Most agencies make more money when you ship more slowly. Hourly billing = perverse incentive.

We flipped it.

AI Velocity Pods pair 3 5 senior engineers with governed agentic workflows, AI agents running test generation, code review, QA, and docs in parallel to human dev. Not sequential. Parallel.

Result: 38-day avg delivery vs industry's 120+. Fixed price. No scope creep invoices.

We build AI-native software on fixed-price contracts, no hourly billing, no surprise invoices.

Here's how it works:
AI Velocity Pod assembled in 48 hours
Milestone demos every 2 weeks, pay when software ships
Full IP transfer on delivery. Zero lock-in.

We've shipped 300+ products across 22 countries for clients like Apna (50M users), AssureCare (53M members), and BankSathi (200K+ advisors).

ISO 27001 & ISO 9001 certified.

"AI Velocity Pod Starter Kit", A free, open resource (template pack + methodology guide) that helps engineering teams structure their first agentic sprint using Ailoitte's proven governance framework.

Why this works on Product Hunt:

• Free resource = high upvote motivation (PH loves useful free things)

• Template + methodology = tangible, immediately useful

• Positions Ailoitte as a thought leader without selling a service directly

• Drives qualified traffic to ailoitte.com from engineers exploring agentic workflows

Most software projects don't fail because of bad code. They fail because of bad incentives and bad scoping.

Founders and engineering leaders spend weeks getting estimates from agencies and still end up with timelines that slip and budgets that balloon. We built the AI Velocity Pod Starter Kit because we were tired of watching that pattern repeat and because the methodology that fixes it shouldn't live behind a sales process.

Here's what's in the kit:

1. AI Velocity Pod Team Structure Template: The exact team composition, role definitions, and handoff protocols we use to compress a traditional 120-day SDLC to a 38-day delivery cycle. Edit it for your team's context.

A free, open resource bundle, AI Velocity Pod team structure template, agentic QA checklist (OWASP-aligned), fixed-price MVP scoping worksheet, and 38-day sprint cadence template. Lead-gen tool that provides genuine value while demonstrating Ailoitte's methodology to the exact audience most likely to need it.

Why this works on Product Hunt:

• Free tools with genuine utility consistently outperform paid launches for trust-building

• Solves a real, named problem (AI-native team scoping is genuinely hard)

• Educational, teaches the AI Velocity Pod concept through usage

• Collects qualified inbound leads organically

Hiring vs. Outsourcing
Early-stage teams face this constantly. Hiring AI engineers takes 3 6 months. Agencies are slow and billed by the hour. But moving fast matters more than ever.

More teams are experimenting with outcome-based delivery models, fixed scope, fixed timeline, and full IP transfer.

Has anyone here gone that route? What worked, what didn't?

You hire an AI vendor. They're smart. Months pass. You have demos, Notion docs, and Slack threads, but nothing in production.

It's not always the vendor's fault. Vague outcomes, no accountability structure, and hourly billing create exactly this situation.

How are founders here actually managing AI delivery without getting burned?

There's a lot of debate around MVP timelines. Some say 3 months is lean. Others are shipping functional AI prototypes in 3 4 weeks.

The difference usually isn't budget; it's how the team is structured and whether the scope is truly locked.

Founders: how long did your first AI MVP take, and what would you cut if you did it again?

Most AI projects don't die because of bad technology. They die because of slow delivery cycles, misaligned scope, and teams that bill hours instead of owning outcomes.

The companies actually shipping AI in 2025 have one thing in common: they stopped treating AI as an R&D experiment and started treating it like a product sprint.

What's the biggest bottleneck you've hit taking an AI idea into production?