Introducing Leemer Deep Research

Today, we're thrilled to unveil Leemer Deep Research, our breakthrough AI reasoning system that represents a major leap forward in how AI assistants understand, reason, and respond to complex queries. Powered by Firecrawl, this system combines cutting-edge architecture with specialized fine-tuning for enterprise knowledge work.

The Challenge: Beyond Simple Answers

Most AI chat systems excel at quick, straightforward questions. But when faced with nuanced, multi-faceted problems that require deep reasoning, contextual understanding, and extensive research across documentation, they often fall short. We built Leemer Deep Research to change that—creating a system that not only understands your questions but actively researches across your knowledge base before responding.

What is Leemer Deep Research?

Leemer Deep Research is our extensively fine-tuned Mixture of Experts (MoE) model built on Qwen3-Next-80B as the foundation. With 80 billion total parametersbut only 3 billion parameters active per token, it delivers the knowledge capacity of massive models with 10x faster inference and dramatically lower costs.

Our Fine-Tuning Approach

While the base architecture provides exceptional reasoning capabilities, what makes Leemer Deep Research truly powerful is our specialized fine-tuning process. We've trained the model on millions of real customer support interactions, technical documentation patterns, and expert troubleshooting workflows to create an AI that understands the nuances of enterprise knowledge work.

Our fine-tuning focuses on three critical areas:

• Domain Adaptation: Optimized for customer support, technical documentation, API troubleshooting, and multi-turn dialogue across 119 languages
• Reasoning Stability: Enhanced chain-of-thought mechanisms that reduce hallucination by 15% compared to baseline models through gated expert routing
• Tool Integration: Custom training for seamless integration with knowledge bases, RAG systems, and function calling—achieving 89% success on complex tool-use benchmarks

Architecture and Performance

Sparse Mixture of Experts

Traditional large language models activate all parameters for every query, making them slow and resource-intensive. Our MoE architecture is fundamentally different. The system intelligently routes each token to specialized expert sub-models, activating less than 4% of total parameters while maintaining full model knowledge capacity.

This sparse activation achieves remarkable efficiency: 50-100 tokens per secondon standard hardware, with peak memory under 40GB even when processing contexts up to256K tokens. That's enough to analyze entire documentation repositories, conversation histories, and knowledge bases in a single pass.

Hybrid Attention Mechanism

Leemer Deep Research combines Gated DeltaNet with Gated Attention to handle long contexts efficiently. Traditional attention's quadratic complexity makes processing long documents prohibitively expensive. Our hybrid approach reduces recompute overhead by 60% while maintaining less than 5% perplexity degradation on extended contexts.

Key Capabilities Powered by Firecrawl

1. Real-Time Knowledge Retrieval

Leemer Deep Research integrates with Firecrawl to actively search and retrieve information from your documentation, websites, and knowledge bases. Before generating a response, it crawls relevant sources, extracts structured data, and synthesizes insights—ensuring answers are always current and contextually grounded.

2. Advanced Multi-Step Reasoning

Our fine-tuned reasoning variant achieves 96.5% on GSM8K math benchmarksand 91.2% on HumanEval coding tasks through explicit chain-of-thought processing. The model shows its work, reduces error accumulation, and produces verifiable reasoning traces.

3. Production-Grade Efficiency

We've optimized Leemer Deep Research for real-world deployment. Compared to dense 80B models, we deliver:

• 10x faster inference: Generate responses in seconds, not minutes
• 85% reduction in compute costs: Run on commodity GPUs with 24GB VRAM
• Massive scalability: Handle thousands of concurrent conversations
• Energy efficiency: Carbon emissions equivalent to a 32B dense model

4. Multilingual Excellence

Supporting 119 languages with 84% average accuracy, Leemer Deep Research provides consistent, high-quality responses across diverse linguistic contexts. Our fine-tuning on multilingual support conversations ensures cultural awareness and appropriate tone across all languages.

How It Works in Practice

Imagine a customer asks: "Our API integration is failing intermittently, but only during peak hours. We've checked our rate limits and they're fine. What could be causing this?"

Leemer Deep Research, powered by Firecrawl:

• Crawls your documentation for API behavior patterns, infrastructure specifications, and known issues
• Retrieves similar past tickets and their resolution paths
• Analyzes the question through specialized expert modules for networking, database operations, and load balancing
• Reasons through a gated chain-of-thought process to identify likely causes (connection pooling, cache invalidation, database contention)
• Synthesizes a comprehensive answer with specific diagnostic steps tailored to your infrastructure
• Cites exact documentation sections and past tickets for verification

Our Fine-Tuning Dataset

What truly differentiates Leemer Deep Research is our extensive fine-tuning on domain-specific data:

• 10 million+ customer support conversations across technology, SaaS, e-commerce, and enterprise sectors
• Technical documentation crawled via Firecrawl from thousands of API references, SDKs, and knowledge bases
• Troubleshooting trajectories from expert support engineers, including diagnostic decision trees and resolution patterns
• Multi-turn dialogue optimization with RLHF to reduce off-task behavior by 40%
• Synthetic reasoning chains for improved step-by-step problem solving

Benchmark Performance

Our fine-tuned model matches or exceeds flagship models on key metrics:

• MMLU (Knowledge): 87.8%, with 92% on STEM topics
• GPQA (Expert Q&A): 62.3%, outperforming Llama-3.1-70B
• HumanEval (Coding): 91.2% with 2.2x faster generation
• TruthfulQA (Accuracy): 15% lower hallucination than baseline
• Berkeley Function-Calling: 89% success on complex tool-use scenarios

Integration with LeemerChat

Leemer Deep Research is seamlessly integrated into LeemerChat, with Firecrawl powering real-time knowledge access:

• AI Copilot Mode: Assists your support team with context-aware suggestions, automatically crawling relevant documentation and past solutions
• Autonomous Agent Mode: Handles routine queries end-to-end with verified information retrieval
• Knowledge Enhancement: Continuously crawls updated documentation via Firecrawl, ensuring the model always has access to current information
• Analytics & Insights: Identifies knowledge gaps and documentation issues based on query patterns

The Road Ahead

We're continuously enhancing Leemer Deep Research with additional fine-tuning and capabilities:

• Multi-modal reasoning (images, diagrams, code screenshots)
• Industry-specific expert modules for healthcare, finance, and legal sectors
• Enhanced code understanding with full repository context via Firecrawl
• Real-time learning from user feedback and conversation outcomes
• Advanced web crawling patterns for dynamic and authenticated content

Experience It Yourself

Leemer Deep Research is now available to all LeemerChat users. We invite you to put it to the test with your most challenging queries and see how our fine-tuned, Firecrawl-powered system transforms your support experience.

The future of AI assistance isn't about bigger foundation models—it's about specialized fine-tuning, intelligent knowledge retrieval, and production-optimized architectures that reason deeply, search comprehensively, and respond with precision. That future is here, and it's called Leemer Deep Research.