~5yr
Building ML at Apple
Search: Maps, Safari, Spotlight
For Enterprises
Your board wants AI results.
Not AI plans.
You've seen the strategy decks. You've sat through the vendor pitches. What you haven't seen is a team that actually builds production AI at scale — because they've done it at Apple and Instacart. That's what AIshar Labs delivers. For teams building on Google Gemini and other frontier models, AIshar Labs is backed by Google for Startups - not just another API user.
Built by engineers from
~4.5yr
Architecting ML at Instacart
Search, Recs, Ranking, Autocomplete
15
AI patents filed
Personalization, retrieval, embeddings
B+
Queries served at scale
Strict latency, high QPS, production
The Enterprise AI Problem
You've spent on AI. Have you shipped?
The enterprise AI landscape is full of firms that can assess, advise, and strategize. What's missing is firms that can build.
Large consultancies spend billions on AI practices, then staff projects with juniors who've never shipped a production model. The result: expensive assessments, long timelines, and systems that stall in the proof-of-concept stage.
Meanwhile, your competitors are moving. The window between "exploring AI" and "left behind" is closing faster than any board presentation suggests.
AIshar Labs is different because we're not consultants who learned AI. We're AI engineers who worked at Apple-scale. The person who designs your system is the same person who built search for a billion Apple users.
Expensive assessments, no production systems
Big 4 engagement ends. You have a report. You still don't have AI.
Proof-of-concept purgatory
Demo works in a notebook. No one can get it into production.
Junior consultants, senior prices
The partner pitched you. The analyst builds it. You pay partner rates.
Vendor lock-in by design
Proprietary frameworks. Only they can maintain it. That's the plan.
What We Build
Production AI systems, not prototypes.
Every system below is one we've built in production — at Apple, Instacart, or for clients.
🔍
Search & Retrieval
Enterprise search that actually finds what people need. Query understanding, spelling correction, ranking, relevance tuning — the same patterns that power Apple Maps and Safari.
Built at: Apple Search (Maps, Safari, Spotlight)
🎯
Recommendation & Personalization
Systems that learn what each user wants and surface the right content, products, or actions. Embeddings, collaborative filtering, contextual bandits, real-time ranking.
Built at: Instacart (Recipes, Products, Feed Ranking)
🤖
LLM Integration & Fine-Tuning
Not generic ChatGPT wrappers — purpose-built LLMs fine-tuned for your specific tasks, data, and user base. Models that move your business metrics, not just demo well.
Built for: Enterprise client (engagement + conversion lift)
⚙
AI Infrastructure & MLOps
Scalable ML platforms, deployment pipelines, model monitoring, and cost optimization. We've reduced infrastructure costs by 93% for one client — same performance, different architecture.
Built for: Fintech startup ($100K/yr → $7K/yr)
📊
Data Pipelines & ML Platforms
End-to-end data infrastructure — from raw data ingestion through feature engineering to model training and serving. Designed for petabyte-scale from day one.
Built at: Apple & Instacart (petabyte-scale pipelines)
🛡
Compliant AI Systems
HIPAA, SOC2, and industry-specific compliance built into the architecture, not bolted on after. The infrastructure that enterprises and regulated industries require.
Built for: Healthtech startup (HIPAA + SOC2 platform)
Where This Expertise Comes From
Not theoretical. Battle-tested at scale.
Every system we build for enterprises draws on patterns proven at these companies.
Apple
Search at Apple Scale
Led ML engineering for the search systems powering Maps, Safari, and Spotlight. On-device personalization, phrase-level spelling correction, instant search under strict latency and privacy constraints. Multiple innovations showcased at WWDC.
~5yr
tenure
3+
patents
B+
queries
Instacart
Recommendation at Instacart Scale
Architected search, recommendation, ranking, and autocomplete ML systems. Designed embedding-based retrieval, contextual bandits, and A/B experimentation frameworks. Multi-million dollar revenue impact.
~4.5yr
tenure
10+
patents
$MM+
impact
Adobe
Cloud & Analytics at Adobe Scale
Led data and ML initiatives across Adobe's creative and document workflows. Drove measurable improvements to Reader's core features and cloud-based analytics, operating within massive distributed systems.
3yr
tenure
Sr.
technical staff
Enterprise Build Story
Custom LLMs that moved business metrics.
Enterprise Client
Off-the-shelf
Fine-tuned
Engagement▲ +31%
Conversion▲ +14%
Task Accuracy▲ +7%
Purpose-built LLMs beat generic, every time.
An enterprise client needed AI that moved real business metrics — not a chatbot demo. Off-the-shelf models weren't delivering. We fine-tuned LLMs for their specific tasks, users, and data.
The result: measurable lifts across engagement, conversion, and task accuracy — hitting the targets the business had set. The models are proprietary to the client, trained on their data, optimized for their outcomes.
Anyone can call an API. We build models that become your competitive advantage.
Fine-tuned LLMsEngagement LiftConversion LiftProprietary Models
Industry Experience
Deep enough to build. Broad enough to adapt.
We're industry-agnostic in capability, but we've built real systems in these verticals.
🏥
Healthcare
HIPAA and SOC2-compliant AI platforms. Medical imaging, clinical workflows, hospital integration. Built systems that unlocked partnerships regulated institutions require.
Proof: Built compliant AI platform for healthtech startup → hospital partnerships 8-10mo faster
💲
Financial Services
AI infrastructure optimization, risk models, personalization engines. Understanding of regulatory requirements and the low-latency, high-reliability demands of financial systems.
Proof: Re-architected fintech AI infra, 93% cost reduction ($100K → $7K/yr)
🛒
E-Commerce & Retail
Search, recommendation, ranking, and personalization at scale. The same systems our founder built at Instacart — adapted for your catalog, your users, your metrics.
Proof: Instacart search & recommendation architecture, 10+ patents, $MM+ impact
Setting Expectations
We are not a Big 4 consultancy
- ✕60-page assessments before any code is written
- ✕Teams of 20 juniors billing partner-level rates
- ✕Proprietary frameworks designed to create dependency
- ✕18-month transformation timelines
- ✕Strategy that never becomes production
We are a focused AI engineering team
- ✓Architect-led — senior engineers do the work
- ✓Small team, massive output, Apple/Instacart rigor
- ✓You own 100% of code, models, and IP
- ✓Production systems in weeks, not quarters
- ✓Knowledge transfer — your team takes over
How We Engage
Two models for enterprise teams.
Both start with a conversation, not a proposal.
Embedded Build
Monthly · Ongoing
We embed with your engineering team for sustained AI development. Ideal for organizations building AI capability they intend to own and operate long-term.
- ✓Dedicated ML architect(s) integrated with your team
- ✓Full development: models, infrastructure, deployment
- ✓Works within your security, compliance, and tooling
- ✓Continuous knowledge transfer to internal team
- ✓Flexible scope that evolves with your priorities
Targeted Build
Fixed scope · Fixed timeline
A specific AI system, built and delivered. Clear milestones, clear handoff. For organizations with a defined problem and a desire for a concrete deliverable.
- ✓Scoped project with milestone-based delivery
- ✓End-to-end: architecture through production deployment
- ✓Complete codebase, models, and documentation
- ✓Team training and operational handoff
- ✓Post-launch support period included
Next Step
Production AI. Not another deck.
A direct technical conversation with our founder — the person who built search at Apple and ML infrastructure at Instacart. No sales team. No RFP. Just an honest assessment of whether we're the right fit.
Talk to Manmeet