AI-Powered Data & Consulting Platforms

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, demo-ready.
• 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 & live.
• Collects, classifies and indexes signals across tech, competition & regulation.
• One shared infrastructure for every app: ingestion, index, retrieval, agents, scheduling.
• 15 deployed agents (Collector → Analyzer → Reporter → Indexer → Notifier).
• Cost-aware: ~$5–10/month, SHA-256 dedup, retention & lifecycle rules.

AI Platform · Live

What runs in production

• Public RAG Demo — semantic search + grounded, cited answers.
• Technology · Regulatory · Competitor Radar — scheduled Lambda pipelines (weekly/monthly).
• 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 mapping: NIST AI RMF ~70% · GDPR/DSG ~75% · AWS Well-Architected ~85%.
• Compliance-first: mapping completed before implementation.

Project 2 · Consulting Accelerator

AI-assisted consulting workflow

• Grounded in industry frameworks: IREB, BABOK, BPMN, PMBOK, Scrum.
• A workflow across three layers: Discovery → Analysis → Delivery.
• Turns unstructured customer input into structured, cited drafts.
• Language lock DE/EN — output always follows the input language.
• Every output: "AI-generated draft — requires human review."

Consulting · Capabilities

From a question to a structured analysis

• Framework Q&A (RAG) with source citations.
• 13 named, versioned skills — invoked by name, not by similarity.
• Stateful engagements: multi-round discovery → synthesis (one case file).
• Requirements (BR/FR/NFR), INVEST user stories, quality flags against IREB.
• Export the whole case file as Markdown / Word / PDF.

Consulting · The senior edge

Pattern recognition & Organizational Memory

• Challenge phase & sufficiency gates — understand before solutioning.
• Pattern Library — surfaces 0–2 resembling project archetypes "to validate".
• Organizational Memory — connects existing internal knowledge (skills, projects, radar)
• as cited references; it connects knowledge, never invents it.
• Quality eval harness: language, disclaimer, citation integrity, 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, cost-aware by design.
• 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: isolated DBs for sensitive/client data, never mixed.
• Cost-controlled: dedup, retention, limits — production-grade, not a toy.

What this means for you

From concept to measurable value

• Faster discovery & structuring — weeks become hours, as a reviewable draft.
• 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