AI that earns its place in your product.
Chatbots that resolve, agents that ship work, RAG systems that answer with sources, and generative features that customers actually use. We build AI like we build software — measurable, maintainable, and accountable.
AI is a system, not a feature.
Most AI demos look magical and break in production. We build the unglamorous parts that turn a prototype into a system: evaluations, guardrails, observability, retrieval pipelines, and the iteration loops that keep performance from drifting once real users show up.
Whether you’re embedding AI into an existing product, replacing a brittle workflow, or shipping a new generative experience, we treat the model as one component inside a system that has to be reliable, fast, and explainable.
Sub-services in this category.
Each one is a dedicated practice with senior leads, dedicated tooling, and a written playbook.
How we run an engagement.
Audit
We review your data, your stack, and the workflows you want to change. We define success metrics before writing prompts.
Design
We design conversation flows, agent boundaries, and evaluation criteria. Tone, escalation rules, refusal behavior — all written down.
Build
We integrate with your LLM of choice (OpenAI, Anthropic, open-source), connect your data sources, and stand up evals from day one.
Launch & tune
We deploy to a slice of users, monitor latency, accuracy, and cost, and improve weekly. AI is never done; it’s tuned.
Frequently asked.
5 questions answered. Still have one? Reach out.
It depends on the workload. For high-stakes reasoning and long context, we default to Claude. For low-latency, low-cost classification and chat, we use GPT-4.1-mini or open-source. We benchmark on your data before recommending.