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Sunil Kumar

1d ago

Pre-vetted AI engineers. Productive in 2 weeks. 70% less.

US ML engineer hiring is difficult for most startups, $250K $350K packages, 4 6 month timelines.

AI Velocity Pods pairs pre-vetted offshore AI engineers with senior architects. Productive in 2 weeks. 70% less than US equivalents.

Vetting on actual production work, not interviews
Senior architect on every engagement
2-week structured onboarding
100% IP ownership
Fixed price, no hourly billing

Sunil Kumar

2d ago

How AI Velocity Pods ships a production-ready MVP in 4 weeks

We wanted to share how AI Velocity Pods actually work for anyone building an MVP right now.

The short version: one senior architect, AI dev agents handling the heavy lifting, and a structure that's designed around a 28-day handoff.

Sunil Kumar

2d ago

AI Velocity Pods is live on Product Hunt, we'd love your support

We're live on Product Hunt today with AI Velocity Pods.

Your support means everything: upvotes, comments, questions. All of it helps.

Here's what we're sharing:

AI Velocity Pods is the way we build software, not a platform you log into. Each pod pairs AI agents with senior engineers inside a fixed-price engagement. No hourly billing. No open-ended timelines. You agree on what gets built. We build it.

Sunil Kumar

3d ago

Production-grade RAG engineering. Demo to 94% accuracy.

Most RAG systems work in demos. They fail in production.

We know because we've been on the call when a VP forwards a wrong AI answer to three of their reports. That's the moment you realize your 62% baseline accuracy is a business problem, not just a technical one.

Sunil Kumar

3d ago

Ailoitte: AI Velocity Pods - Ship in weeks, not months. 5x faster. Fixed price. not hours

Ailoitte replaces hourly uncertainty with AI-native teams built for fixed-price, outcome-based delivery. Ailoitte is an AI-native product engineering partner helping founders and enterprises build, launch, and scale digital products with greater speed, clarity, and accountability. We combine product thinking, senior engineering, and AI-native delivery models to turn ideas, legacy systems, and growth strategies into production-ready software.
Sunil Kumar

7d ago

Ailoitte | AI-native engineering pods for early-stage startups

Outcome-based AI product engineering for pre-seed to Series A founders

Most AI development engagements start with a sprint plan. We start with an audit.

Before we write a line of code, we look at the use case technically, what the AI actually needs to do, where inference is the right tool versus where a deterministic approach works better, and what "good" looks like in production versus in a demo.

Sunil Kumar

7d ago

Ailoitte: MVP in 4 Weeks, fixed price product engineering for startups

Fixed-scope MVP delivery for pre-seed founders who can't afford scope creep

Most early-stage founders don't have a build problem. They have a timeline and budget problem.

We've worked with enough pre-seed teams to see the pattern: the first month gets eaten by scoping conversations. The second month gets eaten by revisions on decisions that should have been made in week one. By month three, the MVP has moved, and so has the budget.

Ailoitte: MVP in 4 Weeks is a fixed-scope, fixed-price engagement. We scope the MVP with you before a line of code is written, lock it, and ship in four weeks.

Sunil Kumar

13d ago

We're launching Ailoitte: MVP in 4 weeks Delivery on Product Hunt this Friday

Hey everyone - wanted to share directly with our community first.

Ailoitte: MVP in 4 weeks. Delivery goes live on Product Hunt at 12:01 AM EST Friday, April 17.

Most MVPs stall for the same three reasons: scope creep mid-sprint, QA treated as a Week 4 problem, and founder decisions taking 3+ days.

If you follow this page, you'll get notified automatically when we go live. Would genuinely love your feedback on launch day: http://producthunt.com/products/...

Sunil Kumar

15d ago

How we think about data governance in regulated industry deployments

One thing we get asked about constantly, especially in healthcare and fintech, is how we handle the audit trail problem.

Every AI query needs to be attributable to a specific user, timestamp, and context. Not as an afterthought. Baked into the architecture from day one.

Sunil Kumar

16d ago

Makers who've shipped AI agent systems in production, what broke that you didn't expect?

Building production agent systems is genuinely hard in ways that demo videos don't show.

The failure modes I keep running into:

  • Cascading quality degradation when agent confidence doesn't propagate between transitions

  • Race conditions in the shared state under concurrent load

  • Silent errors where the orchestration reports success, but the final output is quietly wrong

That last one is the most dangerous. Everything looks fine until a real user hits it.

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