LLM
Solutions
Fine-tune, evaluate, and deploy large language models that meet your exact performance, safety, and business requirements.
What We Offer
We build and optimize LLM-powered systems across the full stack, from SFT and RLHF pipelines to production AI assistants and knowledge retrieval systems. Our LLM work is grounded in rigorous evaluation and measurable performance benchmarks.
LLM Fine-Tuning (SFT)
Supervised fine-tuning of open-source LLMs (Mistral, LLaMA, Qwen, Phi) for domain-specific tasks and instruction following.
Prompt Engineering
Systematic prompt design, structured prompting, multi-step reasoning workflow design, few-shot tuning, and prompt evaluation frameworks.
RLHF
Reinforcement Learning from Human Feedback, preference data collection, reward model training, and PPO/DPO alignment.
LLM Evaluation
Benchmark evaluation, human preference evaluation, safety red-teaming, and custom evaluation harness design.
AI Assistant Workflows
RAG pipelines, knowledge base integration, tool use, function calling, and multi-turn conversation systems.
Safe and Aligned AI
Constitutional AI, safety filtering, toxicity reduction, and responsible deployment of LLM-powered products.
Real-World Applications
Domain-Specific Chatbots
Fine-tuned LLMs that understand your industry terminology, workflows, and customer needs.
Code Generation
Custom code assistant models trained on your codebase, coding standards, and preferred libraries.
Document Summarization
Legal, financial, and medical document summarization with domain-appropriate accuracy and style.
Knowledge Management
RAG systems over internal documents, wikis, and databases for accurate, cited AI responses.
Content Generation
Brand-aligned content models for marketing copy, product descriptions, and creative writing at scale.
Data Extraction
Structured information extraction from unstructured text using LLMs as flexible parsing engines.
Ready to Build Your LLM Pipeline?
Tell us your LLM use case and we'll design a fine-tuning plan with clear performance targets and timelines.
LLMs That
Perform in Production
LLM projects fail when they're evaluated on benchmarks but deployed on real user queries. We build LLM systems with rigorous evaluation on your actual use case: domain-specific prompts, edge cases, safety constraints, and cost targets. Every LLM pipeline includes response quality testing, safety red-teaming, and cost monitoring so your system performs reliably at scale.
Use-Case Evaluation
We evaluate on your actual prompts and queries, not just generic benchmarks, ensuring real-world performance.
Safety-First Design
Red-teaming, toxicity filtering, and guardrails ensure your LLM behaves safely in production.
Cost Optimization
Model selection, quantization, and prompt optimization to minimize cost while maximizing quality.
Building Your
LLM Pipeline
Our LLM process ensures your language model performs reliably on real queries with measurable quality and safety metrics.
1. Use Case Definition
Define the LLM's task, success criteria, safety requirements, and cost constraints.
2. Data Preparation
Create instruction datasets, preference pairs, and evaluation sets with domain experts.
3. Model Development
Fine-tune, align, and optimize the LLM with SFT, RLHF, or prompt engineering based on your needs.
4. Safety and Quality Testing
Red-team for safety, test on real queries, and validate quality with domain experts.
5. Deploy and Monitor
Launch with cost tracking, quality monitoring, and feedback loops for continuous improvement.