PROFESSIONAL SERVICES
LLM AND KNOWLEDGE INFRASTRUCTURE
Consulting and professional services firms are sitting on years of valuable IP they cannot access. We unlock it.
Services
LLM and Knowledge Infrastructure
Data Centralisation
AI-Powered Search
Data Strategy
Strategy consultancies, research firms, and specialist advisories accumulate enormous institutional knowledge: frameworks, benchmarks, client research, market analysis. It ends up buried in unstructured archives and every new engagement starts from scratch. Dataverse builds AI-powered knowledge infrastructure that makes that IP searchable, retrievable, and usable in real time.
Years of client work were sitting in folders that nobody opened. Every time a new project came in, the same frameworks were being rebuilt from scratch. Once it was possible to ask a question and get a referenced answer drawn from existing work, the value was immediately obvious.
500+
Documents indexed and searchable
3hrs
Saved per engagement on research
Plain English
Query the entire knowledge base
The Challenge
Professional services firms generate high-value knowledge continuously but almost none of it is structured for reuse. Deliverables live in shared drives. Research is in email threads. Frameworks are in files nobody can find. There is no discovery layer, no way to query across the archive, and no mechanism to surface relevant prior work when a new engagement begins. The result is that the same thinking gets rebuilt repeatedly, senior time is wasted pointing people to existing work, and the firm's collective intelligence compounds at a fraction of its potential rate.
Hundreds of documents and deliverables in unstructured, unsearchable storage
New team members rebuilding frameworks that already existed, wasting time and cost
No way to identify which prior work was most relevant to a new mandate
Senior staff spending hours per week directing others to existing material
The solution
Dataverse designed and built an AI-powered knowledge infrastructure. Documents were ingested, parsed, chunked, and embedded using a RAG pipeline built on Airbyte, pgvector, and Pinecone. Claude API powered the natural language query interface with source-linked answers drawn from real documents. Supabase managed the operational data layer. BigQuery and dbt provided usage analytics. A clean search interface was deployed via Vercel. Dagster orchestrated the ingestion pipeline to process new documents automatically as they were added.
Full document archive ingested, embedded, and indexed and accessible via natural language
RAG pipeline built on pgvector and Pinecone for high-accuracy semantic retrieval
Claude API powering the query interface with referenced, source-linked answers
Dagster pipeline auto-ingesting new documents as they are added to the archive
Looker Studio usage dashboard showing most-accessed assets, query patterns, and knowledge gaps