$1M+
Annual cost of a small ML team
2 ML engineers + 1 ML lead + infra
For Startups
Your VCs gave you capital.
We give you the AI engineering.
You raised money to build an AI product. Hiring a world-class ML team takes 6-12 months and costs $1M+/year. You can't wait that long, and you can't afford to get the architecture wrong. That's where we come in.
The Startup Math
Building an internal ML team is expensive. And slow.
+
6-12mo
Time to hire & ramp
Recruiting, interviewing, onboarding, context
=
Months lost
Before your first model is in production
While competitors ship and fundraise
AIshar Labs gives you an architect-led AI engineering team that starts building in weeks, not quarters — for a fraction of the cost of a full-time hire. When you're ready, we help you hire internally and transfer everything.
Problems We've Actually Solved
These aren't hypothetical. We've lived each one.
Every problem below is one a real startup brought to us — and one we solved.
💰
"We need AI to be fundable."
You have domain expertise and a thesis. Investors want to see a working AI system, not a slide about one.
We build the technical proof that makes you fundable — architecture, working models, and infrastructure that withstand due diligence. We've helped startups go from "idea with a deck" to "funded company with production AI."
✓ Helped startups become fundable and raise capital
🚀
"We need to build from zero."
You have a founding team and capital, but no ML talent and no AI codebase. The clock is ticking.
We become your AI engineering arm. We design the architecture, build the models, deploy the infrastructure, and ship the product — while your founding team focuses on customers, product, and fundraising.
✓ Built AI products and MVPs from zero for multiple startups
🔄
"Our dev agency shipped garbage."
You hired an agency or offshore team to build your AI. The code is a mess, the models don't perform, and you've lost months.
We've taken over from failed agencies and rebuilt properly — production-grade architecture, real ML engineering, systems that actually scale. It's painful to start over, but it's more painful to keep building on a broken foundation.
✓ Took over from failed dev agencies and delivered working systems
📈
"Our AI infra is eating our runway."
Your infrastructure bill is climbing every month. You're spending on compute what you should be spending on growth.
We re-architect your AI infrastructure for efficiency without sacrificing performance. One fintech startup went from $100K/year to $7K/year — same performance, 93% savings. That's runway, not waste.
✓ Saved a startup $93K/year through infrastructure re-architecture
Startup Build Stories
Real startups. Real systems we built. Real numbers.
Healthtech Startup
8-10mo
faster to hospital partnerships
HIPAA + SOC2 Compliant AI Platform
Hospitals wouldn't talk without compliance. We built the entire AI platform — compliant from day one. Multiple hospital partnerships secured months ahead of the founder's original timeline.
Compliance wasn't the goal — it was the unlock that opened every door.
HIPAASOC2Full PlatformHealthcare AI
Fintech Startup
$93K
saved annually
$100K/yr → $7K/yr Infrastructure
A fintech startup was burning $100K/year on AI infrastructure. We re-architected the entire system — 93% cost reduction, same performance. The savings extended their runway by months.
That's not optimization. That's a fundamentally different architecture.
Infra Re-architecture93% SavingsMLOps
Your Stage
We meet you where you are.
Different stages need different things. Here's how AIshar Labs maps to your moment.
Pre-Seed / Seed
Idea → Fundable
You have domain expertise and a thesis. You need enough technical proof to raise — not a full product, but the right architecture and a working prototype.
- →Architecture Sprint to validate AI approach
- →Working proof-of-concept for investors
- →Technical roadmap for fundraising deck
- →Data assessment and feasibility analysis
Start with: Architecture Sprint (2 weeks)
Series A
Product-market fit → Production AI
You've raised. Now you need to ship — fast. The architecture decisions you make now determine whether you scale or stall. This is where our Apple/Instacart experience matters most.
- →Embedded Build — we become your ML team
- →Production AI systems from zero
- →Architecture designed to scale with your growth
- →Knowledge transfer to build internal capability
Start with: Embedded Build (monthly, ongoing)
Series B+
Growth → Optimization
You have a product and users. Now you need better models, faster systems, lower costs. Infrastructure optimization and ML performance tuning at scale.
- →ML model optimization and fine-tuning
- →Infrastructure cost reduction
- →Scaling architecture for 10x growth
- →Custom LLMs for your specific use case
Start with: Targeted Build (fixed scope)
Investor Readiness
What investors see when we've built your AI.
When a startup comes to market with AI systems built by a team that designed search at Apple and recommendations at Instacart, investors notice. Not because of name-dropping — because the technical quality is visible in due diligence.
We've seen founders walk into funding conversations with a level of technical depth and production readiness that most pre-Series B companies can't match. The architecture is clean, the models are in production, the infrastructure is built for scale, and the team can explain every decision.
That's the difference between "we plan to use AI" and "here's our AI system running in production."
- ✓Production AI, not prototypes. Working systems serving real users, not notebooks and demos.
- ✓Architecture that scales. Built for the next 10x, not just today's load.
- ✓Clean technical due diligence. Every architectural decision documented and defensible.
- ✓IP you own 100%. No vendor dependency. No licensing traps. Full ownership.
- ✓Knowledge transfer done. Your internal team can explain, maintain, and extend the system.
"As Google for Startups Accelerator Americas alumni, we're embedded in the AI ecosystem — not observing it from the outside. I know what it feels like to burn runway while waiting for the right hire. I know what it feels like to explain to investors why your AI is different. And I know what it takes to build the kind of production ML system that makes investors lean forward instead of leaning back."
"If you're building something real, I'd rather spend 30 minutes talking about your problem than sending you a pitch deck. And if AIshar Labs isn't the right fit, I'll tell you — and point you to what is."
Manmeet Singh
Founder & CEO, AIshar Labs · Ex-Apple, Ex-Instacart
Start Here
Your AI isn't going to build itself.
30-minute founder-to-founder conversation. No pitch deck. No NDAs. Just an honest technical conversation about what you're building.
Talk to Manmeet