What it is
We replace routine work with LLM assistants: answering from your knowledge base, classifying inbound tickets, parsing invoices and contracts, generating reports. For domain-specific edge cases — model fine-tuning.
What’s included
- RAG systems: indexing corporate documents into a vector database (pgvector, Qdrant, Weaviate), semantic search instead of keyword match, answers with source citations.
- Embeddings: picking and tuning embedding models (OpenAI, Cohere, BGE, multilingual) for the client’s language and domain.
- Fine-tuning: further training of open-source models (Llama, Qwen, Mistral) for specific tasks on our own Blackwell-class GPU lab rig.
- Integrations: assistants meet users where they already work — Telegram, Slack, web UI, plugins for 1C and email.
- Self-hosted deployments: for clients with confidentiality requirements — models run on their own servers, data never leaves the perimeter.
When you need this
- Your company has accumulated gigabytes of documents (policies, manuals, old correspondence), and people spend hours hunting for answers.
- There’s routine work with reasonable rules but too much variation for classical RPA: ticket triage, classification, first-pass document review.
- Off-the-shelf SaaS LLMs don’t fit your confidentiality model, or get expensive at high request volume.
Track record
We run our own GPU lab rig with Blackwell-class hardware, where we work out fine-tuning runs and quality benchmarks before rolling them into client production. Deployed RAG systems live both on our servers (Cloudflare Workers + pgvector) and inside client infrastructure.