Workflow for the Acquisitions Analyst's Deal Sourcing and Screening

Analysts spend 60-70% of their time on repetitive data gathering: checking broker emails, scanning listing sites, and manually entering leads into a CRM. A custom agentic workflow automates this ingestion from fragmented sources (e.g., CoStar, proprietary lists, broker blasts), parsing unstructured data and creating structured lead records. This eliminates the manual search and data entry bottleneck, freeing analysts to focus on high-value negotiation and underwriting.

Manual screening is slow and inconsistent, causing high-potential off-market deals to slip through. An automated workflow applies your firm's investment criteria (e.g., minimum cap rate, asset class, market) as a first-pass filter, scoring and ranking thousands of opportunities. This systematic pre-qualification ensures analysts only review deals that fit the strategy, increasing the volume of actionable leads each analyst can handle per quarter.

The traditional sourcing-to-review cycle creates a 2-3 week lag, allowing competitors to move first. An automated pipeline triggers immediate enrichment (appending property details, ownership history, zoning) and pre-screening the moment a lead is ingested. This compresses the initial qualification phase from weeks to hours, moving viable deals into active diligence faster and improving win rates in competitive bid situations.

Manual processes lead to data entry errors, lost emails, and inconsistent record-keeping in the CRM. A custom workflow enforces data normalization, deduplicates leads across sources, and automatically logs all interactions and source documents. This creates a single, auditable source of truth, ensuring no lead is lost and improving the accuracy of pipeline reporting and forecasting for the acquisitions team.

Growing an acquisitions team linearly with deal flow is costly and operationally complex. This workflow acts as a force multiplier. The orchestration layer (using frameworks like LangGraph) coordinates scraping agents, enrichment APIs, and scoring models, pushing qualified leads directly into Salesforce or DealCloud. This allows a team to 10x its sourcing coverage without proportionally increasing analyst headcount, fundamentally improving operating leverage.

Implementation focuses on high-impact, low-complexity integration points first: connecting to primary email inboxes and listing APIs, deploying initial scoring logic, and building a simple CRM sync. A pilot can be live in 3-4 weeks, demonstrating immediate time savings and pipeline growth. The full ROI—calculated from increased deal velocity and reduced cost per acquired asset—becomes measurable within a single quarter, justifying further workflow expansion.

This agent orchestrates the continuous pull of raw deal signals from fragmented sources: scraping off-market listing sites, parsing broker email attachments, ingesting RSS feeds from commercial data vendors (CoStar, RCA), and monitoring social or news sentiment. It normalizes this data into a unified schema, handling API rate limits, authentication, and schema drift automatically. Without this layer, analysts waste hours daily on manual data collection across disparate inboxes and websites.

Takes raw lead data (often an address or partial company name) and executes a parallel enrichment pipeline. It calls geocoding APIs, pulls parcel data from county assessors, retrieves zoning codes, appends owner/LLC details from public records, and fetches recent sales history. A critical sub-agent performs entity resolution, deduplicating the same property appearing across multiple sources (e.g., a broker email and an off-market platform) to create a single golden record for screening.

Applies the firm's investment criteria as executable logic. This agent calculates preliminary metrics (e.g., price per unit, cap rate estimate) from enriched data, scores the deal against thresholds for market, asset class, and size, and flags potential show-stoppers (e.g., environmental liens, unfavorable zoning). Deals that pass a confidence threshold are pushed to the CRM (e.g., Salesforce) as a qualified lead with a structured data package; others are archived with a reason code. This is where analyst screening time is cut from minutes per lead to seconds.

The central nervous system built on a framework like LangGraph or Temporal. It manages the state of each lead, routes exceptions for human review, triggers follow-up tasks (e.g., 'request floorplans'), and ensures bi-directional sync with the CRM. When a lead is qualified, it automatically creates a contact record, attaches the enriched data packet, and assigns it to the correct acquisitions analyst based on territory or asset type rules, eliminating manual data entry and assignment lag.

A critical control plane for exceptions and high-value/high-ambiguity leads. The orchestrator routes deals that fail automatic enrichment (e.g., unclear address) or sit near scoring thresholds to a secure web dashboard. Analysts can review the agent's work, provide corrective feedback (which retrains the system), and make a final qualification call. This gate ensures model errors don't lose good deals and provides the labeled data needed for continuous improvement.

Not an afterthought, but a core component for production operations. Tracks key pipeline metrics: leads ingested, enrichment success rate, automatic qualification rate, and time-to-first-contact. Monitors agent health, API costs, and data freshness. Provides audit trails for compliance and enables the acquisitions lead to tune scoring thresholds based on conversion rates, directly linking workflow performance to sourcing ROI.