AI Multilingual Chatbot for Financial Assistance
24/7 multilingual
support enabled through an AI chatbot.
Lower operational costs
achieved by automating FAQs and loan pre-qualification assistance.
Industries
Industries We Serve
Cross-industry engineering experience across regulated and high-growth sectors.
All IndustriesSuccess Story
Job Recommendation Platform Optimization
Zoolatech enhanced a large-scale engagement platform by improving experimentation, AI-powered personalization, campaign delivery, and platform maintainability.
Experimentation-driven optimization
enabled faster product validation through controlled user experiments.
Scalable engagement delivery
supported personalized recommendations across email and mobile at scale.
Technologies
Amplitude
Redis
JavaScript
TypeScript
React
Node.js
Event-Driven Architecture
Email Delivery Systems
Push Notification Services
Expertise
Product experimentation
Platform modernization
Engagement platforms
AI content enrichment
Client Overview
Glassdoor
Glassdoor is a career and workplace platform that helps job seekers research companies, compare salaries, read employee reviews, discover jobs, and evaluate workplace fit. The platform also supports employers with tools to manage their employer brand, respond to reviews, and reach talent.
Industries
HR technology, recruiting, career services
Headquarters
San Francisco, CA, USA
Company size
500+ employees
The Challenge
Scaling Personalized Engagement Increased Operational Complexity
As personalization and experimentation expanded, the platform needed to maintain reliable campaign delivery, performance, and long-term maintainability across multiple engagement channels.
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Build a scalable foundation for personalized engagement and continuous product innovation.
Legacy architecture slowed platform evolution
Years of incremental development resulted in inconsistent implementation patterns and growing technical debt that complicated feature delivery and maintenance.
Experimentation required operational flexibility
Product teams regularly launched engagement experiments, requiring a reliable framework for audience segmentation, feature rollouts, and behavioral analysis.
AI-powered content increased processing complexity
Generating AI summaries introduced additional latency and resource consumption that could impact campaign execution.
Email delivery faced technical constraints
Large recommendation emails risked exceeding Gmail size thresholds, potentially hiding sponsored content and reducing campaign effectiveness.
External service dependencies created operational risk
Critical workflows relied on third-party and internal services that occasionally changed or became deprecated, requiring rapid investigation and remediation.
Have a Similar Challenge?
Build a scalable foundation for personalized engagement and continuous product innovation.
Why They Chose Us
Expertise in Building Scalable Engagement Platforms
Glassdoor needed an engineering partner with the expertise to support high-volume engagement platforms while enabling continuous product innovation.
Scalable platform engineering
Zoolatech brings experience optimizing large-scale platforms that support personalization, experimentation, and reliable user engagement.
Collaborative product delivery
Our engineers integrate seamlessly with client teams, providing the technical expertise and flexibility needed to evolve complex products without disrupting ongoing operations.
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
Workflow
Enhancing a Large-Scale Engagement Platform
The team contributed to ongoing platform modernization initiatives while supporting experimentation, campaign delivery, personalization, and operational stability.
Phase 1
Assessing platform architecture and delivery workflows
The team analyzed campaign generation systems, engagement workflows, experimentation mechanisms, and service dependencies to identify technical debt and operational bottlenecks.
Phase 2
Modernizing experimentation capabilities
An internal experimentation framework was progressively migrated toward Amplitude, enabling improved audience segmentation, experiment management, behavioral analysis, and product decision-making.
Phase 3
Enhancing personalization workflows
The team implemented AI-powered content enrichment capabilities and integrated personalized job recommendation data into engagement campaigns. Special attention was given to processing efficiency, scalability, and delivery performance.
Phase 4
Optimizing campaign execution
Monitoring, reporting, and performance improvements were introduced to support reliable email generation, reduce delivery issues, and improve communication effectiveness across engagement channels.
Phase 5
Improving maintainability and platform health
The team continuously reduced technical debt, aligned implementation patterns, improved testing practices, and modernized critical platform components to support long-term evolution.
The initiative evolved from platform support activities into a broader modernization effort focused on experimentation, personalization, delivery optimization, and long-term maintainability of a critical user engagement platform.
Solution
Optimizing Personalized Engagement Across Multiple Channels
The team enhanced the platform through a combination of experimentation improvements, performance optimizations, personalization capabilities, and engineering modernization initiatives.
Experimentation-driven product optimization
The platform leverages Amplitude to support audience segmentation, hypothesis testing, feature rollouts, and engagement analysis. Product teams can evaluate user behavior, validate ideas, and make rollout decisions using real-world performance data.
Scalable AI content generation
To support AI-generated job summaries, the team implemented a preprocessing workflow that generates content before campaign execution. Summaries are processed asynchronously, stored in cache, and retrieved during delivery, reducing latency and preventing AI service bottlenecks during large-scale campaigns.
Campaign delivery optimization
The team developed monitoring and reporting capabilities to identify oversized emails and analyze delivery constraints. These insights support ongoing optimization efforts aimed at preventing Gmail truncation while preserving advertiser visibility and critical recommendation content.
Multi-channel engagement delivery
The platform coordinates personalized communication through email and push notifications, enabling consistent user engagement across multiple channels and devices.
Engineering modernization
The team continuously reduced technical debt, improved architectural consistency, standardized testing approaches, and introduced maintainability improvements across critical platform services to support long-term evolution.
Risks and Mitigations
Managing Personalization, Scalability, and Delivery Constraints
The team addressed several challenges related to AI processing, campaign delivery, platform evolution, and external service dependencies.
Option |
Risk |
Mitigation |
|---|---|---|
| AI content generation | AI-generated summaries introduced latency and processing overhead during campaign execution. | Implemented asynchronous preprocessing, audience segmentation, and caching to reduce runtime dependencies on AI services. |
| Email delivery constraints | Large recommendation emails risked Gmail truncation and reduced visibility of sponsored content. | Introduced monitoring, reporting, and optimization initiatives to identify and reduce oversized emails. |
| Platform maintainability | Legacy implementation patterns increased technical debt and slowed development. | Applied incremental refactoring, architectural improvements, and testing standardization. |
| Experiment complexity | Frequent product experiments required reliable rollout and measurement mechanisms. | Leveraged Amplitude to manage experimentation, audience segmentation, and behavioral analysis. |
| Service dependencies | External and internal service changes could disrupt critical workflows. | Implemented monitoring, investigation processes, and migration strategies to reduce dependency-related risks. |
Results
A More Flexible and Maintainable Engagement Platform
The initiative strengthened the platform's ability to support personalization, experimentation, and future product evolution.
Expanded experimentation capabilities
Product teams gained greater flexibility to validate engagement hypotheses and evaluate user behavior through controlled experiments.
Improved platform scalability
Queue-based processing, caching strategies, and workflow optimizations reduced operational bottlenecks associated with AI-powered content generation.
Enhanced platform maintainability
Ongoing modernization efforts improved architectural consistency and reduced technical debt across critical platform components.
Better operational visibility
Monitoring and reporting improvements provided deeper insight into campaign behavior, delivery constraints, and system performance.
Empowerment & Value
A Foundation for Continuous Engagement Optimization
The platform provides a scalable foundation for future personalization, experimentation, and communication initiatives.
Faster product experimentation
New engagement concepts can be evaluated through controlled testing before broader rollout.
Sustainable platform evolution
Modernized architecture and improved engineering practices support long-term feature development and operational stability.
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