Arkeo AI

Transforming Sales with
AI-Driven Innovation

Arkeo AI is a technology platform using advanced AI and data analytics to streamline operations, enhance decision-making, and drive innovation. With agentic architecture, it transforms SaaS sales into dynamic, proactive workflows.

Rebuilding and Scaling Arkeo AI's Platform for Success

The Challenge

Restoring Core Functionality

Agent Malfunction
Three essential AI agents, the Ideal Customer Profile (ICP), Deal Notes Agent, and Pipeline Analysis Agent, were inoperative due to the sudden departure of the CTO and backend engineer.

Platform Instability
Critical frontend bugs and backend issues were impairing the user experience and preventing successful onboarding.

Documentation Gap
Poor documentation complicated onboarding and limited engineers’ ability to maintain and grow the platform effectively.

Infrastructure and Growth Challenges

Scalability Issues
The system lacked robust error-handling and scheduling mechanisms, leading to delays and instability.

Unnecessary Complexity
Overlapping or redundant code hindered system optimization and team efficiency.

The Solution

Stabilizing Core Functionality
We repaired three AI agents—ICP, Deal Notes, and Pipeline Analysis—and resolved backend idempotency issues with reliable key-handling. We also fixed critical frontend bugs like display errors and page reload issues to enhance the user experience.

Documentation and Knowledge Transfer
We developed comprehensive documentation, including setup guides, workflow explanations, and environment variables, along with actionable references to streamline onboarding and ensure future continuity.

Infrastructure Optimization
We resolved scheduler issues, implemented throttling for better error recovery, and refactored the codebase to remove unused variables and enhance robustness.

Enhanced System Reliability
We improved logging capabilities for better debugging and system monitoring while strengthening file handling and deployment processes to prevent downtime during updates.

Key Features Delivered

Restored AI Agents
Reactivated three core AI agents to optimize sales efficiency and streamline processes.

Improved User Experience
Addressed critical frontend bugs and enhanced platform usability for smoother onboarding and day-to-day operations.

Comprehensive Documentation
Built a robust knowledge base to support new engineers and ensure seamless maintenance.
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System Stability Enhancements
Introduced resilient error-handling mechanisms and optimized backend processes for reliability.
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Technical Challenges

1. Restoring AI Agent Functionality

Challenge: The platform relied heavily on three broken AI agents, with no prior documentation for fixes.

Solution: Implemented in-depth debugging and diagnostics to restore agent functionality, ensuring alignment with the platform’s goals.

2. Platform Instability

Challenge: Frequent crashes and unresolved bugs were affecting user experience and customer trust.

Solution: Fixed critical errors, optimized code for performance, and implemented robust error-recovery mechanisms.

3. Documentation Deficiency

Challenge: Lack of documentation hindered onboarding and maintenance.

Solution: Created comprehensive guides covering system setup, workflow explanations, and environment variables.

Results

Stabilized Platform
Restored critical AI agents and fixed platform bugs, enabling successful onboarding.

Improved Reliability
Enhanced system stability with robust error-handling and documentation.

Foundation for Growth
Simplified infrastructure for easier scaling and development of future features.

Positive Client Feedback
Acknowledged for rapid stabilization of the platform and laying the groundwork for innovation.

Performance Metrics

Development Timeline
Delivered critical fixes and enhancements within 3 months.

User Engagement
Improved onboarding experience led to increased user satisfaction and retention.

System Reliability
Enhanced stability ensured uninterrupted service and smoother operations.

Upcoming Opportunities

Agent Expansion
Plan to develop up to 20 new agents within the year to address emerging needs.

Simplified Infrastructure
‍Transitioning to a modern tech stack (NextJS, Vercel, Trigger.dev) for better scalability and engineer accessibility.

Enhanced Testing and Monitoring
Introducing broader test coverage and real-time monitoring to maintain platform reliability.

Tech Stack

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1. Restoring AI Agent Functionality

2. Platform Instability

3. Documentation Deficiency

Performance Metrics

Tech Stack

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Blockchain-Integrated Portals

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