Back office ops · Production

Trace3 Innovation-GPT: Custom LLM and RAG Architecture for Automated Research and Knowledge Management

The problem

The Trace3 Innovation Team manually researched companies by searching the web each time a new funding event occurred, and faced an overwhelming volume of information to track and recall across a large number of enterprise technology solutions.

Workflow diagram · grounded in source
1
Funding event triggers research
trigger
“When new funding events occur, the Trace3 Innovation Team manually researches the company and solution by searching the web”
2
Spider and scrape company websites
integration
“spiders and scrapes the websites of newly funded enterprise technology solutions”
3
Compile profiles and generate metadata
ai_action
“compiles the data into robust company profiles, aggregates the data into structured data records (JSON) based on category, generates verbose metadata annotations for those records”
4
Vectorize and store for retrieval
ai_action
“vectorizes the data, and then stores that data for subsequent retrieval”
5
RAG chatbot query interface
ai_action
“allows real-time interaction with that data via a natural language processing (NLP) or "chatbot" interface”
6
Human fact-check and supplement
human_review
“Every output is thoroughly fact-checked and supplemented with manual research and analysis to ensure our team consistently arrives at the best possible conclusions”
Reported outcome

Innovation-GPT streamlines the team's research and knowledge management workflows, enabling natural language querying of aggregated company profiles and reducing manual research effort.

Reported metrics
Operational efficiency improvementsignificant improvements in efficiency and innovation
Reported stack
LLMsRAGNLP
Source
https://blog.trace3.com/beyond-the-hype-real-world-custom-implementations-of-generative-ai
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Innovation-GPT streamlines the team's research and knowledge management workflows, enabling natural language querying of aggregated company profiles and reducing manual research effort.

What tools did this team use?

LLMs, RAG, NLP.

What results were reported?

Operational efficiency improvement: significant improvements in efficiency and innovation (source-reported, not independently verified).

How is this back office ops AI workflow structured?

Funding event triggers research → Spider and scrape company websites → Compile profiles and generate metadata → Vectorize and store for retrieval → RAG chatbot query interface → Human fact-check and supplement.

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