Credit-Report Intake & Parsing
Uploaded credit reports are converted into structured JSON and categorised by bureau (Experian, Equifax, TransUnion), account type and status — turning unstructured documents into clean, usable data.
An AI-Powered Credit Repair & Automation Platform for Harold
A full-scale AI-driven credit-repair and client-management system that automates the entire lifecycle — from credit-report intake and analysis to dispute handling, client communication and tracking — built with GoHighLevel, Relevance AI, Make.com and Airtable.
17
AI Analysis Agents
3
Credit Bureaus Automated
5
Integrated Platforms
FCRA
Compliance-Safe Logic
Project Summary
Harold at a glance
A full-scale, compliance-friendly credit-repair system that automates the entire lifecycle — from report intake and AI analysis to dispute letters, client comms and tracking — and scales with client volume.
- Client
- Harold
- Industry
- FinTech / Credit Repair
- Services
- AI + CRM Automation Platform
- Status
- Completed & Live
- Year
- 2026
01
The Challenge
Credit Repair Doesn’t Scale on Manual Work
Credit repair is drowning in manual work: parsing credit reports, analysing negative accounts, deciding dispute angles, writing letters, and keeping clients updated — all while staying FCRA-compliant.
Harold needed to eliminate that manual work, improve dispute accuracy, and scale operations using AI, CRM automation and structured data workflows — without sacrificing compliance.
-
01Unstructured Credit ReportsReports arrived as messy documents that had to be read and categorised by hand before any work could start.
-
02Complex, Risky Dispute LogicDispute decisions require nuanced, bureau-specific, FCRA-safe reasoning — hard to do consistently and at volume.
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03Fragmented OperationsClient data, disputes and tracking were spread across disconnected tools with no single source of truth.
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04Scale vs ComplianceAutomating aggressively risks non-compliant claims; staying manual caps growth. The system had to do both.
02
The Approach
How We Built It
A layered AI + automation architecture: structured intake, a multi-agent AI analysis system, controlled dispute generation with human checkpoints, and CRM automation tying it together.
17-Agent AI Analysis System
A Relevance AI multi-agent system analyses every account — classification, status normalisation, date accuracy, duplicate/re-aging detection, FCRA violation screening, dispute-eligibility scoring and more — improving accuracy while reducing false positives.
FCRA-Safe Dispute Generation
Dispute logic is generated dynamically from account type, bureau rules, detected inaccuracies and prior history — then composed into bureau-specific, legally-safe letters, with high-risk cases flagged for manual review.
GoHighLevel CRM Automation
Automated pipelines for new client, report received, dispute in progress, response received and next round — with automated email & SMS client updates and internal task creation for the team.
Make.com Orchestration & Monitoring
Make.com scenarios move data between Relevance AI, Airtable and GoHighLevel, handle status updates, error logging and milestone notifications — the automation engine behind the whole system.
Platform Walkthrough
A Look Inside What We Built
A look inside the platform — the GoHighLevel client lifecycle, the Make.com automation scenarios, and the Relevance AI agents that do the analysis.
03
The Results
What We Delivered
A major reduction in manual workload, faster dispute turnaround, more consistent and accurate disputes, and a scalable infrastructure built for long-term growth.
17
AI Analysis Agents
3
Bureaus Automated
5
Integrated Platforms
Report Intake
Manual reading
Parsed to structured JSON
Dispute Prep
Manual drafting
AI-generated, FCRA-safe
Operations
Scattered tools
Unified & automated
Tech Stack
Technologies
Technologies
& Tools
A layered AI + automation stack: GoHighLevel for CRM and client communication, Relevance AI for the multi-agent analysis and dispute reasoning, Make.com as the automation engine, and Airtable as the structured database — with Google Docs/PDF dispute letters and email/SMS updates.
GoHighLevel
Pipelines & Workflows
Relevance AI
Multi-Agent System
Prompt Engineering
Make.com
Airtable
Error Handling & Logging
Google Docs / PDF
Email & SMS
FAQ
Questions about this project
Straight answers to what buyers ask before a project like Harold.
Harold is an AI-driven credit-repair and client-management platform. Devvista built the whole system — structured credit-report intake, a 17-agent AI analysis layer (Relevance AI), FCRA-safe dispute generation, and full GoHighLevel CRM automation — orchestrated by Make.com with an Airtable database, to automate the entire credit-repair lifecycle.
Uploaded reports are converted to structured JSON, then a multi-agent system of 17 Relevance AI agents handles classification, status normalisation, date accuracy, duplicate and re-aging detection, FCRA violation screening, dispute-eligibility scoring and more. The layered approach improves accuracy and reduces false positives.
Yes — compliance is built in. Dispute logic avoids guaranteed outcomes or illegal claims, uses legal-safe language, flags high-risk or edge-case accounts for manual review, and keeps manual approval checkpoints. The system benefits from AI speed while staying aligned with FCRA-safe practices.
After AI analysis, each account is assigned a primary dispute reason, optional supporting arguments and bureau-specific phrasing. Data is pushed to Airtable, bureau-specific templates are populated, and individual letters are generated for Experian, Equifax and TransUnion — avoiding duplicate language that weakens disputes.
GoHighLevel runs the CRM side: automated pipelines for new client, report received, dispute in progress, response received and next round, plus automated email and SMS client updates and internal task creation for the team.
Make.com is the automation engine. Its scenarios move data between Relevance AI, Airtable and GoHighLevel, push status updates, handle error logging, and trigger team notifications on milestones or exceptions — keeping all the platforms in sync.
A major reduction in manual workload, faster dispute turnaround, more consistent and accurate disputes, and a scalable infrastructure designed for high client volume and long-term growth.
Yes. The pattern — structured intake, multi-agent AI analysis, compliant document generation, and CRM automation orchestrated by Make.com — applies to many operations-heavy, compliance-sensitive businesses, not just credit repair.
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