principles

What you can rely on

  • Grounded & cited — no fabricated facts, no fabricated sources.
  • AI assists, humans decide — drafts, not finished deliverables.
  • Confidentiality: sensitive data in an isolated, encrypted DB — before real client
  • data, the Accelerator also moves to its own dedicated DB.
  • Cost control: budget alerts, hard-capped LLM spend, resources only when needed.
What this means for you

From concept to measurable value

  • Faster discovery & structuring — cited drafts instead of a blank page.
  • Reusable knowledge: prior projects & market signals as cited references.
  • Enterprise foundation: auth, RBAC, audit, compliance already demonstrated.
  • Proven, not claimed — live on AWS and demonstrable end-to-end.
  • Let's talk about your use case → bridging-data.com