We start by turning your idea into a clear product design and user flow, so we know exactly what needs to be built.Then we design the full architecture — including data pipelines, vector stores or knowledge graphs, and how everything connects. We clean, structure, and stream your data so the AI has something reliable to work with. If your use case needs it, we fine-tune or train models to match your domain. Finally, we assemble the full web, backend, and AI platform and deploy it on your cloud so it’s ready for real users.

We don’t rely on random hiring or one-off freelancers. We run a living network of fractional AI researchers and specialists that we continuously discover, test, and vet as the field evolves. For hard problems — novel models, complex retrieval, or tricky data — we tap into this researcher network. For productizing and shipping, we bring in our full-time engineers who follow proven DevOps, MLOps, and open-source AI practices. The result is teams that can solve deep technical challenges and still deliver production-ready AI applications.

We’re model-agnostic — we work with OpenAI, Anthropic, and leading open-weight models like Gemma, Llama, Qwen, and DeepSeek, as well as on-prem and fine-tuned deployments when required. We design a custom orchestration layer for each use case, combining the right tools for the job — from agent frameworks and vector databases to analytics, graph databases, and production-grade MLOps built on FastAPI, Docker, and Kubernetes.

Most agencies give you developers. Most vendors give you tools. We give you full-stack AI teams that collaborate with your internal teams and deliver real, production-ready applications. Our teams combine product builders, AI engineers, and researchers who’ve already shipped AI systems in the wild. We’re model-agnostic, deploy everything on your cloud, and stay ahead through our fractional research network — so you get working AI products, built using cutting-edge AI models.

Hiring top AI and ML talent is slow, expensive, and highly competitive — and most teams end up overpaying or underutilizing people just to keep them around. We drive that cost down by continuously grooming and training our own engineers, and by using a fractional network of senior AI researchers only when their expertise is actually needed. That means you get access to high-end talent without paying full-time rates for it, and your budget goes into building your product instead of funding a hiring war.