Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

AI Agents are task-specific, autonomous systems that integrate large language models with structured tools, APIs, and real-time data sources. 

They operate across domains such as cybersecurity, supply chain logistics, and healthcare by executing workflows that traditionally required human-in-the-loop decision making. 

These agents leverage vector databases, retrieval-augmented generation, and fine-tuned embeddings to enable contextual reasoning and dynamic response generation. 

As orchestration frameworks mature, multi-agent systems are increasingly capable of handling end-to-end processes like demand forecasting, patient triage, and adaptive tutoring with minimal supervision.

The below chart shows just how broad their impact is:

1.🔹 IT & Security : Phishing filters, threat detection, patch suggestions
2.🔹Healthcare : Patient alerts, medical chatbots, symptom matching
3.🔹 Education : Flashcards, concept explainers, AI tutors
4.🔹 Sales & Marketing : Lead scoring, campaign ideas, email outreach
5.🔹Logistics : Fleet tracking, demand forecasting, inventory updates
6.🔹Manufacturing : Predictive maintenance, robotic control, energy monitoring
7.🔹 Research : Academic writing, data cleaning, topic expansion
8.🔹 Customer Support : FAQ bots, emotion detection, chat summaries
9.🔹 Smart Environments : Digital twins, voice commands, access control
10.🔹Ops Automation : Shift scheduling, system alerts, order tracking

What used require significant manual effort, now takes a few smart agents.

I believe it’s a great time to start exploring and experimenting in this space…

If you’re learning AI automation without a roadmap, you’re guaranteed to get overwhelmed.

People usually “learn AI automation” by jumping straight into tools… and then wonder why nothing works consistently.
Real automation requires structure - thinking, logic, testing, and a gradual build-up of skills.

This 18-day roadmap breaks down the exact sequence to go from zero → confidently building automations with AI, APIs, tools, and no-code platforms.

Here’s the full breakdown, day by day:

Day 1 - AI Automation Fundamentals
Learn what automation really means, how it differs from AI and agents, and see real examples.

Day 2 - Automation Thinking
Break work into steps, triggers, and outcomes - the mindset behind every good automation.

Day 3 - APIs & Webhooks Basics
Understand how apps communicate and how events trigger workflows.

Day 4 - No-Code Automation Platforms
Explore Zapier, Make, n8n - and how no-code tools actually run workflows.

Day 5 - Build Your First Automation
Create a simple trigger-action workflow and connect two apps.

Day 6 - Data Handling
Pass data between steps, map fields, and work with text, numbers, and dates.

Day 7 - Logic & Error Handling
Add filters, conditional logic, retries, and fallbacks to keep automations reliable.

Day 8 - AI Model Basics
Learn prompts vs system instructions, tokens, limits, and LLM behavior.

Day 9 - Using AI Inside Automations
Insert AI steps into workflows and parse structured AI outputs.

Day 10 - Prompt Design for Automation
Write consistent prompts and reduce hallucinations with JSON outputs.

Day 11 - Text-Based Task Automation
Automate email replies, summaries, CRM updates, and document tasks.

Day 12 - Knowledge Automation (RAG Basics)
Connect AI to internal documents and fetch accurate answers from real data.

Day 13 - AI Agents Basics
Understand agent planning, tools, and identify use cases for agents.

Day 14 - Business Use Case Automation
Automate lead qualification, ticket routing, and internal processes.

Day 15 - Sales & Marketing Automation
Personalize outreach, repurpose content, and automate follow-ups.

Day 16 - Operations Automation
Manage approvals, notifications, and repetitive operational tasks.

Day 17 - Monitoring & Optimization
Track workflow success, cut costs, and improve performance.

Day 18 - Build & Ship Your System
Design, test, document, and finalize a complete end-to-end automation.

You don’t master AI automation by learning tools, you master it by learning systems thinking, data flow, and structured execution.

Follow this roadmap, and you’ll build automations that are reliable, scalable, and business-ready.

Data ➝ AI ➝ Value.
That’s still what companies think AI Looks like

But in the real world…

AI inside companies looks nothing like that.

Because value doesn’t come from “building a model”.

Value comes from building a working system around the model.

This guide explains what AI actually looks like in companies 👇

It’s a full production chain, not a single step:

1) Data
Selection, sourcing, synthesis - getting usable data is already 50% of the work.

2) Data Science
Exploration → cleaning → normalization → feature engineering → scaling
Then modeling: selection → training → evaluation → tuning

3) Value (Operationalizing)
Real value starts only after:
registration → deployment → monitoring → retraining

So ML is not “AI work”.

It’s data engineering + modeling + MLOps, all together.

What GenAI-enabled AI actually looks like

GenAI doesn’t remove complexity.

It shifts complexity from “models” to workflows + control.

The real GenAI path is:

- Understand the decision process
What decisions are being made today? Where does judgment happen?

- Workflow experimentation
Iterate with prompts, APIs, tools, retrieval, automation, coding — until it works reliably.

- Guardrails
Policies, safety filters, access control, cost limits, prompt rules, grounding.

- Monitoring
Track quality, drift, failure modes, user behavior, data leakage, hallucinations.

- Human in the loop
Because the highest-value workflows still need supervision, approvals, and feedback.

The takeaway?

AI isn’t a “feature.”

AI is an operating system change.

And companies that understand this early…

ship faster, safer, and with real ROI.

If you're building enterprise AI, stop chasing “Data → AI → Value”.

Start building: Workflows → Guardrails → Monitoring → Value.

#AI

Knowing RAG is easy. Measuring it is the real skill.

Everyone can plug in a vector DB and call it a RAG system.
But only a few can actually evaluate whether the system retrieves the right knowledge, avoids hallucinations, and answers with confidence.

That’s where retrieval metrics matter, they turn guesswork into engineering.

Here’s a simple breakdown of the 20 metrics that actually show whether your RAG system is good… or just lucky.

1. Precision@K - Checks how many retrieved chunks are truly relevant.
2. Recall@K - Measures whether all important chunks were found.
3. Hit Rate - Confirms if at least one correct chunk appears.
4. MRR - Rewards results where the correct chunk ranks high.
5. NDCG@K - Evaluates ranking quality, not just correctness.
6. Top-1 Accuracy - Tests whether the first chunk is the best one.
7. Query Coverage - Checks if retrieval answers the entire question.
8. Evidence Sufficiency - Asks: “Is this enough to answer fully?”
9. Context Relevance Score - Measures alignment between query meaning and chunk content.
10. Chunk Redundancy Rate - Tracks repeated or duplicate chunks.
11. Chunk Diversity Score - Shows whether results come from varied, reliable sources.
12. Source Coverage - Checks if retrieval pulls from the right documents.
13. Citation Accuracy - Confirms the final answer cites real, correct evidence.
14. Groundedness Score - Tests how strongly the answer stays rooted in retrieved context.
15. Faithfulness - Ensures the model doesn’t distort the evidence.
16. Hallucination Rate - Measures unsupported claims the model invents.
17. Retrieval Latency - How fast chunks reach the model.
18. Token Efficiency - How much useful context fits into the token limit.
19. Freshness - Checks if the retrieved info is up-to-date.
20. Retrieval Failure Rate - Tracks how often retrieval returns irrelevant or misleading chunks.

Anyone can build RAG.
Only skilled teams can measure retrieval, and that’s what decides whether your system scales or breaks.

Which metric do you think is the most underrated right now?

Single agents are powerful.
But multi-agent systems?
That’s where AI starts behaving like a real team.

As AI moves from “answering questions” to doing actual work, the real unlock isn’t a bigger model, it’s how multiple agents collaborate.

Different patterns fit different types of tasks, and choosing the right one can be the difference between a smart system and a chaotic one.

Here are 8 multi-agent collaboration patterns every builder should understand:

1. Manager–Worker Pattern
One agent breaks down the task, others execute the pieces.
Great for large, structured work like research + writing + QA.

2. Specialist Agents (Role-Based Team)
Each agent has a defined role - researcher, analyzer, writer.
Best when accuracy and expertise matter.

3. Planner–Executor Pattern
A planner decides the steps; an executor runs tools and actions.
Ideal for automation tasks, workflows, and system integrations.

4. Critic / Reviewer Agent
One agent generates, another fixes mistakes.
Useful for RAG responses, code checks, compliance-sensitive tasks.

5. Debate Pattern
Agents challenge each other with alternative solutions until the best idea wins.
Good for strategy, architecture, and trade-off decisions.

6. Voting / Consensus Pattern
Multiple agents propose answers, the system picks the best one.
Great for open-ended, ambiguous, or high-risk queries.

7. Swarm / Parallel Exploration
Many agents explore multiple sources at once.
Perfect for fast research and time-sensitive retrieval.

8. Tool Router / Dispatcher Pattern
A router agent decides which tool or agent should handle the request.
Works best in enterprise setups with many databases, APIs, and systems.

AI isn’t becoming powerful because models are getting bigger.
It’s becoming powerful because agents are learning to work together.

This is what turns AI experiments into real products.

Teams get excited about the model… 
but the model is only one slice of the stack.

Real AI products work because every layer around it is engineered with intent.

This breakdown shows what a modern, production-grade AI product stack actually looks like: 

1. Application Layer
The user-facing shell - authentication, UI, APIs, monitoring, analytics. This is where reliability, speed, and trust are felt long before AI runs.

2. Agentic AI Layer
The intelligence engine - reasoning, retrieval, tool use, orchestration, and memory. This layer decides whether your system simply answers… or actually acts.

3. GenAI Ops Layer
The guardrails - tracing, evaluation, experiments, and quality checks. This is how you keep an AI system stable as it learns and evolves in production.

4. Tools & Services Layer
The essentials - model APIs, data warehouses, and external integrations. These services give your AI the inputs, structure, and capabilities it needs to function.

5. Infrastructure Layer
The backbone - compute, storage, GPUs, networking, and security. If this layer is weak, nothing above it scales safely.

AI products don’t succeed because of one great model.
They succeed because every layer, from UI to GPUs - works together as one system.

RAG is not just “upload PDFs + chat with them.”

But that’s like calling every vehicle a “car.”

Because in real AI systems, there are 3 completely different architectures hiding behind the same word:

Naive RAG vs Agentic RAG vs Agentic Memory.

And if you don’t know the difference, you’ll build the wrong system… and blame the LLM later.

Here’s what’s actually happening : 

1. Naive RAG (Basic Retrieval)
Naive RAG is the most common setup: documents get indexed once, stored in a vector database, and whenever a user asks a question, the system fetches the closest chunks and sends them to the LLM. It works well for simple document Q&A, but the accuracy completely depends on retrieval quality  - if the wrong chunk is retrieved, hallucinations become inevitable.

2. Agentic RAG (Tool-based Retrieval + Reasoning)
Agentic RAG is not a fixed pipeline - it’s a decision-making system. Instead of blindly retrieving from a single database, an agent dynamically decides which sources to search, which tools to use, whether the answer needs verification, and when to retry retrieval. This is what enables enterprise-grade search across documents, CRMs, knowledge bases, and even the web - but it also introduces more complexity and cost.

3. Agentic Memory (Long-term Learning Loop)
Agentic Memory is not about pulling knowledge from documents - it’s about building continuity. The system stores useful signals from past interactions (preferences, decisions, repeated tasks, user context) and retrieves them later to give better responses over time. This turns the assistant into something that evolves with usage but if memory isn’t governed properly, it can quickly become noisy, irrelevant, or even risky.

The easiest way to remember it:

Naive RAG = answers from stored docs
Agentic RAG = answers using tools + multiple sources
Agentic Memory = answers that improve over time

If you’re building agents in 2026…
understanding this difference is not optional, it’s architecture.

I'm Ravit Jain — the Founder & Host of The Ravit Show, a platform dedicated to meaningful conversations with the people shaping the future of Data and AI.

What started as a passion for learning from industry leaders has grown into a global media platform that reaches millions. Today, I spend my time interviewing experts, covering major tech events, and working closely with companies to help them share their stories in a way that truly connects with the community.

Over the past few years, I’ve had the chance to:

Host 550+ interviews with data and AI leaders, founders, engineers, and product thinkers

Collaborate with 200+ companies — from fast-moving startups to nearly every major tech giant

Cover 100+ conferences across the world, sharing real-time insights and backstage conversations

Support 200+ partner campaigns to amplify their product launches, webinars, reports, and community efforts

My focus is always on creating content that is useful, grounded, and easy to understand — no hype, just real value. Whether it's discussing GenAI, data quality, modern architecture, or what it takes to scale AI responsibly, my goal is to keep the conversation practical and community-first.

If you’re building in this space or want to share what you’re working on, I’d love to connect and learn more.

You can reach out to me at Ravitjain0909@gmail.com for The Ravit Show queries :)

Ravit Jain

Most AI projects do not fail because of the model.

They fail because the data foundation is weak.

That is why I spent time with the ebook Building a Data and AI Platform with PostgreSQL by EDB.  You can get the digital copy here: https://lnkd.in/dKA9S3rq

What stood out to me is the way it treats data, AI, and PostgreSQL as one platform, not three disconnected initiatives that struggle to work together.

The ebook breaks things down in a very clear and practical way:

Why AI needs far more data than traditional applications

What happens when that data is spread across teams and tools

How PostgreSQL can sit at the core of an AI and analytics platform

It stays grounded. You see real patterns for GenAI and RAG, examples built on Postgres, and honest guidance on handling technical debt and data silos.

If you are serious about building AI on PostgreSQL while keeping control over your data, this is worth reading.

#Sponsored #PostgresAIPlatform #EDBPostgresAI #SovereignAI #DigitalSovereignty #PostgreSQL

🚨Breaking: Big announcements by MongoDB. I just spoke to Pete Johnson, Field CTO for AI at MongoDB on The Ravit Show. This conversation cuts through the noise around AI and focuses on what actually works in production!!!!

We get into:

- What the Field CTO for AI role really looks like in the field
- How MongoDB thinks about the data and AI intersection
- Which recent MongoDB announcements actually matter
- What enterprises should prioritize as they head into 2026
- Clear advice for developers, data, and DevOps teams building with AI

No hype. No buzzwords. Just practical insight from someone working directly with customers every day.

If you are building, scaling, or planning AI beyond pilots, this episode is for you.

Thank you MongoDB for sponsoring this post!

#data #ai #mongodb #databases #mongodbpartner #agents #agenticai #theravitshow

Most mainframe challenges today are not caused by broken systems.
They are caused by disappearing knowledge.
Senior experts are retiring.
Documentation is incomplete or outdated.

And newer team members are expected to operate some of the most critical systems in the enterprise with very little context.

In Part 1 of this podcast, we focus on where the AI journey really begins.
- Why mainframe teams are under pressure right now.
- What types of institutional knowledge are most at risk.
- And how AI changes the way that knowledge is captured, shared, and used.

Liat Sokolov, BMC Software walks through why AI is showing up at this moment and why traditional approaches have not been enough.

Anthony DiStauro, BMC Software explains how AI helps close the skills gap by shortening learning curves and guiding teams through complex systems with more confidence.

This episode is not about autonomy yet.

It is about preserving what matters and helping people work better with the systems they already depend on.

Part 1 is live now.

Link in comments for full YouTube video.

#data #ai #mainframe #agents #bmi #mainframe #skills #bmc #theravitshow

Most enterprises I speak with are not trying to replace Oracle.
They are trying to make Oracle work in a modern data stack.

That is exactly why I am excited about this one.

Oracle still sits at the core of critical enterprise systems. But getting that data out reliably, at scale, and into cloud platforms for analytics and AI is where teams struggle the most. I hear this problem repeatedly from CIOs, data leaders, and platform teams.

This session on modernizing Oracle data integration with CData Sync hits a very real enterprise pain point. Check out and register here -- https://lnkd.in/dVujQiyP

What stood out to me is the focus on doing this without adding more complexity. No heavy engineering effort. No fragile pipelines. Just reliable, continuous data movement that works across hybrid and cloud environments.

For enterprises, this matters because Oracle data is not going away. But expectations around speed, freshness, governance, and AI readiness have changed.

If your Oracle data still lives in silos or takes days to reach analytics teams, this is a must attend. This is about making Oracle data usable again for modern decision making.

Highly recommend this for enterprise data and analytics leaders who want fewer workarounds and more impact.

#data #ai #oracle #pipelines #enterprise #agents #agentic #cdata #theravitshow

At Microsoft Ignite, I finally got to interview the man himself, Arun Ulag, President, Azure Data, Microsoft, on The Ravit Show. He is one of the best in the data and analytics space, and meeting him in person was so good.

This was not a surface-level chat. We went straight into the big announcements and what they unlock for customers:

 • The real takeaways from his keynote and how they help enterprises move faster with data and AI
 • What Fabric IQ actually is, how it works behind the scenes, and why it is a big deal
 • How customers can start seeing value from Fabric IQ from day one, not after months of setup
 • What makes Azure HorizonDB for PostgreSQL different and where it fits for modern workloads
 • What is coming next for data services in the era of AI
 • How leaders should think about the next 4 to 5 months with all these updates landing at once

If you are trying to understand where the Microsoft data and AI stack is really heading, this conversation will give you an obvious picture!!!!

Link to the complete interview in the comments ⬇️

Most people are still asking what an LLM is. 

Techies in 2026 will be asking which one to use and why.

The stack is getting clearer.

You have platforms that power reasoning and agents.
You have open source models that give you control.
You have tools for memory, search, and context.
You have frameworks to connect everything.
And you have no code options that remove friction completely.

This is no longer about experimenting.
This is about building real products, faster and smarter.

If you are in tech and not tracking this shift, you are already late.

Save this.
You will come back to it.

LLM Tools in 2026

READY for DataDriven 2026!!!! Last year at the DataDriven Conference, I had a conversation with Manish Sood, Founder and CEO of Reltio. We spoke about a simple but critical truth. AI does not fail because of ambition. It fails because data is fragmented, inconsistent, and hard to trust.

Our discussion covered:

- The shift enterprises must make to get real value from AI
- Why fragmented data is still the biggest blocker
- What a unified system of record actually enables in real use cases
- How enterprise data management will evolve over the next few years

That conversation has stayed with me because the problem is even more real today.

This is why I am excited to be heading back to DataDriven 2026.

We are now in the age of agentic AI. Systems that can act and improve on their own are becoming real. But they only work when the data foundation is strong, trusted, and connected. 

DataDriven is one of the few conferences that stays grounded in this reality and focuses on execution, not hype.

This year, the event brings together data and AI leaders who are actually building and running these systems inside large enterprises. The focus is clear. How to unify data. How to govern it. How to make it usable for AI at scale.

Event details:

Feb 23–26, 2026

Marriott Orlando World Center, Orlando

Register here: https://lnkd.in/dzjf2BCr

Use code JAIN for $100 off registration

Looking forward to the conversations, the lessons, and reconnecting with people who are shaping how enterprise AI really works.


#data #ai #datadriven #reltio #theravitshow

If you are aspiring to build a serious career in AI in 2026, learning tools is not enough. You need skills that actually compound over time.

Most people focus on prompts or the latest model. That helps you get started. It does not help you stand out.

The biggest shift I am seeing is this. AI roles are no longer about using one tool well. They are about understanding the full system around AI.

That is why I put together a breakdown of the top 15 AI skills almost everyone must know in 2026.

These are not hype skills. These are the skills teams quietly expect you to have.

A few that matter more than people realize:

- AI literacy
You must understand how models think, where they fail, and why hallucinations happen. Without this, everything else breaks.

- Context engineering
Great outputs do not come from clever prompts. They come from feeding the right context, instructions, memory, and examples before the model responds.

- Prompt chaining and workflows
Real work is never one prompt. It is plan, draft, improve, validate, and ship. This is how AI becomes useful at scale.

- AI research and fact checking
Using AI like a consultant matters more than generating text. Sources, comparisons, and insights are the real value.

- AI agents and automation
Delegating tasks to AI requires structure, guardrails, and evaluation. Otherwise agents become expensive demos.

- Evaluation and safety
The most underrated skill. If you cannot measure quality, consistency, cost, and failure modes, you are guessing instead of engineering.

- The key thing to understand is this.
In 2026, strong AI professionals are not judged by outputs. They are judged by reliability, repeatability, and real outcomes.

If you are planning to upskill this year, focus less on tricks and more on foundations.

This is where long term AI careers are being built.

Join The Ravit Show Newsletter - - https://lnkd.in/dCpqgbSN

#data #ai #agents #agentic #enterprises #models #a2a #memory #theravitshow

Top AI Skills Almost Everyone Must Know in 2026

Most people are mixing these up. Agent. AI Agent. Agentic AI. They are not the same thing.

An Agent follows rules.

If this happens, do that.

No thinking. No learning. Just execution.

An AI Agent can think within limits. It understands context. It can use tools.
But it still needs guidance.

Agentic AI is different.
It has goals.
It plans.
It acts.
It learns from feedback and improves over time.

This shift matters.

Because most teams are still building rule-based automation
While the world is moving toward systems that can run, adapt, and improve on their own.

If you are designing products, workflows, or data platforms in 2026
You need to be clear about which one you are actually building.

Not everything needs Agentic AI.
But pretending they are all the same is a mistake.

Save this.
Share it with your team.

Breaking announcement - IBM has just shared an important update around its Partner Plus program, and this one really stood out to me.

I’ve been involved in the IBM partner ecosystem for years. I’ve judged the Partner Plus event, attended every IBM Think, and one of my favorite parts of those events is always meeting partners. Builders, operators, system integrators, ISVs. People doing the real work of turning strategy into outcomes.

Great announcement by my friend, Kareem Yusuf Ph.D, Senior Vice President, Ecosystem, Strategic Partners & Initiatives, IBM. Check out the details here – https://lnkd.in/dSxeSS8B

This announcement is important because it doubles down on something that actually works: a strong, aligned ecosystem.

IBM is accelerating how it supports partners by making enhancements that encourage growth, speed, and execution. The focus is clear.
- Helping partners move faster
- Improving partner economics
- Reducing friction in how deals are built and closed
- Making AI real for enterprises, not theoretical

What stands out most is that this is not just about adding more programs or checking boxes. It’s about alignment. Between IBM teams and partners. Between technology and go-to- market. Between AI ambition and business reality.

When ecosystems work, customers win. Partners grow. Innovation scales.
I’ve seen the energy in this community up close, at Think and beyond. 

This update feels like a natural next step in strengthening that momentum.
Looking forward to seeing how partners take this forward.

#data #ai #ibmpartners IBM Partner Plus IBM Consulting IBM Data, AI & Automation #ibmpartnerplus #ibm #isv #agents #theravitshow

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