HemansAI

Data & AI Consulting, Development, AI Leadership Training.

Founder

85% of AI products and projects fail.

The two biggest challenges businesses face with AI:

1. Estimating and demonstrating AI value [VPs, CEOs, product managers, and decision-makers]
2. Lack of talent and skills [AI engineers and data professionals]

Most business leaders fall into the first category.

Even if you’re skilled at your job, your team might lack confidence that the AI solution will deliver the desired outcomes.

It’s frustrating, right? You see others succeeding with AI, but your team your team is stuck planning, fixing, or juggling disconnected tools.

AI is supposed to bring automation, sharper insights, and smarter decisions.

But when you try to implement it, you encounter chaos:

- Lack of clarity on ROI
- Endless troubleshooting instead of progress estimation
- Too many disconnected systems to demonstrate AI value

It’s easy to wonder if AI will ever deliver what it promises for your business and career.

Now, imagine a future where AI doesn’t just work—it works seamlessly in your organization.

Your data isn’t just stored—it powers real-time insights, precise forecasts, and simplified processes.

Picture an AI strategy that works without constant troubleshooting—a setup that scales and brings results.

I’m an AI consultant, here’s how I can help you:

- the AI leadership coaching

It’s about creating an AI path that’s right for your business or your career with real results, not just ideas.

Let’s connect. we’ll talk about AI strategies, products, and career opportunities.
connect@himanshuramchandani.co

Himanshu Ramchandani

ChatGPT can't do math. So why does your agent get calculations right?

You built an agent that "reasons"

Added ChatGPT + some tools

Called it intelligent

But here's what's actually happening:

↳ user asks a question
↳ LLM extracts parameters
↳ function gets called
↳ result comes back

That's it.

No magic. No intelligence. Just orchestration.

The real agent architecture:

Orchestration Layer → manages memory, state, and planning
Model → understands natural language intent
Tools → executes actual capabilities

When you ask "what's 65 × 85?"

The LLM doesn't calculate.

It CAN'T calculate.

It recognizes a math pattern, extracts numbers, calls calculator tool.

This is why your agent hallucinates prices but gets calculations right.

One uses training data (unreliable)

Other uses tools (deterministic)


Before: "my agent is smart"
After: "my agent routes to the right tool"

Stop building smarter prompts.

Start building better tool architectures.

Follow Himanshu Ramchandani for real AI engineering.


P.S. Building 7 production AI Agents in my bootcamp. Link in comments.
Join 75 Hard AI Engineering Challenge
Join 30 Day AI Agent Challenge [starting 2nd December]

AI Engineering & Leadership • Building an AI product studio and providing Development & Consulting services

Your transformer knowledge is worthless

- you read the "Attention is All You Need" paper
- you took a Hugging Face course
- you fine-tuned BERT

Congratulations - you are still useless

Here's the truth nobody tells you:

90% of AI engineers can't explain what happens in model.forward()

↳ they just stack layers and pray
↳ when training fails? chatGPT or stack overflow
↳ when memory explodes? smaller batch size
↳ when inference is slow? "just add more GPUs"

This is why you'll never build anything real

The engineers building GPT, Claude, and Llama?

They don't just use transformers

↳ they optimize every matrix multiplication
↳ they understand gradient flow at scale
↳ they know exactly why their model fails

Stop collecting certificates

Start understanding fundamentals

- matrix multiplication > another course
- calculus > another tutorial
- production constraints > another paper

You want to be valuable?

Build a transformer from scratch

Optimize it until it's 5x faster

Deploy it at scale

Then you're an AI engineer

Until then? You're just another API caller

Follow Himanshu Ramchandani for the brutal truths about AI engineering

P.S. - Get the deep dive in the FREE 75 Hard AI Engineering Challenge. AI Agents Bootcamp, ending early bird access today. (last 6 early bird seats left).

You're building AI agents. But you don't understand how they scale - Here's Why:

Every agent you deploy uses resources

Every API call costs money

Every response consumes compute

Most engineers build agents without understanding efficiency

Here's what changes everything:

- when you see this: agent.run()
- that innocent line is burning through your compute budget

Scaling laws predict exactly how your agent will perform:

↳ model size vs accuracy
↳ token usage vs cost
↳ response time vs user satisfaction

The truth about production agents:

Before understanding scaling:

- random model choices
- unpredictable costs
- can't forecast performance

After understanding scaling:

- calculate costs before deploying
- choose optimal model sizes
- predict agent behavior at scale

Chinchilla's insight: smaller models + more data = better results + lower costs

A 7B model trained right beats a 13B model trained poorly

This is Day 26 in the 75 Hard AI Engineering Challenge [join for free]

Real engineering.

Real results.


Follow me Himanshu Ramchandani for daily AI engineering insights

PS: FREE AI Agents Workshop this weekend. Join via newsletter 🚀 - AI Agent Bootcamp spots filling fast (check comments)

Comfort is why most AI engineers stay mediocre

Day 30 of the 75 Hard AI Engineering Challenge is done

Half the cohort is thriving

The other half? Drowning in content they have never seen before

"Himanshu, this is too hard"
"Can we slow down?"
"I don't understand derivatives"

My response?

(this dialogue from KungFuPanda movie)

The engineers building production AI systems aren't comfortable

- they are learning RAG at midnight
- debugging embeddings at 5 AM
- implementing attention from scratch on weekends

discomfort = growth

Here's what I'm seeing from those who push through:

↳ week 1: confused
↳ week 2: frustrated  
↳ week 3: connecting dots
↳ week 4: building confidently

The struggle isn't a bug <> It's the feature

Real AI engineering isn't copying tutorials

It's understanding

- why your training explodes
- why your embeddings cluster weird
- why your inference is slow

45 days to go. The best part?

You're already different than Day 1.

Still accepting latecomers who want the challenge

Follow Himanshu Ramchandani for real AI engineering

Here are some updates:
↳ 75 Hard AI Engineering Challenge ($0) 
↳ AI Agents Bootcamp (Start at $0) - add 7 deployed Agents in your portfolio
↳ 30 Day AI Agents Challenge ($0) starting 2nd December
↳ AI Builder's Bootcamp (Launching in January 2026)
↳ 4 Hands-On Workshop(for professionals)/Webinar(for leaders) in November on AI Agents 
↳ Product Launch - Knowledge Engine at Dextar
↳ AI Leadership Accelerator - The Elite - beta version (DM for pre booking)
↳ Long form AI Engineering deep dives on substack (sharing this week)
↳ Dropping new episode of Humanly Foolish podcast (Ask Anything AI)

- more updates will share later


P.S. - Free AI Agents Workshop this weekend. Link in comments.

You are spending $1,500 extra on API calls. Because you are still using JSON

Every API call you make:

- chatGPT charges per token
- claude charges per token
- gemini charges per token

That employee dataset? 126,860 tokens in JSON

Same data in TOON? 49,831 tokens

TOON (Token-Oriented Object Notation)

↳ 60% fewer tokens
↳ 60% lower costs
↳ same information

Here's what nobody tells you:

JSON wasn't built for LLMs.

It's verbose by design

JSON:

{
"users":[{"id":1,"name":"Alice"},
                {"id":2,"name":"Bob"}]
}
```

TOON speaks LLM's language:

users[2]{id,name}:
 1,Alice
 2,Bob


but Himanshu, why this matters?

When you send data to GPT-4:

↳ repeating field names = wasted tokens
↳ extra brackets = wasted tokens
↳ redundant syntax = wasted tokens

TOON declares structure once

Streams data as rows

Real impact at scale:

↳ 1M API calls with that employee data?

JSON: $2,537 in tokens
TOON: $996 in tokens

You just saved $1,541

What it means-

Before: blindly serialize everything as JSON
After: encode as TOON for LLM input, get 30-60% token reduction

Have you calculated your token costs? [repo link in comments]

Follow me Himanshu Ramchandani for practical AI engineering insights

P.S. - AI Agents bootcamp starting on 2nd December, 23 out of 60 are taken, today is last day of early bird access.

You are running local (on-premises) LLMs but don't know if they are actually good

- every AI engineer downloads Llama or Mistral
- runs them locally
- feels excited about - free inference

Then reality hits:

- is this output even correct?
- is GPT-4 actually better?
- am I just burning electricity for worse results?

Here's what nobody tells you:

Running local models without benchmarking is like driving blindfolded

You need three numbers:

↳ speed (tokens/second)
↳ quality (% correctness)
↳ cost (real cost per 1M tokens)

When I started the 75 Hard AI Engineering Challenge,
I thought local = always better

I was wrong

Real example from Day 32:

- mistral 7B locally: 35 tok/s, $1.50 per 1M tokens (estimated)
- gpt-4 API: 18 tok/s, $30,000 per 1M tokens (claimed)

For code generation?

- mistral: 67% correct
- gpt-4: 100% correct

The question is not "which is faster"
The question is "which solves MY problem"

For 100K tokens/month → use APIs
For 10M tokens/month → local wins
For sensitive data → local (on-premises) only

benchmarking:

Before:

- "this model feels slow"
- "the output seems wrong"
- "maybe I should try GPT-4"

After:

- "this model runs at 32 tok/s"
- "it's 89% accurate on my test set"
- "break-even is at 2M tokens/month"


Do this:

↳ pick 100 real examples from your work
↳ run through 3 different models
↳ measure speed, cost, quality
↳ choose based on data, not hype


Follow me Himanshu Ramchandani for daily AI engineering insights that actually matter

PS: Join the FREE 75 Hard AI Engineering Challenge [link in comments]

PPS: Learn AI Agents in the upcoming bootcamp, limited seats, FREE workshop on RAG  Agents today 7:30 PM IST

If you want to ship a production LLM app by 2025, you have less than 40 days so here's what you are going to do:

Stop calling OpenAI and praying it works

Your app breaks when their API goes down

You have zero control

Here's what production engineers actually do:

Build fallbacks

↳ openAI down? switch to anthropic
↳ both down? use your self-hosted model
↳ all automatic - users never notice

The math that changes everything:

- commercial API only:
$9,500/month at 50M requests

- hybrid approach (API + self-hosted):
$730/month at 50M requests

That's 92% savings

Companies like Notion and Shopify don't use one LLM

They use smart routing between multiple models

This is part of the Day 35 of the 75 Hard AI Engineering Challenge:

↳ multi-provider fallback system
↳ self-hosted model with vLLM
↳ cost optimization logic
↳ production deployment on AWS

This isn't theory

The difference between a demo and a product? 

reliability when things break

Build something that actually ships.


Follow Himanshu Ramchandani for real AI engineering.


PS: Build a fridge AI agent that will order groceries automatically, join the community and bootcamp waitlist.

Stop building smarter agents. Start building agent teams.

Everyone's obsessing over:

- better prompts
- bigger context windows
- smarter models

the real breakthrough is multi-agent architecture

Single Agent Problem:

You: optimize my resume for this role
Agent tries to:

- understand job requirements
- analyze your background
- rewrite experience
- create cover letter
- format everything

It's doing 5 jobs at once

Quality suffers

Multi-agent solution, same task, different approach:

Agent 1 → deep dives on company (research specialist)
Agent 2 → rewrites your resume (writing specialist)
Agent 3 → creates cover letter (personalization specialist)

Each agent is great at ONE thing.

- they share context
- collaborate
- roduce better outputs

Difference:

- single agent: Jack of all trades, master of none
- multi-agent: Specialists working together


Architecture Basics:

Orchestration layer → Routes tasks to right agents
Shared memory → Agents access same context
Sequential workflow → Output of one feeds into next
Parallel processing → Multiple agents work simultaneously

CrewAI, LangGraph, AutoGen already enable this.

Tonight at 7:30 PM IST, I'm showing you how.

We build a multi-agent system that:

→ researches companies
→ optimizes your resume
→ writes personalized cover letters

Live and step by step

Free workshop [Link in comments]

This is how production AI systems actually work

Follow Himanshu Ramchandani for real AI engineering

🎤 Speaker Spotlight
🚀 AI Unplugged: A Prelude to DevFest 2025
Get ready to learn from the best!
Join Himanshu Ramchandani, expert in AI Engineering & Leadership, as he shares insights on
💡 “How to Learn AI Fast with a Builder’s Mindset?”

Discover how to accelerate your AI journey by thinking like a creator, innovator, and problem-solver.

📅 Date: Saturday, 15th November 2025
🕙 Time: 10:00 AM onwards
📍 Venue: Devi Ahilya University Auditorium, Indore
🔗 Register here: https://lnkd.in/dufGG_4r

#SpeakerSpotlight #DevFest2025 #PreDevFest2025 #GDGIndore #AIUnplugged #GoogleDevelopers #LearnWithGDG #BuilderMindset #AI #TechCommunity #IndoreTech #DeveloperCommunity

I've never shared this before

6 months ago, a senior engineer got rejected for promotion

"You use AI tools. We need someone who can architect AI systems."

That's the gap in 2025

Companies don't want LLM users

They want AI Agent Architects

I'm launching the AI Agents Bootcamp December 2nd.

What you build:

↳ 7 production-ready agents in 4 weeks
↳ ReAct patterns & planning systems
↳ LangGraph state machines
↳ Multi-agent orchestration
↳ Production deployment with guardrails

Real results:

Mamta (Amazon): "made my AI engineering transition"
Saravanan: "went from mechanical to AI engineer"
Meena (Amazon): "hands-on approach changed everything"

FREE workshops before launch:

Nov 19: RAG Agents Production
Nov 26: Deploy FastAPI + Docker

189 waiting. 60 seats only.

Two paths:

1 - Join FREE workshops (link in comments) and get FREE AI Agents Guides
2 - Secure bootcamp seat now

Not for quick hacks.

For engineers ready to architect systems companies fight over.

♻️ Repost if you know someone stuck at "just using ChatGPT"

Follow me Himanshu Ramchandani for AI engineering that matters

STOP using LLM APIs like toys

You're calling OpenAI's API like it's a REST endpoint

It's not

Every junior dev I meet:

- hardcodes API keys in Python files
- no retry logic
- no fallback providers
- no token management

Then their app crashes in production and they blame the API

Here's the truth:

↳ they'll fail on you
↳ llm APIs aren't reliable
↳ anthropic will have outages
↳ openAI will hit you with rate limits

Your $4k bill? That's your retry loop gone wrong

But nobody tells you this because they're too busy showing you toy examples from documentation

Production AI engineering isn't about making API calls

It's about:

↳ circuit breakers
↳ cost optimization
↳ exponential backoff
↳ multi-provider fallbacks

Your GPT-4 call doesn't need GPT-4

Use GPT-3.5 and save 80% on costs

Your system crashes?

You didn't classify your errors properly

Stop copying tutorials

Start building systems that don't break when the API sneezes

Follow me Himanshu Ramchandani for production AI that actually works

PS: This is from the Day 30 guide of 75 Hard AI Engineering Challenge, Join the AI Agents Bootcamp is Live.

[Live Workshop] RAG Agents - Build a production-ready document Q&A system


What we are going to cover:
- Retrieval-Augmented Generation
- Embeddings and Vector Representations
- Vector Databases
- RAG Pipeline
- Hands-On Project

Date: 19th November, 2025 (Today)
Time: 7:30 PM IST

→ why vector DBs aren't enough
→ how persistent memory changes everything
→ what enterprise RAG actually needs


Register by joining the workshop inner circle group [link in comments]

[Webinar] AI Agents for Business Leaders - ROI & Implementation Strategy

- roadmap
- case studies
- justifying team investment

Date: 10th November 2025
Time: 7 PM IST

PS: Register for the Webinar for FREE [check comments]

ValueLabs

Software Engineer Specialist

Technophile Bud

Intern

Pocket Guru

I describe myself as a tech community enthusiast with a knack for content writing. I have always been eager to learn more about Python and Machine Learning. Having an analytical brain, I'm particular about my research and writing.
I live by the motto "the more you share, the more you learn" and being a techie, it's fun to learn, create and share.

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