We help investment firms turn AI into customized workflows that fit their data, process, and operating model.
See our approachInvestment firms want AI, but off-the-shelf solutions are too generic for their proprietary data, specialized research processes, compliance requirements, and investment decision loops. Useful AI needs to be customized around the firm's actual workflow, with visibility and control built in from day one.
Financial workflows are too specific for a simple horizontal SaaS product. REC Lab follows a Palantir-style motion: understand the firm's nuance, customize the agentic system around its real workflow, then productize the patterns that repeat.
Learn the firm's investment process, data environment, operating rhythm, and constraints before building around it.
Design firm-specific agentic workflows that fit how the team already works across research, operations, reporting, and investor-facing work.
Turn repeated workflows into reusable modules over time, so the product becomes scalable without losing customization.
Observability and governance are not separate products. They are embedded across every AI workflow, giving leadership visibility into usage, cost, quality, policy, and risk as adoption scales.
Agentic workflows for research synthesis, quantitative research, document review, and strategy development.
Customized workflows for internal reporting, investor-facing updates, and repetitive financial operations.
Built-in visibility across every AI workflow: usage analytics, audit trails, policy controls, cost visibility, and anomaly monitoring.
Three founders across quant trading, AI product, financial data infrastructure, and investment workflows.
Quant trading background; AI product at OKX across trading and agentic AI.
Built financial data and workflow systems at Citadel and Addepar.
Quant and investment background at KKR.