Language Models Tuned
to Your Business
We fine-tune, deploy, and integrate large language models on your proprietary data, enabling intelligent automation, contextual reasoning, and enterprise-grade reliability at scale.
End-to-End LLM Engineering for Enterprise
From fine-tuning proprietary models to building production RAG pipelines, we handle every layer of the LLM stack, designed for accuracy, speed, and scale.
We adapt foundation models to your domain using your internal datasets, producing models that understand your business terminology, tone, and logic without hallucination.
We build RAG systems that ground LLM outputs in your verified knowledge base, delivering accurate, citation-backed responses from live enterprise data sources.
We engineer multi-step LLM agent workflows using LangChain and similar orchestration frameworks, enabling autonomous task execution across connected systems and APIs.
We deploy fine-tuned models with optimized inference, token efficiency, and latency controls, ensuring consistent performance under high-volume enterprise workloads.
We deploy LLMs within your own cloud infrastructure, ensuring sensitive data never leaves your environment while maintaining full control over model behavior and outputs.
We establish robust evaluation frameworks for LLM output quality, including BLEU, ROUGE, and task-specific benchmarks, with pipelines for ongoing model improvement over time.
From Raw Data to Intelligent Language Systems
Building a production LLM system is more than selecting a base model. We engineer the full pipeline: data curation and preprocessing, fine-tuning with RLHF or supervised instruction tuning, vector database integration for retrieval, and optimized inference with monitoring in place.
Every system we deliver is designed for the specific demands of your use case: whether that's legal document review, customer support automation, internal knowledge retrieval, or complex multi-step reasoning tasks.
We fine-tuned and deployed a large language model on UCLA's institutional knowledge base, enabling their student services team to automate responses to common academic queries, policy questions, and enrollment support requests. The system handled a high percentage of incoming student support volume without requiring staff escalation.
LLM Projects That Moved the Needle
Real deployments across legal, consulting, financial advisory, marketing, and software where LLM-powered systems replaced slow manual processes with accurate, fast, and consistent outputs.
The Knowledge That Lived in One Person's Head: How a Consulting Firm Made It Accessible to Everyone
A small US consulting firm was dangerously dependent on one senior consultant who held almost all institutional knowledge. When she was unavailable, junior staff could not answer client questions confidently. We built a RAG system trained on five years of her documented processes, proposals, and client communications that junior staff could query in plain English.
The Compliance Document Nobody Could Find: How a Financial Advisory Firm Cut Search Time by 92%
Staff at a small US financial advisory firm were spending 20 to 30 minutes per query searching an unorganized shared drive for compliance documents and regulatory guidance. We built a RAG-powered search tool that answered compliance questions in plain English, cited the exact source document and version number, and flagged conflicting versions.
The Slow Proposal Process: How a Marketing Agency Went From 5 Hours to 30 Minutes Per Draft
A US marketing agency was writing every new business proposal from scratch despite most proposals sharing 60 percent of their content. We fine-tuned a lightweight LLM on their best-performing historical proposals and built a simple interface where account managers input a client brief and receive a structured first draft within minutes.
The Chatbot That Actually Knew the Product: Replacing a Generic Bot With a Custom LLM
A US software company's generic website chatbot was frustrating visitors by failing to answer specific product questions and redirecting them to documentation pages instead. We replaced it with a custom LLM trained on their product documentation, FAQs, and support history, with a confidence threshold that escalated edge cases to a human agent with full context attached.
The Contract Review That Never Ended: How a Law Firm Cut Review Time From 3.5 Hours to 45 Minutes
A small US law firm was spending 3 to 4 hours per commercial contract review as junior associates read every word searching for risk clauses, liability caps, and missing terms. We built a fine-tuned LLM trained on the firm's past reviewed contracts and senior attorney annotations that flagged high-risk sections automatically, letting associates focus on flagged areas only.