Ex Meta | Data-Driven | Mentor

I’m a global GTM leader turned AI operator. I've spent the last decade helping companies like Meta, the BBC, and early-stage startups scale through brand storytelling, strategic partnerships, and product-led growth.

Today, I’m the Head of GTM & Chief of Staff at Ruli, a legal AI startup I helped build from the ground up. I wear many hats, from shaping the GTM strategy to working directly with customers and engineers to make the product better every week.

Outside of work, I serve on the board of a community foundation and write a weekly AI newsletter called Mama Bear, where I explore the intersection of tech, work, and humanity in plain English.

What drives me:

I believe technology should be in service of people, not the other way around. I’m excited about tools that make us more curious, capable, and connected. Whether it’s AI, workforce training, or platform design, I’m drawn to ideas that bridge the gap between human potential and real-world impact.

Rouge Y.

Data Analytics - Turning Coffee into Insights, One Caffeine-Fueled Query at a Time! | Healthcare Data | Financial Expert | Driving Business Growth | Data Science Consultant | Data Strategy

As a data analytics professional, I have a passion for turning raw data into meaningful insights that drive business decisions. With my experience in data visualization, statistical analysis, and data modeling, I can help your organization harness the power of data to optimize operations, improve customer experiences, and achieve your strategic goals.

🔍 Discovering Insights

My expertise lies in exploring complex data sets, uncovering hidden trends and patterns, and providing actionable insights that support informed decision-making. I can help you answer questions like "What are our customer's buying habits?" or "What factors are impacting our sales performance?", and use data to guide your business strategy.

🎨 Data Storytelling

I believe that data storytelling is an art form, and I love using creative and engaging data visualizations to communicate complex information in a way that's easy to understand. Whether it's through interactive dashboards or custom reports, I can help you see your data in a new light, and use it to tell the story of your business.

🧠 Continuous Learning

I'm a lifelong learner, and I'm always exploring new data tools and techniques to stay at the forefront of the industry. Whether it's machine learning, artificial intelligence, or predictive analytics, I'm excited to apply the latest advancements to help you gain a competitive edge.

🤝 Let's Connect

If you're looking for a data analytics professional who can help you unlock the power of your data, let's connect! Whether you're a startup or a large corporation, I'm excited to collaborate with you and help you achieve your business goals. Feel free to reach out at Ravenaravs@gmail.com

Ravena O

Still confused between LLM, RAG, AI Agents, and MCP?
You’re not alone — most teams mix these up and design the wrong systems.

This visual breaks it down clearly ⬇️
Here’s the mental model that actually works:

🟡 LLM (Large Language Model)
• Pure reasoning + text generation
• No external knowledge beyond training
• Best for summarisation, Q&A, ideation

🟡 RAG (Retrieval-Augmented Generation)
• LLM + external context (docs, DBs, PDFs)
• Reduces hallucinations
• Ideal for enterprise knowledge, internal search

🟡 AI Agents
• LLM + planning + memory + tools
• Can take actions, not just respond
• Used for workflows, automation, copilots

🟡 MCP (Model Context Protocol)
• Standard way for models to talk to tools & data
• One unified interface → many systems (GitHub, Slack, files)
• Critical for scalable, tool-driven agent ecosystems

👉 Think of it like this:
• LLM = Brain
• RAG = Brain + Books
• Agent = Brain + Hands
• MCP = Nervous System connecting everything

Why this matters

• Most AI failures are architecture mistakes, not model issues
• Agents without MCP don’t scale
• RAG without governance breaks fast

Learn more (high-quality resources):

• RAG explained: https://lnkd.in/dUfV33DE
• AI Agents overview: https://lnkd.in/dE9A5G29
• MCP (Model Context Protocol): https://lnkd.in/dNMmQnSf
• Agent design patterns: https://lnkd.in/df4Yeuug

💡 If you’re building GenAI systems in 2026, this stack isn’t optional — it’s foundational.

What are you building right now: RAG, Agents, or full MCP-based systems? 👇

Ravena  O

Small Language Models are the future of AI Agents
—but only if you know how to use them correctly.

Gartner predicts that by 2027, organizations will use small, task-specific AI models 3× more than general-purpose LLMs.

NVIDIA confirms it clearly:
“Small Language Models are the future of AI Agents.”

So what actually makes SLMs so powerful for agentic systems?

I put together a simple handbook to make this easy to understand.

Here’s a quick breakdown.


What are SLMs?

Transformer-based neural networks with fewer parameters (millions to low billions) that prioritize efficiency over broad generalization.

They deliver:
🟠 Faster inference
🟠 Lower memory usage
🟠 Easier deployment in production

🟠 How are SLMs created?

Using three core techniques:

Quantization – Reduces bit precision (32-bit → 8-bit) while preserving accuracy
🟠 Pruning – Removes less important parameters to shrink model size
🟠 Distillation – Transfers knowledge from large “teacher” models to compact “student” models


🟠 4 strategies to maximize SLMs in AI agents

🟠 Intelligent Routing – Simple tasks go to SLMs, complex ones to LLMs
🟠 Pipeline Collaboration – SLMs preprocess, LLMs refine outputs
🟠 Parallel Verification – SLMs draft quickly while LLMs verify
🟠 Conditional Activation – Escalate to LLMs only when confidence is low


🟠 Real-world implementations

🟠 Uber – SLMs handle query preprocessing and answer validation in Agentic RAG
🟠 OpenAI – SLMs power guardrails, relevance checks, and intent classification
🟠 Microsoft – SLMs improve cloud supply chain fulfillment accuracy

🟠 Why enterprises are adopting SLMs

🟠 Privacy-first – On-prem deployment for healthcare triage and legal reviews
🟠 Cost-efficient – 50,000+ receipts/day processed at ~1% of LLM cost
🟠 Hyper-specialized – Fine-tuned models for internal coding and support routing

🟠 Case studies

🟠 Uber: https://lnkd.in/geNJNhvK
🟠 OpenAI: https://lnkd.in/gD5-v58d
🟠 Microsoft: https://lnkd.in/gZ5hGuNy

If this post helped you rethink Agentic AI,
🟠 Like
🟠 Share
🟠 Or comment how you’re using SLMs in production

CC: Rakesh Goel

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