TL:DR;

For a $10B hedge fund, manual data engineering was the ultimate bottleneck, turning high-value alternative data into a weeks-long "firefighting" exercise.

‍Genesis transformed this 4-week manual struggle into a 3-day automated cycle.

By deploying our secure, multi-agent framework natively within their VPC, we autonomously resolved inconsistent schemas and entity mapping—reducing manual coding by 70% and handling 80% of schema drift automatically.

The result? 

The fund achieved 4-6x faster delivery and reallocated a $200k hiring budget, all with only 10 hours of total client effort.

If you want to clear your engineering backlog and scale your data capacity without adding headcount, this is how you do it.

Client Context

A New York-based hedge fund managing approximately $10B in AUM expanded its research program to ingest alternative data feeds such as point-of-sale logs, foot-traffic telemetry, supply-chain traces, mobile-activity aggregates, and e-commerce event streams.

These datasets arrived in inconsistent formats and had to be reconciled to the fund's internal entity graph before analysts could use them. The engineering team maintained custom code per feed, and vendor schema changes often triggered weeks of rework and re-validation. As the number of vendors grew, this bespoke approach became the primary bottleneck, creating the need for a repeatable, scalable ingestion method that reduced manual coding and handled high variability across sources.

Problem Summary

Across datasets, the engineering 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

Result: fragmented logic, limited throughput, and long onboarding cycles. The fund needed a deterministic, reproducible ingestion model without custom code per dataset.

Genesis Intervention and Onboarding Effort

Genesis deployed a multi-agent framework inside the client’s AWS VPC alongside Databricks and the warehouse, replacing bespoke ingestion with a blueprint-driven pipeline.

Deployment and configuration included:

1. Environment Setup. Genesis deployed in the client’s VPC, connected to S3, Databricks, the warehouse, and the entity graph, and kept all processing in-environment on existing tools.
2. Blueprint Creation. Genesis built a blueprint aligned to the entity graph and warehouse, standardizing classification, entity resolution, transforms, validation, and drift handling.
3. System Integration. Genesis wired the agents to the entity graph, S3, Databricks, and warehouse tables end to end, with the client only reviewing access.
4. Initial Validation. Genesis ran two real datasets end-to-end (foot-traffic and supply-chain data) to confirm mapping accuracy, pipeline behavior, and output structure.

Client effort stayed under 10 hours: two 60 to 90-minute sessions, 5 to 10 sample rows per dataset, plus one engineer to validate access and one analyst to review outputs. After deployment, the client just dropped files into S3 and started a run, while the blueprint handled mapping, generated and deployed dbt assets via Databricks and Snowflake, and used Bigeye to detect drift and trigger remediation with no manual coding.

Multi-Agent System Overview

Genesis split responsibilities between two agents so the workflow stayed clean and repeatable.

The blueprint coordinates a clean handoff: Discovery produces the mapping spec, Engineering deploys the pipeline, and failures or drift trigger a spec update and regeneration. This mirrors real teams, with Discovery defining and Engineering implementing.

Blueprint Logic

The blueprint ran a fixed sequence for every dataset: inspect samples, classify the feed, load entity-graph context, generate a mapping spec, then produce and deploy Databricks ingestion and dbt transformations to write normalized warehouse tables, with Bigeye monitoring drift. Because the workflow is blueprint-driven, runs are idempotent, restarts are controlled, and outputs stay consistent across dataset types.

Dataset Examples

To validate the approach end to end, Genesis ran two real datasets through the blueprint:

System Behavior

1. Parallelism. Discovery can profile multiple datasets while Engineering deploys completed specs, with Databricks PySpark handling the heavy parallel processing.
2. Drift Handling. Bigeye flags drift, triggers Discovery analysis, and either auto-remediates via updated specs and regenerated artifacts or escalates to an analyst.
3. Entity Graph Updates. Discovery adds unknown entities using inferred relationships and cross-dataset checks, while Engineering enforces referential integrity with generated tests.

Outcomes

Across the first two datasets:

Engineering Takeaways

The system succeeded by separating mapping from implementation: Discovery defined the spec, Engineering built the pipelines, and the blueprint ensured reproducible, controlled runs. Continuous, event-driven drift detection handled frequent provider changes, while agents generated dbt and SQL artifacts and left heavy processing to PySpark and warehouse engines. Deploying inside the client’s VPC preserved data custody and security.

ROI Summary

Faster cycles translated into clear operational and cost impact:

Overall Impact

Overall, the fund moved to faster research cycles with a steady flow of new alternative data coming online. Higher onboarding throughput removed the main engineering bottleneck, reduced ongoing maintenance work, and lowered operational risk as schema changes became something the system could absorb rather than firefight.