Case studies · B2B software

LLM-based automated ecosystem mapping (ECOMAPPER)

In short

A document-intelligence pipeline that reads large, heterogeneous document collections and extracts stakeholders, relationships, needs and pain points as structured, schema-driven data — feeding the client’s ecosystem-mapping platform.

Client: Swiss B2B software SME — ecosystem-mapping platform
Timeline: 2025 · 8–12 weeks
Status: Prototype and integration delivered (2025).
Runs on: Cloud-deployed prototype

The challenge

Analysts extracted stakeholders, relationships and needs by hand from large collections of heterogeneous documents before any mapping could start — slow, inconsistent and impossible to scale.

What we built

Mentis delivered the pipeline and its integration in a defined 48 person-day engagement:

Schema-driven LLM extraction

GPT-4-based extraction of structured ecosystem information — stakeholders, relationships, pain points — from workspace documents, validated against a defined schema.

Graph representation

Extracted entities and relations stored in Neo4j, ready for ecosystem mapping and queries.

Analyst application

A Streamlit application to run, review and correct extractions.

Platform integration

Results fed into the ECOMAPPER platform; designed to process document collections scaling into the thousands.

Results

✓ Automated extraction of structured ecosystem information from unstructured documents
✓ Designed for document collections scaling into the thousands
✓ Replaced a significant share of manual document reading and structuring

Qualitative outcomes; hours saved were not measured, so no figure is claimed.