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

The Challenge

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

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.

hub

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.
sync_alt

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.
travel_explore

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.
analytics

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.
bar_chart

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.
web

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.
psychology

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

hub

15+ data sources

integrated into a unified platform.
account_tree

30+ workflows and applications

deployed — from sourcing and diligence to portfolio support.
trending_up

Investments sourced

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

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.

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