Every founder dreams of their product changing an industry. But few understand the invisible scaffolding that supports those dreams—the workflows, the sprints, the tough architectural calls made when traction hits and things break.
At thAIng.ai, CEO Kearvyn Arne didn’t just set out to build another AI platform. He wanted to build a deep-tech engine that solves high-friction enterprise problems—securely, scalably, and fast.
What followed was a crash course in building, breaking, and re-building—without losing speed. This is the playbook thAIng used to go from MVP to market.
1. Start with a Sharp, Pain-Killing Thesis
thAIng.ai didn’t begin as a general-purpose AI tool. Its initial thesis was narrow but surgical: automate complex decision workflows for compliance-heavy industries, starting with finance and infrastructure.
“Most AI products fail because they chase possibility instead of solving pain,” says Kearvyn. “We built backwards from bottlenecks.”
MVP principle: Build something small that hurts when it doesn’t exist.
2. Choosing a Scalable, Modular Tech Stack
The core architecture was built with Python-based microservices, orchestrated via FastAPI and deployed on Kubernetes. For AI modeling, the team used Hugging Face transformers paired with custom inference endpoints.
- Frontend: React + Tailwind, with Radix UI for atomic components
- Backend: Python (FastAPI), PostgreSQL, Redis
- AI Layer: GPT-4 fine-tuning + LangChain + private vector DBs
- Infra: Docker, Kubernetes (EKS), GitHub Actions for CI/CD
Why modular? Because scale demands flexibility.
“We designed for pivot points. Every component had to be swappable—especially our inference and data layers.”
3. Agile Sprints with a Reality Filter
thAIng’s product team runs bi-weekly sprints but keeps feature queues brutally lean. Weekly checkpoints include:
- Live customer feedback demos
- Model performance benchmarks
- Internal ‘pre-mortem’ reviews (What breaks at scale?)
Their mantra: Ship less, but ship smarter.
Tooling stack: Linear for sprint boards, Notion for product specs, Figma for prototyping, Slack integrations for real-time updates.
4. Solving for Security from Day One
Because thAIng works with enterprise clients in sectors like finance and logistics, zero-trust architecture and data sovereignty were non-negotiable.
- Encrypted API calls
- Role-based access control with biometric 2FA
- Region-specific data storage (India, EU compliance)
- On-prem deployment options via Helm Charts
Most MVPs ignore security. thAIng made it core to its GTM.
5. Balance Between Human and Machine Decisions
The platform’s intelligence is multi-layered. While it leverages LLMs for classification and workflow routing, final decisions in high-stakes cases are human-reviewed through a custom moderation UI.
This created a feedback loop where human analysts train the system over time—without fully automating away responsibility.
6. Lessons from the Trenches
What worked:
- Building with real client data in shadow mode before launch
- Creating vertical-specific modules (instead of one-size-fits-all AI)
- Internal QA ‘chaos weeks’ to simulate scale before onboarding
What failed:
- Over-engineering analytics dashboards that clients didn’t use
- Delayed user onboarding flows (fixed with AI co-pilots)
- Trying to self-host everything early—eventually hybridized with cloud-native services
Final Thought: MVP Is Just the First Sprint
At thAIng.ai, the product wasn’t born from a hackathon—it was carved out of real-world friction. What made it successful wasn’t just clean code or smart AI, but the discipline to listen, pivot, and protect the core vision.
In a world chasing velocity, thAIng’s real edge was durability.
⚡ Key Takeaways:
- Start with a sharp problem thesis—don’t chase generic AI hype
- Choose modular architecture that lets you pivot without rewriting
- Security and compliance can’t be afterthoughts in B2B AI platforms
- A lean, customer-informed sprint cycle beats bloated backlogs every time
