We build GenAI systems that go into production—not demos. Our solutions hold up under real-world constraints in scale, accuracy, and compliance.
Our stack blueprints—vector DBs, prompt routers, eval layers—are already in production across healthcare, finance, and SaaS platforms.
From Claude and GPT-4 to LLaMA and Mistral—we evaluate tradeoffs in licensing, customization, and cost, and deploy accordingly.
We design orchestration layers and internal tools that teams can evolve without vendor constraints.
Pilots often use hosted APIs with no eval, grounding, or governance. Production GenAI requires orchestration layers, system prompts, monitoring, and domain-specific control built into the stack.
Yes. We help clients evaluate GPT-4, Claude, Gemini, LLaMA, Mistral, and others — based on cost, capability, TCO, fine-tuning paths, and compliance constraints.
Our stacks typically include vector DBs, grounding workflows, retrieval augmentation, eval harnesses, caching layers, system prompts, logging, and rollout gates.
Agents are autonomous or semi-autonomous systems that can reason, plan, and act. We build agentic systems with prompt chaining, tool use, memory, and human fallback paths.
Yes — if built right. We include audit logs, HITL enforcement, prompt traceability, and content filters that align with regulatory needs from the start.
Start by identifying your top-priority GenAI use case. We’ll assess the gap between demo and deploy — then build the stack that bridges it.
