I redesigned Full Credit Sweep’s client portal and built Python services around GoHighLevel to connect call follow-up, CRM workflows, messaging, credit-report analysis, and document generation.

Full Credit Sweep helps people work through credit-related issues and track their progress in an online portal. Danny, the CEO, hired me directly to improve the client experience and automate the repetitive work surrounding each case. I still work with the company.

There are two different numbers here: 2,000+ active paying clients have portal access and active cases. The GoHighLevel CRM also holds 68,000+ lead records from campaigns and earlier conversations. Those leads are not 68,000 customers.

I redesigned the client-facing dashboard from the ground up. It gives clients one place to see their credit information, disputes, documents, and case progress without exposing the internal workflows behind their case.

The dashboard also delivers generated reports and documents. Because this work involves sensitive financial information, I’m only showing the public login page—not private dashboards, reports, calls, or client records.

GoHighLevel is the system of record, but much of the business logic runs in Python services I built around its APIs and webhooks.

A change in a CRM pipeline can trigger the next step: update a record, notify a team member, prepare a document, send a message, or route the case to a more specific workflow. That keeps the CRM, internal team, and client portal aligned without asking staff to repeat the same update in several places.

The connected communication workflows include SMS, email, inbound and outbound calls, AI web chat, and internal notifications.

Human agents conduct and qualify calls. The AI does not make that decision for them.

After a call, the automation summarizes what happened, categorizes the outcome, and updates the relevant CRM stage when the defined conditions are met. If a client reports an issue with a document or credit report, the system can route the case to the right pipeline and notify the person responsible.

The team gets a clear next step without having to replay every call or rely on someone’s handwritten notes.

I built workflows that analyze two credit reports and prepare material based on the results. The system can generate dispute letters, summaries, contracts, and other case documents.

A human agent checks generated documents before they are finalized and delivered through the client portal. That review matters when the underlying information is personal and financially sensitive.

Several connected systems have been live in production for approximately four to five months. They now support a client portal used by 2,000+ active paying clients and operational workflows around 68,000+ lead records.

My work brought the dashboard, CRM, calls, messages, report analysis, and documents into a connected process. Repetitive coordination is automated; client-facing decisions and document checks remain with the human team.

I’m not claiming an unmeasured conversion rate, accuracy score, or labor-saving percentage. The result I can stand behind is a live system supporting a substantial credit-resolution operation.

Danny hired me directly. I redesigned and developed the client dashboard, built Python automation services, connected them to GoHighLevel through APIs and webhooks, and continue to maintain and extend the workflows.

Client: Full Credit Sweep Role: AI Automation Developer and Client Portal Developer Website: https://www.fullcreditsweep.com/