A global commercial real estate research organization that produces comprehensive quarterly market reports across dozens of markets and property types. Its research function spans more than 130 researchers responsible for market data, analysis, and published commentary.

The client produced comprehensive quarterly market reports through a heavily manual process, which limited speed, consistency, and scale:

• Manual and labor-intensive: roughly 130 researchers collected data, analyzed trends, built charts, and drafted narrative commentary for each report.
• Error-prone and inconsistent: heavy reliance on manual interpretation and formatting increased the risk of errors and created variability across markets.
• Time-consuming: long production cycles reduced the firm's ability to respond quickly to market shifts and emerging insights.
• Limited scalability: the level of human effort required constrained the firm's ability to expand market coverage or increase reporting frequency.

The client, with Turing's support, built a fully AI-driven, end-to-end research reporting platform that automates report production:

• Data sourcing and grounding: data is pulled from the firm's Snowflake warehouse using reusable, parameterized SQL templates defined by administrators, keeping every report grounded in governed source data.
• Multi-agent architecture: five agents run in a hybrid sequential and parallel model to minimize latency, handling initial narrative generation, tone normalization, structure, data and unit checks, and final validation, with automatic regeneration when validation fails.
• Human-in-the-loop governance: both original AI outputs and human-edited versions are stored with full revision history, so researchers act as reviewers and approvers rather than primary authors.
• Observability and tracing: all agent executions, traces, and performance metrics are logged for reliability and continuous improvement.
• Scalable infrastructure: the platform runs on AWS using an Amazon EKS Kubernetes cluster, with horizontal scaling through KEDA to match demand and workload volume.

Technology stack: Python (FastAPI for API, LangGraph for agents), React front end, AWS RDS Postgres for application data, Snowflake for client data, frontier models for commentary generation, and Jenkins for CI/CD.

• Days to minutes: report generation dropped from days to minutes, accelerating insight delivery and decision-making.
• 80% productivity improvement: automation replaced manual report production for 80+ researchers, who shifted to reviewer and approver roles focused on higher-value research and strategy.
• Accuracy and consistency across 82+ markets: uniform formatting, branding, and numerical accuracy across 82+ global markets and multiple property types.
• Reliable and continuously improving: human-in-the-loop oversight and full revision history preserve institutional knowledge, improve data integrity, and reduce future errors.
• Strategic impact: expanded analyst capacity, faster insight delivery, and a stronger ability to respond quickly to market changes.