Principle 01
Architect-led, always
The person you talk to is the person who builds. No bait-and-switch — senior architects do the work, not junior developers reading a playbook.
How We Work
We don't advise.
We embed and build.
No discovery marathons. No 60-page proposals. We move fast, build real things, and make sure you own everything when we're done. Here's exactly what working with AIshar Labs looks like.
Our Principles
Principle 02
You own everything build for you
Full codebase, trained models, architecture docs, deployment configs. 100% of the IP is yours. We build independence, not dependency.
Principle 03
Production or nothing
We don't deliver prototypes and walk away. Every engagement ends with systems in production, serving real users, generating real value.
The Process
From first conversation to production AI in weeks, not quarters.
Every engagement follows this path. No surprises, no scope fog.
Step 130 min
Founder-to-Founder Conversation
Not a sales call. A direct conversation with Manmeet about what you're building, where AI fits, and whether AIshar Labs is the right team for this. No NDAs needed. No slides. Just an honest technical conversation.
Half the time, we tell people they don't need us yet — and point them to what they should do first. That honesty is why the other half become clients.
You walk away with
- →Honest assessment if and how AI is right for your problem
- →High-level technical direction (even if we don't work together)
- →Clear yes/no on whether there's a fit
Step 22 weeks
Architecture Sprint
A paid, focused engagement where we go deep on your problem. We audit your data, map the technical landscape, and design the AI architecture — not as a document, but as a working blueprint that's ready to build from.
Most clients tell us this sprint alone saved them months of wrong turns. It's also the fastest way to see how we work before committing to a full build.
You walk away with
- →System architecture design (not a slide deck — a build-ready spec)
- →Data assessment and readiness analysis
- →Technical roadmap with realistic timelines
- →Working proof-of-concept where feasible
- →Go/no-go recommendation with clear reasoning
Step 3Ongoing
Build
We embed with your team and build. Not in a silo — alongside your engineers, your product team, your stakeholders. Multiple standups, shared repos, real-time collaboration. We're not an external vendor you check in with on Fridays.
This is where the architecture becomes production code, where models get trained and deployed, where infrastructure gets built for scale. The same caliber of engineering behind Apple Search and Instacart's recommendations.
What happens here
- →ML model development, training, and optimization
- →Production infrastructure and deployment pipelines
- →Iterative testing, experiments, and metric-driven decisions
- →Continuous knowledge transfer to your team
Step 42-4 weeks
Handoff & Independence
We don't disappear. We transfer everything — codebase, models, documentation, operational runbooks — and train your team to own, maintain, and extend what we built. When we leave, you're not dependent on us. That's by design.
You walk away with
- →Complete codebase and trained models (you own 100%)
- →Architecture documentation and operational runbooks
- →Team training — your engineers can maintain and extend
- →Optional ongoing support if you want it
Engagement Models
Three ways to work together.
Pick the one that fits your stage and problem. Or start with the Sprint and we'll figure out the rest together.
Architecture Sprint
2 weeks · Fixed scope
Go deep on your problem before committing to a full build. Get a production-ready system design and a clear path forward.
- ✓System architecture design
- ✓Data audit and readiness assessment
- ✓Technical roadmap with timelines
- ✓Working proof-of-concept
- ✓Go/no-go recommendation
Best for: Founders validating an AI approach before investing. Teams with budget for a specific technical decision.
Embedded Build
Monthly · Ongoing
We become your AI engineering team. Embedded in your standups, your repos, your Slack. Full architect-led development, month to month.
- ✓Dedicated ML architect(s) on your team
- ✓Full development: models, infra, deployment
- ✓Daily collaboration with your engineers
- ✓Continuous knowledge transfer
- ✓Flexible scope — adapts as you learn
Best for: Funded startups building AI products. Teams that need sustained engineering firepower without a 12-month hiring cycle.
Targeted Build
Fixed scope · Fixed timeline
A specific AI system, built and shipped. Clear deliverables, clear timeline, clear handoff. For teams that know exactly what they need.
- ✓Defined scope and milestone plan
- ✓End-to-end build and deployment
- ✓Complete codebase and documentation
- ✓Team training and handoff
- ✓Post-launch support period
Best for: Enterprises with a defined AI project. Teams that need a specific system built by people who've done it at scale.
What You Own
When we leave, you keep everything.
Most consultancies create dependency — proprietary frameworks you can't maintain, models you can't retrain, infrastructure only they understand. That's their business model. It's not ours.
We build your AI system as if we were your internal team. When the engagement ends, there's no lock-in, no licensing, no ongoing dependency. You own 100% of the IP and your team can run with it.
Our anti-dependency promise
We succeed when you don't need us anymore. That might sound counterintuitive for a services business, but it's how we earn referrals and repeat work. The healthtech startup we built a HIPAA-compliant AI platform for? They now run it independently. The fintech whose infrastructure we re-architected from $100K to $7K? Their internal team maintains it. That's the goal, every time.
- 💻
Complete Codebase
Every line of code, every config file, every deployment script. Hosted in your repos from day one.
- 🧠
Trained Models
All ML models, training pipelines, evaluation scripts, and model artifacts. Ready to retrain and iterate.
- 📜
Documentation
Architecture docs, API specs, operational runbooks, and decision logs. Not afterthought docs — living documentation.
- 🎓
Trained Team
Your engineers understand the system, can maintain it, extend it, and debug it. We transfer knowledge continuously, not at the end.
- 🔒
Zero Lock-in
No proprietary frameworks. No licensing fees. No "call us to change anything." Full independence.
Common Questions
Things founders ask before starting.
Typically within 1-2 weeks of signing. The Architecture Sprint can often start the same week as our initial conversation. For embedded engagements, we onboard within two weeks.
Yes. We primarily have team members in both the US and Canada, and we work with clients globally. All of our Apple and Instacart work was done in the San Francisco Bay Area. Time zone overlap matters — we optimize for it.
Even better. We embed alongside your existing engineers, not in place of them. We bring ML architecture expertise they may not have yet, and we transfer that knowledge as we build. Your team gets stronger, not sidelined.
Start with an Architecture Sprint. It's the smallest investment and produces the most clarity — a production-ready system design, data assessment, and technical roadmap you can use to fundraise, hire, or build independently.
A fractional CTO advises. We build. You get architect-level strategic thinking plus hands-on engineering execution — the person who designs the system also writes the code. Most fractional CTOs can tell you what to build. We actually build it.
Yes. We've built HIPAA and SOC2-compliant AI platforms from the ground up. Compliance isn't an add-on — we architect for it from day one. It's the reason one of our healthtech clients secured hospital partnerships months ahead of schedule.
Start Here
30 minutes. No pitch deck.
Tell us what you're building. We'll tell you honestly whether we can help — and if we can't, we'll point you in the right direction.
Talk to Manmeet