Venture Capital

Data Platform, AI Workflows & Custom Applications for a Leading VC Firm

A top-tier venture capital firm managing over $10 billion, investing across enterprise software, fintech, healthcare, and commerce.

Duration

Multi-year

ongoing

Team

Data engineers

full-stack engineers

Services

Data Platform

Data Engineering

AI/ML

Custom Applications

Workflow Automation

Tech Stack

Snowflake

AWS (Lambda, Glue, API Gateway, SQS, MWAA)

Airflow

dbt

Looker

CRM integrations

Slack

Notion

The Challenge

What stood in the way.

The firm had too much data and no unified way to harness it. Insights were fragmented across tools and teams, resulting in slower decisions, duplicated work, and the risk of missing high-potential deals. They needed a centralized platform that consolidated internal and external data, embedded intelligence directly into investor workflows, and met enterprise-grade security standards.

The technical bar was high: the platform needed to support both batch and event-based processing, power custom web applications, and integrate seamlessly with the firm's existing collaboration tools — all while meeting stringent security, DevOps, and governance requirements.

The Solution

How we built it.

We designed and delivered a modern data platform that gave the firm a single source of truth, near-real-time insights, and self-serve analytics.

15+

data sources

30+

workflows

Unified data hub

15+ internal and external data sources (CRM, deal databases, enrichment providers, employment data, market intelligence platforms) consolidated into Snowflake, creating a consistent foundation for decision-making.

Custom CRM pipeline

off-the-shelf integration tools couldn't handle the firm's scale (initial syncs took weeks). We built a custom pipeline with Airflow orchestration, event-driven processing via AWS Lambda and SQS, and webhook handling through API Gateway. This reduced sync times to near-real-time for core entities.

Advanced lead acquisition

automated sourcing combining signals from professional networks, market databases, and social platforms with multi-source enrichment. The system surfaces opportunities that manual research would miss.

Business reporting automation

key metrics (including deal coverage ratios) automated and delivered via Slack with feedback loops. A custom Slack app allowed investors to review, amend, and respond to automatically classified data.

Self-serve BI

transition from legacy dashboards to Looker, empowering investment and operating teams to explore dbt-modeled datasets and build their own analyses without engineering support.

Custom data applications

secure web applications for full-text search across the firm's network of people and companies, supporting sourcing, diligence, and portfolio company recruiting.

AI applications

LLM-based zero-shot classification for data engineering tasks (inferring industry domains, founder experience). Automated ingestion and analysis of board decks and operating reports, extracting entities and metrics to feed downstream pipelines and reporting.

Results

What we delivered.

15+ data sources

integrated into a unified platform.

30+ workflows and applications

deployed — from sourcing and diligence to portfolio support.

Investments sourced

through opportunities discovered by automated, data-driven processes — validating the platform's strategic impact.

Extensible foundation

that compounds in value with each new integration or workflow added.

Why This Matters

For a firm investing at this scale, the difference between a good deal and a missed one often comes down to data.

This platform consolidated 15+ sources into a single intelligence layer — surfacing opportunities that manual research would miss, automating workflows that used to consume analyst hours, and embedding AI directly where investors already work. The platform doesn't just support investment decisions. It contributes to them.

Other case studies in the industry

More work in Venture Capital.

Venture Capital

ML-Powered Investment Intelligence Platform for a Growth-Stage VC Firm

The strongest demonstration of a complete data platform that powers ML in production. The Deal Scoring model with 300+ features is the most concrete proof point of turning commodity data into proprietary investment signal.

300+ML features

2models in production

View Case Study

Venture Capital

Portfolio Financial Platform for a Growth-Stage VC Firm

The clearest example of building a mission-critical business application — not just data infrastructure — for a VC firm. The platform became the financial backbone of the firm's investment operations.

100+portfolio companies

$1M–$1.5Minvestment

View Case Study

Venture Capital