AI agents & automation
We take judgment-heavy work your team does by hand and hand it to agents you can trust. That means deciding where an LLM earns its place versus a simpler automation, orchestrating agents that call your tools and data, and building the evaluation, guardrails, and observability that let them run in production rather than stall as a demo.
- Agent and multi-agent architecture for multi-step, judgment-heavy work
- LLM workflow design and orchestration across tools and steps
- Tool use, function calling, and integration into your existing systems
- Retrieval, memory, and persistent state over your data
- Guardrails, human oversight, and failure controls
- Evaluation, observability, and cost control so behavior stays measurable
Shipped: an AI shopping assistant for an online fashion platform, and a RAG support assistant taken to production.
multi-agent systems · retrieval (RAG) · tool use & integration · agent memory · evaluation & guardrails
Custom model training
A general API gets you started, but not far: it does not know your domain, the cost climbs with every call, and your data leaves your network. We turn open models (Llama, Mistral, Qwen) into specialized systems that learn your language, tone, and output formats, and ship them on your cloud or your own servers, with the weights under your ownership. We also give you an honest answer about whether training is warranted, since retrieval is often the better first step.
- Model and method selection by quality, cost, latency, privacy, and deployment fit
- Domain adaptation on your proprietary data, terminology, and specialist knowledge
- Instruction and preference tuning, including alignment, for reliable behavior and format
- Distillation and inference optimization for smaller, faster, cheaper models
- Ownership of the trained weights, with no vendor lock-in or per-call fees
- Private deployment on your cloud, private cloud, or on-prem infrastructure
Shipped: a 0.8B on-prem SQL agent post-trained to rival far larger models on text-to-SQL.
open-model fine-tuning · distillation · alignment · on-prem & cloud deployment · owned weights
