Harshith, Head of Growth at JustPaid, reveals how 100+ AI agents are redefining fintech ops—from billing to compliance—on the path to a one-person unicorn.
Harshith, could you briefly share your background and professional journey leading up to your role at JustPaid?
My journey started in community management within the Web3 space, where I worked on projects like Love, Death & Robots for Netflix, leading NFT initiatives and building engaged communities. This experience taught me the power of combining technology with strategic growth.
I then transitioned into AI and fintech by founding ARTIFIN.ai, an AI-driven financial analysis platform that I successfully built and exited. This hands-on experience gave me deep insights into how AI can transform financial operations and the real pain points that finance teams face.
Before joining JustPaid, I expanded my expertise across the tech ecosystem – leading content and events at Metaschool, a Peak XV-funded Web3 platform where I managed international hackathons, and driving growth initiatives that consistently delivered 2x performance improvements.
At JustPaid, I’m now Head of Growth, where I’m excited to apply this unique combination of community building, AI product development, and growth expertise to help transform revenue operations. I’m leading our growth strategy through our podcast and YouTube channel, while internally I’m implementing AI agents to run autonomous newsletters and execute growth hacks – essentially practicing what we preach by using AI to automate our own marketing operations. It’s incredibly rewarding to contribute to our mission of eliminating the manual bottlenecks that have traditionally consumed weeks of finance teams’ time.
You’ve made headlines by “hiring” 100 AI engineers at JustPaid. How did you ensure trust, compliance, and accuracy in its financial systems while scaling an AI-driven engineering workforce?
The key was building trust through transparency and rigorous validation frameworks. Each AI agent operates within defined guardrails with comprehensive audit trails. We implemented multi-layer verification systems where critical financial operations require consensus from multiple AI agents before execution.
For compliance, we built compliance-first architecture. Every AI decision is logged with full context, creating an immutable audit trail that exceeds traditional human-generated documentation. Our AI agents are trained on regulatory requirements like SOC 2 and GAAP, ensuring they don’t just follow rules but understand the reasoning behind them.
Accuracy comes from continuous learning loops. Our AI agents learn from every transaction, building institutional knowledge that actually improves over time – something traditional systems can’t achieve.
Billing automation often struggles with edge cases and integrations. How does JustPaid’s AI Billing Agent handle complex enterprise billing environments, and what underlying tech stack powers this reliability?
Our AI Billing Agent uses a multi-modal approach that combines large language models with specialized financial reasoning engines. The
core architecture includes:
Adaptive Contract Intelligence: AI agents that can parse and understand complex contract language, identifying billing triggers, escalation clauses, and custom terms
Dynamic Integration Framework: Instead of pre-built connectors, our AI agents learn API patterns and can adapt to new systems in real-time
Edge Case Learning System: When the AI encounters unusual scenarios, it doesn’t break – it learns, documents the pattern, and handles similar cases automatically in the future
The tech stack centers on distributed AI orchestration with real-time data processing pipelines. We use vector databases for contextual memory and graph neural networks for understanding complex billing relationships.
You’ve moved from human-led coding to AI-led engineering in finance infrastructure. How do you manage security, error handling, and version control when code is generated by AI agents?
We’ve developed what I call ‘AI-Native DevOps’ – a comprehensive framework designed specifically for managing AI-generated code in production financial systems:
Security: Every AI-generated code change goes through multi-layered automated security scanning. We use Claude and other AI agents trained on security best practices to perform code reviews that often catch vulnerabilities traditional static analysis misses. Our AI agents generate secure-by-default code patterns and can identify potential attack vectors in real-time.
Error Handling: Our AI agents implement defensive programming by default. Using tools like Claude for code generation, we ensure comprehensive error handling, structured logging, and graceful degradation patterns are built into every function. When errors occur, our monitoring agents can analyze the issue and often generate patches autonomously – essentially self-healing code.
Version Control: We’ve revolutionized our git workflow with AI-powered semantic versioning. Our agents generate detailed commit messages that explain not just what changed, but the reasoning behind each decision. Claude helps maintain living documentation that evolves with the codebase, making code reviews more thorough and contextual than traditional human-generated code.
Agent Orchestration: We use a multi-agent system where specialized AI agents handle different aspects – one for code generation, another for testing, one for security review, and another for deployment. This creates a robust pipeline where each agent validates the work of others, similar to how human teams do code reviews, but at machine speed and scale.
The result is code that’s often more consistent, well-documented, and secure than what traditional development processes produce, while moving at the speed our fintech operations demand.
What’s the technical workflow behind JustPaid’s AI reducing billing cycles from weeks to a day, from contract parsing to collections?
The workflow operates as an intelligent pipeline:
- Contract Ingestion: AI agents parse contracts using multimodal understanding, extracting billing terms, payment schedules, and custom conditions
- Dynamic Billing Logic Generation: Instead of manual configuration, AI generates billing rules and validates them against contract terms
- Real-time Invoice Creation: AI agents create invoices with appropriate line items, tax calculations, and compliance requirements
- Intelligent Delivery: AI determines optimal delivery methods and timing based on customer preferences and payment history
- Automated Follow-up: AI agents handle collections with personalized communication strategies, escalating only when necessary
The entire process runs continuously with AI agents monitoring for changes, exceptions, and optimization opportunities.
Finance teams often rely on deterministic, auditable systems. How do you ensure traceability and compliance with standards like SOC 2, ISO 27001, and GAAP in its AI workflows?
We’ve built “Explainable Finance AI” – every AI decision includes detailed reasoning that can be audited:
Traceability: Each transaction includes a complete decision tree showing how the AI reached its conclusion, what data it considered, and which rules it applied.
Compliance Integration: Our AI agents are trained on compliance frameworks as core knowledge. They don’t just follow compliance rules – they understand the intent and can adapt to new regulations.
Audit-Ready Documentation: AI agents generate documentation that exceeds human standards. Every process change, exception handling, and decision point is automatically documented with timestamps and reasoning.
Continuous Compliance Monitoring: AI agents continuously monitor for compliance drift and can automatically implement corrective measures.
What key infrastructure choices enabled JustPaid to scale 100 AI agents across frontend and backend finance operations?
Scaling 100+ AI agents across our finance operations required building what we call our ‘AI Agent Orchestration Platform’ – a purpose-built infrastructure designed for autonomous financial systems. As a Y Combinator company, we leveraged $350k in Azure cloud credits, which enabled us to build this at enterprise scale from day one:
Microservices Architecture: Each AI agent runs as an independent containerized service using Azure Container Instances and Azure Kubernetes Service (AKS). This allows us to scale individual agents horizontally based on workload – for example, our invoice processing agents can spin up dozens of instances during month-end, while our compliance agents maintain steady capacity. We use Azure Service Fabric to manage inter-agent communication securely.
Event-Driven Communication: Our agents communicate through Azure Event Hubs and Service Bus, enabling real-time coordination without tight coupling. When an invoice is received, it triggers a cascade of events – OCR agents extract data using Azure Cognitive Services, validation agents check compliance, and approval agents route based on business rules. This creates a resilient system where agents can join or leave without disrupting the entire workflow.
Shared Knowledge Base: All agents access a unified knowledge graph built on Azure Cosmos DB and Azure Cognitive Search, containing business rules, customer data, and continuously learned patterns. This isn’t just static data storage – it’s a living system where agents contribute insights back, improving the collective intelligence. Our Claude-powered agents can reason over this knowledge base to make contextual decisions.
Resource Management: We implement dynamic resource allocation using Azure Auto Scaling and custom orchestration logic. AI agents request compute resources based on their current workload, and our system provisions Azure GPU instances for complex reasoning tasks while scaling down during idle periods. The $350k in credits allowed us to experiment with high-performance computing configurations without budget constraints.
Fault Tolerance & Recovery: Our system implements circuit breakers and health checks for every agent using Azure Application Insights. If an invoice-processing agent fails, our orchestrator automatically redistributes its workload to healthy instances while spinning up replacements. We maintain hot standby agents for critical functions and use Azure’s checkpoint recovery to resume work exactly where it left off.
Monitoring & Observability: We’ve built custom dashboards using Azure Monitor and Application Insights that track not just traditional metrics like CPU and memory, but AI-specific metrics like reasoning accuracy, decision confidence scores, and learning velocity. This gives us unprecedented visibility into how our AI workforce is performing.
The YC Azure credits were game-changing – they allowed us to build enterprise-grade infrastructure and experiment with cutting-edge AI services like Azure OpenAI Service without the typical startup constraints. The result is a system that processes thousands of financial transactions daily with sub-second response times, while maintaining the accuracy and compliance standards required for enterprise finance operations.
Most fintech startups focus on speed; you’ve focused on autonomy. How do you ensure AI-generated features or billing updates don’t disrupt revenue flows or ERP/CRM integrations?
Autonomy requires sophisticated safeguards:
Gradual Rollout Systems: New AI-generated features are deployed with progressive exposure – starting with test transactions before handling production workloads.
Integration Health Monitoring: AI agents continuously monitor integration points, detecting anomalies before they impact revenue flows.
Rollback Capabilities: Every AI-generated change includes automatic rollback triggers if key metrics deviate from expected ranges.
Revenue Protection: AI agents prioritize revenue continuity above all else. They’re designed to maintain cash flow even when adapting to new requirements.
Looking forward to your “One-Person Unicorn” vision, what core AI tools and financial modules are essential for a founder to run a fully autonomous billing and revenue operation?
My ‘One-Person Unicorn’ vision is my personal moonshot – imagine one founder orchestrating thousands of AI agents to operate at unicorn scale. For fully autonomous billing and revenue operations, the essential stack would include:
AI Contract Manager: Automatically handles contract negotiations, amendments, and renewals. These agents understand business context and
can negotiate terms while maintaining compliance.
Autonomous Billing Engine: Generates invoices, handles collections, and manages payment processing without human intervention. It adapts to customer preferences and optimizes payment timing.
Intelligent Revenue Recognition: Ensures proper accounting compliance while optimizing cash flow. The AI structures deals to maximize financial efficiency automatically.
Predictive Cash Flow Management: AI-powered forecasting that takes proactive actions like adjusting payment terms or accelerating collections to optimize cash position in real-time.
Customer Success AI: Proactively manages relationships to reduce churn and increase expansion revenue. Identifies at-risk customers and implements retention strategies before they even think about leaving.
Growth Operations AI: Handles lead generation, nurturing, and conversion optimization. Can launch campaigns, optimize pricing, and negotiate enterprise deals autonomously.
The breakthrough is these aren’t separate tools – they’re interconnected AI agents sharing context and coordinating decisions. When the contract AI negotiates better terms, the billing AI adjusts invoicing, the cash flow AI updates forecasts, and the customer success AI personalized onboarding.
The technical foundation for this autonomous revenue operation runs on GitHub Actions orchestrating Claude agents in continuous deployment cycles. Each financial workflow triggers automated GitHub workflows that spawn specialized Claude agents – when a new contract is signed, GitHub Actions automatically provisions Claude agents to extract terms, update billing schedules, and modify customer databases. These agents commit their changes back to the repository, triggering additional workflows that deploy updates across the entire revenue stack.
The beauty is GitHub’s event-driven architecture becomes the nervous system connecting hundreds of Claude agents – contract changes trigger billing updates, billing updates trigger cash flow recalculations, and customer behavior changes trigger retention workflows. All running 24/7 in the cloud, with full version control and rollback capabilities. It’s like having an entire finance team that operates through code commits and never makes manual errors.
What advice would you give to fintech leaders and developers looking to embrace AI-driven automation while navigating the challenges of this rapid transformation?
My advice is counterintuitive: Stop focusing on efficiency and start building distribution through AI systems, because the game is fundamentally changing.
Start with trust-building, not efficiency-seeking. The biggest mistake is trying to automate everything at once. Begin with low-risk, high-visibility processes where AI can prove its reliability. But here’s the key – use these early wins to build your distribution engine.
Build distribution using AI agents and AI systems because it’s becoming so easy that anybody can build software. Eventually, people will build their own software instead of subscribing to SaaS products. If you want to sustain long-term in SaaS, distribution is everything – personal brand, content creation, community building. All of this can and should be automated with AI.
Focus on AI-human partnerships initially, but understand this is temporary. Create systems where AI handles what it does best while humans focus on strategic decision-making and relationship building. But invest heavily in observability and explainability – you need to understand not just what your AI is doing, but why it’s making specific decisions.
Most importantly, you need a very strong moat – without a moat, it’s very difficult to survive. Stay at the forefront of AI development, but more critically, build defensible distribution channels and brand equity. The transformation isn’t just happening – it’s accelerating exponentially. Companies that embrace AI thoughtfully while building unbreachable moats through distribution and brand strength will dominate. Those without strong moats will become irrelevant as customers start building their own solutions.
The real question isn’t whether to adopt AI – it’s whether you’re building the distribution moat and brand strength to survive when AI makes software development trivial for everyone. That’s the only strategic moat that matters now – everything else becomes commoditized.
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