A Schedule of Values (SOV) lists project costs by line item, phase, or component, and it anchors how funds are allocated and tracked. In lending and insurance workflows, SOVs help brokers and funders verify scope, budget integrity, and progress claims before moving a deal forward.
Manual review of SOVs is slow and inconsistent because formats vary across contractors and systems. Key figures hide in different tabs, totals do not always sum correctly, and version labels are easy to miss in email attachments.
Heron automates parsing for SOVs by detecting the document, reading line items and headers, and converting everything into clean, structured fields. Parsed totals, subtotals, and metadata flow into the CRM so underwriting, compliance, and operations can work from the same source of truth.
Teams make better decisions when SOVs become data, not files. With Heron, the record updates in minutes, exceptions surface early, and queues keep moving without manual rekeying.
Use Cases
- Normalize multi-format SOVs into one schema: Heron reads PDFs, spreadsheets, and scans, then maps headings and line items into a consistent structure. This gives reviewers a uniform view, no matter how the contractor formatted the sheet.
- Capture contract value and change orders: The parser pulls original contract value, approved change orders, and revised totals. Underwriters see budget movement without opening every tab or attachment.
- Read progress and retainage fields: Heron parses percent complete, work in place, stored materials, and retainage by line. Reviewers can validate pay requests against the latest SOV quickly.
- Extract dates and identifiers: Project name, project number, period ending date, and version tags become fields. Matching to the correct deal record is faster and more reliable.
- Parse cost codes and categories: Labor, materials, equipment, and subcontractor buckets are captured consistently. This supports quick comparisons across submissions and time periods.
- Handle revised submissions cleanly: When a new SOV arrives, Heron links it to the prior version and highlights what changed. Teams focus on differences instead of re-reading the entire file.
Operational Impact
Automated parsing turns SOVs into immediate, decision-ready data. Queues shrink because reviewers do not hunt through file systems or rekey line items into spreadsheets.
Accuracy improves once totals, dates, and version tags become structured fields with confidence scores. Programs get clearer reporting, fewer back-and-forth emails, and faster approvals.
Leaders gain visibility into volume, exception reasons, and cycle time by contractor and broker. The result is higher throughput with the same headcount and a smoother path from submission to funding.
Data Model and Field Mapping
A stable schema is the foundation for trustworthy SOV data. Heron maps common SOV elements to well-labeled fields that your CRM can use immediately.
- Header and identity: Project name, project number, contractor, owner, period ending date, submission date, and version tag map to text and date fields. These anchors make sure record matching stays accurate.
- Totals and changes: Original contract value, change order total, revised contract value, and current application amount map to numeric fields. Reviewers see a single source of truth for budget movement.
- Progress metrics: Percent complete, work in place, stored materials, and retainage map by line and as rollups. Underwriters can validate progress claims at a glance.
- Categorization: Cost code, cost category, and description map to controlled picklists where possible. Consistent categories make reporting and cross-deal comparisons easier.
- Quality flags: Missing columns, unreadable cells, or broken totals map to exception flags. Items with issues route to quick review without stopping clean data from writing back.
Completeness and Quality Controls
Parsing works hand in hand with checks that protect data quality and prevent rework. Heron applies lightweight controls that are strict where it matters and flexible where layouts vary.
- Column presence: Heron verifies that the required columns exist, such as description, scheduled value, and total complete. Missing columns create a clear reason code.
- Summation checks: Line item totals are compared to SOV rollups. Mismatches generate a flag that links directly to the affected cells.
- Date logic: Period ending dates are validated against submission dates and deal stage. Stale periods trigger a freshness alert and a suggested follow-up.
- Version awareness: If a file claims to be a revision, Heron checks whether totals, quantities, or dates changed. Differences appear in the record so reviewers see what is new.
- Readability scoring: Low confidence cells route to a light review, while high confidence fields write through. Teams make sure quality stays high without blocking flow.
Integration in the Heron Workflow
Parsing is one step in an end-to-end loop that keeps SOVs moving. The goal is simple: turn files into fields, then into action.
- Intake: SOVs arrive by email, portal, or API and land in Heron automatically. The system links each file to the right merchant or project record.
- Classification: The document is labeled as an SOV, and multi-file submissions are bundled logically. Reviewers never lose track of attachments.
- Parsing: Headers, line items, and totals become structured fields with confidence scores. This is where SOVs stop being static files.
- Scrubbing: Completeness, totals, and dates are checked. Issues create exception flags and a ready-made list for follow-up.
- Write back: Clean fields populate the CRM with timestamps and version history. Underwriters work from a single, reliable view.
- Routing: Records move to underwriting, compliance, or funding queues based on readiness and exceptions. Owners see exactly what to do next.
Collaboration and Broker Experience
Structured SOV data improves every handoff. Underwriters see the important numbers in the CRM without opening spreadsheets, which cuts review time significantly.
Brokers receive targeted requests when something is missing, such as a period ending date or a mismatched total. The message is clear, and the next submission is cleaner.
Operations teams stop renaming and sorting attachments because the record carries the latest version and a link to the source file. Everyone works from the same truth, which reduces confusion and speeds funding.
Performance Metrics and Analytics
Once SOVs are parsed, measurement becomes simple and repeatable. Teams use a few stable KPIs to drive improvement.
- Intake to ready time: Minutes from arrival to a record marked ready for review fall quickly. Leaders track this by contractor and broker.
- Exception rate by reason: Missing columns, broken totals, and stale periods show up in a simple chart. Training and broker guidance target the highest value fixes.
- Review touch time: Average minutes spent per SOV drops as more fields write back cleanly. Teams see gains without new headcount.
- Version churn: How often revised SOVs replace prior versions becomes visible. Managers plan workflow capacity around real patterns, not guesswork.
- Data accuracy trend: Confidence and correction rates trend downward over time. The system learns while controls stay in place.
Implementation Best Practices
A focused rollout with real files builds trust quickly. Start with the SOV formats you see most, then expand.
- Pick a minimum viable field set: Contract value, change orders, percent complete, retainage, and period ending date give maximum value fast. Add more after the first week.
- Align mapping with the CRM: Map fields to existing objects and picklists. Create new fields only when they support underwriting or reporting.
- Spot check weekly: Review a small sample of low confidence fields and exceptions. Feed corrections back so accuracy keeps improving.
- Publish a short checklist: Give brokers a one-page guide that names required columns and a preferred export format. Better inputs make the whole loop smoother.
- Role clarity: Decide who owns exception resolution and who approves the write-back. Clear roles keep work moving without side conversations.
Operational Scenarios
These real-world scenarios show how parsing changes daily execution. Each scenario turns a manual task into a quick, visible step that moves the deal forward.
- Revised SOV with new change orders: Heron links the file to the prior version, highlights the new change order total, and updates the record. Underwriters see the delta in seconds.
- Spreadsheet with hidden columns: The parser detects hidden columns that affect totals and flags the mismatch. A targeted request asks for a clean export without hidden fields.
- Image-based scan with low legibility: High confidence cells write through, while low confidence lines route to a short review. The queue does not stall while details are confirmed.
- Multi-project bundle: Heron splits a single workbook into separate SOVs by tab and links each one to the correct project. Records remain clean and searchable.
Business Outcomes
Parsing SOVs produces operational gains that compound over time. Intake to decision time drops, manual data entry disappears, and rework declines as totals and dates are validated up front.
Brokers experience faster feedback and fewer generic requests, which builds stronger relationships. Managers gain a reliable view of throughput and quality, which supports credible SLAs and realistic staffing plans.
The end result is simple, more deals move with the same team, and the data everyone uses is accurate, consistent, and audit-ready.
Benefits of Using Heron for Parsing the Schedule of Values
- Speed: Fields appear in the record minutes after intake, which shortens review time across the board.
- Accuracy: Totals, dates, and line items become trusted data points, which reduces rework.
- Consistency: Every SOV maps to one schema, which improves analysis and reporting.
- Scale: Parsing handles volume spikes without slowing queues, which protects SLAs.
- Clarity: Underwriters and ops see what matters in the CRM, which limits context switching.
FAQs About Parse for Schedule of Values
How does Heron handle different SOV layouts and formats?
Heron combines layout detection, table recognition, and keyword cues to read common SOV patterns across PDFs, spreadsheets, and scans. When a contractor uses a novel layout, confidence scoring, and a light review keep quality high while the model learns from the new example.
Can the parser read line item tables with merged cells or hidden columns?
Yes. The system detects merged and hidden cells and evaluates how they affect totals. If they break calculations or hide required values, Heron flags the issue and prompts the sender to provide a clean export so reviewers do not waste time.
What happens when totals do not match rollups?
Heron compares line item sums to top level totals and creates a clear exception with the variance amount. The record stays visible, and a targeted request lists the exact rows or sections that need correction or clarification.
Can we customize which SOV fields are written back to the CRM?
Yes. Teams pick the minimal field set needed for screening and progress checks, then add more as accuracy stabilizes. Heron maps those fields to your schema and tags each with a timestamp and confidence score.
How does parsing improve downstream underwriting and funding?
Parsed fields unlock scrubbing, summary notes, routing, and status updates without extra effort. Underwriters see the budget and progress metrics in the record, which cuts prep time and reduces the number of clarifying emails sent to brokers.
