A New York hedge fund managing $5B in assets under management, looking to streamline its research program, incorporating alternative data feeds.

Client Context

The hedge fund had expanded its research program to incorporate alternative data feeds—including point-of-sale logs, foot-traffic telemetry, supply-chain traces, mobile activity, and e-commerce streams. Each feed required custom ingestion logic, entity resolution against the fund's internal graph, and mapping to a canonical schema before analysts could run queries or train prediction models.

These datasets arrived in inconsistent formats—schema variations, non-standard field names, missing metadata, and frequent drift often in semi-structured json. Mapping vendor identifiers (e.g., merchant IDs, store codes, product SKUs) to the fund's internal entity graph was partially automated with extensive manual override. As the vendor roster expanded from a handful to dozens, bespoke ingestion code became the primary bottleneck.

Problem Summary

The team encountered:

• Inconsistent schemas across providers
• Non-standard field naming
• Missing or partial metadata
• Non-uniform geographic and demographic encodings
• Frequent schema drift
• Fully manual entity resolution and mapping

Each feed required bespoke PySpark notebooks, custom entity-mapping scripts, and one-off quality checks. The result was fragmented logic scattered across repos, slow onboarding cycles, and brittle pipelines that broke whenever vendors updated their formats.

Genesis Intervention and Onboarding Effort

Genesis Computing deployed its multi-agent platform into the client's AWS VPC. The system runs alongside the fund's data lake environment, containing raw vendor feeds, and integrates with their dbt infrastructure.

Deployment and configuration included:

1. Environment Setup: Installed Genesis within the client VPC, configured Databricks connection
2. Blueprint Creation: Co-developed a declarative blueprint defining how feeds should be discovered, mapped, and ingested, starting from the Genesis-provided Source to Target mapping blueprint
3. System Integration: Connected Genesis to the fund's dbt repository
4. Initial Validation: Ran the system on three existing feeds to learn about correct mapping and output

Total client effort stayed under 10 hours—two sessions of 60-90 minutes each. From that point forward, the system operated autonomously, ingesting new feeds and handling schema changes without human code review.

Story Highlights

1. Ingestion latency: Reduced from 3-4 weeks to 3-5 days
2. Human-written code: Reduced by 60-70%
3. Pipeline delivery speed: 4-6x faster
4. Drift resilience: ~80% schema changes handled automatically

Outcomes

Across the first two datasets:

Engineering Takeaways

The system succeeded because it integrated tightly with the client's existing stack (Databricks, dbt), operated within their VPC for security, and required minimal configuration effort. The declarative blueprint abstracted complexity while preserving flexibility, clients can override mappings, add custom transforms, or inject domain-specific rules. The agent architecture separated discovery (understanding the data) from engineering (building the pipeline), allowing each agent to specialize and iterate independently. Pull-request-based approval workflows gave the data team control without forcing them to write code. The result: alternative data ingestion became a scalable, repeatable process rather than a manual, per-feed effort.

ROI Summary

Faster cycles translated into clear operational and cost impact:

Overall Impact

By automating discovery, mapping, and pipeline generation, Genesis Computing enabled the hedge fund to scale its alternative data program without proportional increases in engineering headcount. The research team gained access to more datasets, faster, with higher confidence in data quality. The data engineering team shifted from writing bespoke ingestion code to reviewing and approving agent-generated pipelines—a higher-leverage activity. The fund's ability to react to market opportunities improved as new datasets became available to analysts in days rather than weeks. The agentic data engineering system delivered faster research cycles, lower operational overhead, and a defensible competitive advantage.