We work with financial companies — crypto, fintech and trading desks — building AI for market analysis, signals, risk and research with the security, auditability, and reliability that finance demands.
Large language models turn your text, documents, and data into knowledge — answering, summarizing, and reasoning. We help you pick the right model (open or commercial), integrate it and shape it around your use case — the foundation for everything below.
We connect your documents, wikis and databases to a model with retrieval (RAG), so your team and customers get accurate, up-to-date answers — every one traceable to its source.
We fine-tune models on your data, terminology, and tone (including efficient LoRA adapters) — sharper results on your specific tasks without the cost of training from scratch.
We build AI agents that reason and take action across your tools, APIs, and systems — completing multi-step processes end to end, with guardrails that keep them safe, predictable and on task.
We add tracing, automated evaluation, and dashboards for quality, latency, and cost — so you can see exactly how your AI performs and catch problems before your users do.
We integrate, deploy, and maintain your AI system in production — with monitoring, versioning, security and continuous improvement as your traffic and needs grow.
A production stack we know end to end — Spring AI for enterprise integration in your Java/Spring backend, LangChain for orchestration and agents, Ollama to run open models in your own environment, Milvus as the vector database for retrieval and Langfuse for tracing, evaluation, and analytics. We are able to integrate Python based AI stacks as well.