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AI Agents for Capital Markets Workflows
United States
Project Overview
Strategy & Advisory
AI Products & Platforms
Commercialization & Growth
Agents
Automation & Integration
A real estate capital markets team set out to build a vertically integrated investment platform spanning deal sourcing, underwriting, capital placement, and asset management. Their workflow was distributed across Gmail, Google Calendar, Salesforce, Slack, Egnyte, and multiple outreach tools—creating fragmented data, manual handoffs, and inconsistent execution across teams.
SFAI Labs partnered with leadership to design a value-driven AI strategy that centralized the operating model, standardized core data objects, and created a scalable foundation for internal agents across the full deal lifecycle. Using our lab acceleration model, we aligned stakeholders, defined the minimum viable data infrastructure, and mapped high-leverage automation pathways tied to measurable business outcomes.
Within twelve weeks, SFAI delivered a system-level AI blueprint covering standardized data infrastructure, a unified taxonomy across contacts/companies/properties, and a roadmap of operational agents spanning sourcing, scoring, communications I/O, and deal Q&A. The architecture was designed to integrate directly with existing systems (Salesforce, Gmail, Calendar, Slack, Egnyte) while remaining auditable and extensible.
The result was a clear path to higher deal velocity and conversion through AI-enabled targeting, workflow orchestration, and scoring—positioning the team to scale deal flow without linear headcount growth.
Key Takeaways
Centralized data unlocks automation
Scoring focuses team attention
Integration drives adoption
Auditability enables enterprise scale
Agent workflows reduce key-person risk
Challenge
The organization relied on a patchwork of tools for sourcing, outreach, and deal execution, with critical context split across emails, calendars, CRM records, transcripts, and files. Deal cycles ran 4–6 months, with high stakes and high effort per transaction. The team needed a centralized operating system that could classify, segment, and enrich deal flow—while supporting repeatable execution across sponsors, capital sources, tenants, and internal stakeholders.
Strategy
SFAI Labs defined an AI operating model anchored in standardized objects (Contact, Company, Property) and a clear source-to-core data pathway. We prioritized a minimum viable subset aligned to Salesforce as the reference schema, then designed a scalable enrichment pipeline across data vendors and internal signals. In parallel, we defined agent roles for scoring, sourcing, communications capture, assessment, and document generation—sequenced across a phased platform roadmap.
Solution
SFAI designed an AI-driven operations workflow with:
Standardized data infrastructure mapped 1:1 to Salesforce objects
A unified data taxonomy spanning contacts, companies, and properties
Agent systems for sourcing, scoring, qualification, and workflow targeting
Communication intelligence to capture relationship history and context from Gmail and calls
Workflow orchestration to push enriched outputs back into Salesforce and campaign systems
An extensible architecture supporting future client platform and digital deal rooms
Execution
Week 1–2: System discovery, toolchain mapping, stakeholder alignment
Week 3–5: Data infrastructure design, schema mapping, source segmentation
Week 6–8: Taxonomy development, enrichment workflow design, QA requirements
Week 9–10: Agent definitions (scoring, sourcing, I/O, assessment), workflow architecture
Week 11–12: Roadmap sequencing, deployment requirements, operational handoff
Results
Standardized data infrastructure across core objects
Defined scoring and targeting agent blueprint
Established scalable operating system roadmap
Business Value
The engagement created a foundation for scaling deal flow through repeatable processes, higher-quality targeting, and reduced operational friction. By centralizing data and defining auditable AI workflows, the team reduced key-person risk and unlocked a path to increased conversion and deal velocity without proportional headcount growth.
Why SFAI Labs
SFAI Labs delivered impact by combining AI strategy, systems architecture, and commercialization thinking into an execution-ready operating model. Our lab approach aligned stakeholders quickly, prioritized the minimum viable data foundation, and designed agent workflows that integrate into how the team already works—driving adoption and measurable outcomes.
