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Venture capital

An AI analyst for venture capital

A digital analyst that runs the top of the funnel autonomously: hundreds of millions of rows a week, a custom multi-agent router, and one-button outreach written back to the CRM.

Client

A prominent US-based venture capital firm

300%+

faster deal review

An AI analyst for venture capital — Venture capital

The challenge

What was in the way

Deal sourcing was manual and slow. Analysts spent most of the week reading datasets rather than evaluating companies.

In a market that moves weekly, the real cost was not inefficiency but the good investments nobody got to.

The solution

What we built

A digital analyst that runs the top of the funnel autonomously. It ingests hundreds of millions of rows on emerging startups each week, transforms them through a layered dbt model on Snowflake, and produces a ranked weekly list with financials, news, founder histories and the metrics the investment committee actually uses. Filters are adjustable by non-technical staff.

Under it sits a multi-agent router we wrote from scratch on LangChain LCEL, with no agent framework. A supervisor plans, then calls specialist sub-agents for financial analysis and founder history, a semantic search tool over the warehouse, and a live web-research agent. Chart generation, memory and conversation state were all built rather than adopted.

When a partner wants to open a conversation, one button drafts outreach in the right register with the right names, and on send the deal is written into their CRM under that partner’s account. The system redeploys weekly on GPU infrastructure, re-indexing embeddings as new data lands.

Key technologies

Data
Python, Snowflake, layered dbt models
Agents
Custom multi-agent router on LangChain LCEL — no framework
Retrieval
Snowflake Cortex Search (arctic-embed-l-v2.0), FAISS + OpenAI embeddings
Research
OpenAI and Perplexity as a hybrid layer, LangSmith tracing
Interface
Streamlit, CRM write-back over API

Impact & results

01

Deal review accelerated by more than 300%

The team covers more high-quality deals in a day than it previously managed in a week.

02

Senior analyst time redirected

From research to negotiation and diligence.

03

A consistent evaluation layer

Companies are assessed against the same criteria every week.

04

Sourcing to CRM in one motion

No re-keying, no deals lost between systems.

Next project

An AI-powered learning platform for aviation training

Start here

Start with a problem, not a brief.

Tell us what is slow, what is manual, what is stalled, or what nobody understands any more. We will tell you honestly whether it is worth building.

Start a conversationinfo@databadger.ai
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