Tier-1 bank · Paris
BNP Paribas: Reference Book PO, from React/Redux to product AI agents.
Three Abbeal engineers at the core of the PO Marketplace. React/Redux/Node platform initially, now augmented with a product RAG, Claude agents for PM assistance, and an event-driven Kafka layer to scale.
KPI
RAG
PO product catalog
Duration
Engagement multi-année
Team
3 engineers
Hub(s)
Paris
BNP Paribas. Reference Book PO Marketplace. Three Abbeal engineers embedded on the product side to build the platform that structures the product catalog and PO workflows of a tier-1 bank.
Starting point (2018-2019)
Sebastien, Raphael and Ulric embedded in the Reference Book team. Initial stack React + Redux + Node, on-prem deployment, integration with core banking. Mission: pull the PO Marketplace out of Excel hell and run it on a stable, governed, auditable web platform.
What was delivered
- Unified PO front-end (catalog + active POs + review workflows)
- Node API for PO step orchestration and notifications
- Regulatory reporting and audit trail for internal controls
- Documentation and training for internal PMs on the platform
- Integration with existing BNP tools (SSO, AD, internal ticketing)
The stack we ship today
On the same problems today (financial product catalog, PO workflows, multi-criteria search across tens of thousands of items), here's what we assemble:
- Next.js 16 + React 19 on the front, Server Components by default, Edge runtime on listings
- Apache Kafka to decouple PO workflows and enable event replay
- Product RAG on pgvector: semantic search across the catalog (10k+ products, 50+ typed attributes)
- Claude Sonnet agents via LangGraph to assist PMs: product sheet drafting, regulatory matching, contract term wording
- AWS Bedrock to host LLMs on the compliance side + LangSmith observability for traces
- Auth0 + fine-grained RBAC for data scoping per desk and jurisdiction
Why it's hard (and why few succeed)
- Fragmented product data: 6+ source systems, different conventions per desk
- Required latency: a PM won't read an answer at > 2 seconds
- ACPR + EBA + internal audit compliance on every agent suggestion
- Citation required: no answer accepted without pointing to the source in the reference
- Coexistence of on-prem Mistral (sensitive data) + Claude API (non-sensitive workflows)
What this engagement taught Abbeal
BNP was our first deep dive into financial product PO. We learned to ship in a commit-audited environment, to code for PMs who don't forgive latency, to document for 6-month internal control reviews. That's the experience we replay today on product RAG for other European tier-1 banks.
// Read next
Luxury jewellery & watchmaking · Genève + Paris + Tokyo
Cartier: from audit to in-house private LLM.
Compass (front + back architecture audits), Mapper (watchmaking + jewellery product generator), competitive data ETL on BigQuery, and now a private LLM fine-tuned on Cartier's own infra. A long-term tech partnership on the data and AI stack of a luxury house.
LLM privé
fine-tuned on Cartier infra
Digital banking / FinTech · Tokyo (Tamachi)
Money Forward: data backbone of a brand-new digital bank in Tokyo.
Money Forward, a Japanese FinTech leader listed in Tokyo, partnered with a top-tier Japanese banking group to launch a brand-new digital bank built from scratch. Abbeal partners on the Data Engineering side: designing and operating the Data Hub (Databricks + Delta Lake + dbt + AWS Tokyo) serving JFSA reporting, AML, risk management.
Data Hub
from-scratch digital bank Tokyo
Real estate / Property · Paris + Bordeaux
Pichet: from Symfony/eZplatform to AI Vision on property floor plans.
Premium French property developer. Catalog platform rebuilt (Symfony 4/5 + eZplatform + K8s) then modernized: Next.js 16, headless CMS, Claude Vision interpreting 2D/3D floor plans, semantic search via pgvector.
AI Vision
2D/3D floor plans analysis
