Gusto: AI Agent Enhancement

Success Story

AI Agent Enhancement and Salesforce Platform Support

Zoolatech provided senior engineering expertise to support AI-powered sales applications, improve platform reliability, and enhance operational visibility across Salesforce-based workflows.

82K transcripts

recovered by restoring critical AI workflow data.

Platform reliability

improved through workflow stabilization and issue resolution.

Technologies

Ruby on Rails

Salesforce

Apex

Lightning Web Components

AWS

Terraform

Gumloop

MuleSoft

Python

OpenAI APIs

Anthropic APIs

Expertise

AI agents

Monitoring & observability

Salesforce B2B commerce cloud

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AI workflow complexity

Multiple AI agents, orchestration tools, and Salesforce integrations increased operational complexity and troubleshooting effort.

Limited observability

Engineering teams needed improved monitoring and dashboards to better understand platform health and communicate operational improvements.

Transcript processing failures

Missing meeting transcripts interrupted downstream AI workflows, reducing the information available for sales representatives.

Knowledge transfer

Ownership of critical AI agents shifted between engineers, requiring rapid onboarding while maintaining uninterrupted service.

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Modernize AI-enabled business platforms with experienced engineering support.

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Proven AI experience

Zoolatech engineers brought experience implementing AI agents, RAG systems, OpenAI integrations, Anthropic APIs, and enterprise LLM deployments.

Enterprise platform expertise

Experience with cloud infrastructure, integrations, observability, and Salesforce enabled rapid ownership of critical platform components.

Zoolatech is a senior-heavy engineering firm with Silicon Valley roots and a Miami HQ, specializing in legacy modernization, system re-architecture, and AI deployment to drive long-term, compounding value.

2017

Year Founded

600+

Employees

96%

Client Satisfaction

Phase 1

Discovery and knowledge transfer

Zoolatech engineers onboarded to the existing AI platform and assumed ownership of key AI agents and supporting codebases.

Phase 2

Investigation and analysis

Incoming feature requests and production issues were investigated with client stakeholders to identify root causes and define implementation priorities.

Phase 3

Solution implementation

Based on the findings from the investigation phase,Zoolatech engineers implemented enhancements across multiple repositories to improve platform reliability, support AI-driven workflows, and strengthen operational visibility. Key activities included:

AI agent enhancements
Integration bug fixes
Infrastructure improvements
Observability initiatives

Phase 4

Validation and deployment

Solutions were validated through the client's QA process before progressing through the established deployment environments.

Phase 5

Continuous optimization

Monitoring capabilities and operational dashboards were expanded to improve platform visibility and support ongoing reliability improvements.

Zoolatech strengthened the reliability and visibility of the client's AI platform by supporting critical workflows, improving observability, and resolving operational issues affecting production services.

AI agent ownership

Supported ongoing development and maintenance of AI agents that assist sales representatives during customer conversations.

Observability enhancements

Implemented dashboards, monitoring, and engineering metrics to improve visibility for technical and business stakeholders.

Infrastructure support

Contributed infrastructure improvements using Terraform and cloud services to strengthen operational reliability.

Workflow reliability improvements

Resolved issues affecting transcript processing and downstream AI orchestration workflows.

Option	Risk	Mitigation
Transcript processing failures	Missing meeting transcripts disrupted downstream AI workflows and reporting.	The engineer identified the root cause, extended the backfill window, and restored transcript processing.
Limited operational visibility	Limited monitoring delayed issue detection and reduced platform visibility.	Enhanced dashboards, monitoring, and engineering metrics improved operational visibility and faster issue detection.