Enterprise LLM and AI Development
We build production-grade LLM applications from RAG systems and AI assistants to fine-tuned models and evaluation pipelines, tailored to your business data and workflows.
What we build
Custom LLM Applications
Build production-ready applications powered by GPT-4, Gemini, Claude, or open-source models like LLaMA. We handle prompt engineering, context management, and safety.
- Conversational AI assistants
- Customer support bots
- Internal knowledge Q&A
- Code generation tools
RAG Applications
Connect LLMs to your proprietary data using vector databases and retrieval pipelines so your AI answers from your documents, not general training data.
- Document ingestion pipelines
- Vector DB setup (Pinecone, Weaviate)
- Semantic search integration
- Multi-document reasoning
LLM Fine-Tuning and Evaluation
Fine-tune open-source models on your domain-specific data for higher accuracy and lower cost. We benchmark, evaluate, and iterate until performance meets your threshold.
- LoRA and QLoRA fine-tuning
- RLHF and DPO alignment
- Evaluation benchmarks
- Model hosting and inference APIs
Models we work with
- OpenAI GPT-4o and GPT-4 Turbo
- Google Gemini 1.5 Pro and Flash
- Anthropic Claude 3.5
- Meta LLaMA 3 (open-source)
- Mistral 7B and Mixtral
- HuggingFace ecosystem models
Common use cases
Internal HR chatbot
Employees query HR policies, leave balances, and onboarding docs via a conversational AI.
Legal document summariser
Upload contracts and get structured summaries, risk flags, and clause extraction instantly.
E-commerce product assistant
Customers find products through natural language queries connected to live inventory.
Engineering code assistant
Internal code search and documentation assistant fine-tuned on your codebase.
Ready to build with LLMs?
Tell us your use case and we will scope out the right architecture for your data and workflows.
Zero obligation • Direct technical conversation with engineers • NDA upon request