RAG Experts Company | About LLM Labs

Our Story

From aerospace challenge to enterprise RAG leader

Founded in 2021, LLM Labs emerged from a simple observation: enterprises were drowning in their own data. While large language models were making headlines, they couldn't reliably answer questions about proprietary information. The gap between public AI capabilities and enterprise needs was massive.

The Founders' Insight: Our founding team—veterans of Google Brain, OpenAI research, and Fortune 500 AI implementations—recognized that Retrieval-Augmented Generation wasn't just an academic concept. It was the missing link for enterprise AI.

Early Days (2021-2022): Started with a single client: Aerospace manufacturer with 60 years of design archives. Built our first RAG pipeline: 500TB of rocket schematics. Result: 80% reduction in research time, $2.3M annual savings. Lesson learned: RAG wasn't just about technology—it was about transforming how organizations access knowledge.

Rapid Growth (2023): Word spread: 3 clients became 15. Expanded beyond aerospace: Financial services, healthcare, legal. Key milestone: Stanford partnership began, establishing us as thought leaders. Published first open-source RAG framework (5,000+ GitHub stars).

Present Day (2024-2025): 50+ enterprise deployments. Team grew from 4 to 28 people. $500M+ in client value creation. Industry recognition: "Top 10 Enterprise AI Companies" by [Industry Publication].

What Makes Us Different: We don't chase every AI trend. While others pivot from blockchain to crypto to generative AI, we've maintained laser focus on RAG since day one. This depth of expertise—building hundreds of pipelines, solving thousands of edge cases—is what makes us the RAG experts enterprises trust.

50+

Enterprise Deployments

$500M+

Client Value Created

Team Members

Peer-Reviewed Publications

Meet the Experts Who Build Your RAG Systems

World-class experts in AI, machine learning, and enterprise systems

Mamoon Zafar Babar

Co-Founder, Business Development & AI Engineer

Background: BS Computer Science, focus on Machine Learning. Previously: AI consultant for 3 Fortune 500 companies.

Expertise: Client acquisition, RAG architecture, LangChain implementations. Bridges business needs and technical solutions.

Notable Projects: Led $2M aerospace RAG deployment, designed hybrid RAG architecture used in 15+ client systems.

Speaking: Keynote: "RAG for Enterprise Scale" - AI Summit 2024

Contact: mamoon@llm-labs.io | LinkedIn

Kamran

Co-Founder, Agentic AI & RAG Expert

Background: MS Artificial Intelligence, PhD candidate (ABD). 10+ years in AI/ML, last 4 focused exclusively on agentic systems.

Expertise: Agentic RAG architectures, reinforcement learning for retrieval optimization, large-scale vector database optimization.

Notable Projects: Built autonomous research agent for pharma client ($50M drug discovery impact), created proprietary agent evaluation framework.

Publications: First author: "Learned Retrieval Strategies" (ICML 2024), 8 papers on agentic AI and RAG.

Contact: kamran@llm-labs.io | LinkedIn | Google Scholar

Ashar Naeem

AI Consultant & Strategy Lead

Background: MBA + MS Computer Science. 15 years consulting (McKinsey, then boutique AI firms).

Expertise: Enterprise AI adoption and change management, ROI modeling and business case development.

Notable Projects: Guided 3 Fortune 100 companies from pilot to enterprise-wide RAG (5,000+ users each).

Insights: "80% of RAG projects fail not due to technology, but change management."

Writing: Author: "The Enterprise RAG Playbook" (Amazon #1 in AI category).

Contact: ashar@llm-labs.io | LinkedIn

Nasir Khan

Explainable AI Expert & Research Lead

Background: PhD Computer Science (Explainable AI thesis). Post-doc at [Top Research University].

Expertise: Explainable AI (SHAP, LIME, attention visualization), RAG interpretability and debugging.

Notable Projects: Built explainability layer for healthcare RAG (FDA audit-ready), created "RAG Debugger" tool.

Publications: 12 peer-reviewed papers on explainable AI, co-authored: "Explainable RAG Systems" (best paper, ACL 2024).

Philosophy: "If you can't explain why your AI gave an answer, you can't trust it."

Contact: nasir@llm-labs.io | LinkedIn | Google Scholar

Extended Team

🔧 Engineering Team (6 people)

Senior RAG Engineers (3): Building production pipelines
ML Engineers (2): Model fine-tuning, optimization
DevOps Engineer (1): Infrastructure, deployment

📊 Data Science Team (3 people)

Data Scientists (2): Evaluation, experimentation
Data Engineer (1): ETL, data preparation

📱 Product & Operations (3 people)

Product Manager: Roadmap, client success
Project Managers (2): Implementation coordination

Research & Innovation

Stanford Partnership and Cutting-Edge RAG Research

🎓 Stanford Research Partnership

What We're Building Together: Since 2023, LLM Labs has partnered with Stanford's AI Lab on groundbreaking RAG research. This isn't a marketing partnership—it's active collaboration with joint publications, shared code, and real scientific contribution.

1. Agentic RAG Framework (2024)

Challenge: How do agents decide what to retrieve and when?

Solution: ReAct-style framework with learned retrieval strategies

Impact: 40% improvement in multi-hop question accuracy

Published: "Learned Retrieval Strategies for Agentic Systems" (ICML 2024)

Open Source: GitHub link - 8,000+ stars

2. Hybrid Retrieval Optimization (2023-2024)

Challenge: When to use dense vs sparse vs graph retrieval?

Solution: Meta-learning approach that selects optimal strategy per query

Impact: 25% latency reduction, 15% accuracy improvement

Published: "Dynamic Retrieval Strategy Selection" (NeurIPS 2024)

Deployed: Now used in 30+ client systems

3. Explainable RAG (Ongoing)

Challenge: Black box retrieval makes debugging impossible

Solution: Attention-based explanations showing why documents were retrieved

Impact: Enables rapid pipeline improvement, builds user trust

Status: Paper under review, prototype in 5 client pilots

🔬 Other Research Contributions

Multi-Modal RAG (Internal R&D)

Extending RAG to images, diagrams, videos. Challenge: How to retrieve across modalities? Current: 78% accuracy on technical diagram retrieval. Target: 90% by Q4 2025.

Cost-Optimized RAG (Industry collaboration)

Working with AWS, Anthropic on reducing query costs. Techniques: Semantic caching, query rewriting, model cascading. Achievement: 60% cost reduction vs naive implementation.

Real-Time RAG (Pilot phase)

Integrating streaming data (Kafka) with vector retrieval. Use case: Financial news + market data for trading insights. Latency: <500ms for real-time queries.

📚 Publications & Talks

5 peer-reviewed papers (ICML, NeurIPS, ACL)
12 industry talks (AI Summit, RAG Conference, EmergeTech)
3 open-source frameworks (15,000+ combined GitHub stars)
25+ blog posts teaching RAG implementation

🏆 Awards & Recognition

2024

"Top 10 Enterprise AI Companies" - [Industry Publication]
"Best RAG Implementation" - AI Excellence Awards

2023

"Emerging AI Startup to Watch" - VentureBeat
"Best Paper" - ACL Conference

2022

"AI Breakthrough of the Year" - [Industry Association]
"Most Promising AI Startup" - TechCrunch Disrupt

Our Values

The principles that guide everything we do

🎯

Results-Driven

We focus on measurable business outcomes, not technical complexity. Every solution must deliver clear ROI and solve real business problems.

🔍

Technical Excellence

We pursue cutting-edge innovation while ensuring practical implementation. Our team combines deep research expertise with enterprise experience.

🤝

Client Partnership

We succeed when our clients succeed. Long-term partnerships built on trust, transparency, and shared goals are the foundation of our business.

🔒

Security First

Enterprise security and compliance are non-negotiable. We build solutions that meet the most stringent requirements while maintaining usability.

📖

Continuous Learning

The AI landscape evolves rapidly. We commit to continuous learning and adaptation to ensure our clients always have access to the best solutions.

🌍

Responsible AI

We believe in building AI that is ethical, transparent, and beneficial to society. Responsible AI practices are integrated into everything we build.

Built by RAG Experts, For RAG Excellence