MVPs & proofs of concept
A working build that proves the idea — fast. We start day one and put something real in your hands within days, then iterate toward production instead of debating it in a planning deck.
Applied AI, delivered as outcomes — built on AWS, GCP, or Azure.
We skip the two-week planning deck and start building on day one — voice agents, agentic workflows, and retrieval over your data. Hardened toward production and handed over running.
Concrete systems, not experiments. Each one starts as a POC you can use and grows into production.
A working build that proves the idea — fast. We start day one and put something real in your hands within days, then iterate toward production instead of debating it in a planning deck.
Real-time voice agents and multi-step agentic workflows that do the task — not just chat about it. Grounded, observable, and handed off cleanly to a person when they should be.
RAG and GraphRAG that answer from your own knowledge, with lineage back to the source — accurate where a generic model would guess. Built on the data you already have.
We don't stop at the demo. We harden, instrument, and hand the system over running in your cloud — observable and auditable, and yours to own.
No discovery theater. No two-week planning deck. We scope the outcome and start immediately.
One call to define the outcome and what "done" looks like. We agree on success criteria, not a backlog of meetings.
We begin the same week, in your cloud — AWS, GCP, or Azure. No environment hand-wringing, no setup tax passed to you.
You get something real to use and react to within days. We iterate against your feedback, not a frozen spec.
Once it earns its place, we harden it, instrument it, and hand it over running — documented and owned by your team.
We build in the environment you already run. The system lives in your account from day one — no lock-in to a platform of ours.
Bedrock · SageMaker · Lambda · ECS / Fargate
Vertex AI · Gemini · Cloud Run · BigQuery
Azure OpenAI · AI Foundry · Container Apps · AKS
Four patterns we reach for, and when each one earns its place. Not sure which? Figuring that out is the first thing we do together.
The model pulls the most relevant passages from your documents at query time and answers from them — grounded in your content, with citations back to the source.
Best for: question-answering and search over a large body of documents or a knowledge base.
Builds a graph of the entities and relationships in your data, so the system can reason across connections and answer questions that span many documents at once.
Best for: connected, relational data and "how does X relate to Y" questions.
Several role-specific agents that plan, call tools, and hand off to each other to finish multi-step work — not just answering, but taking action.
Best for: automating processes that take several steps and real tool use.
An open standard for connecting models to your tools, data, and systems through one consistent, secure interface — the plumbing your agents run on.
Best for: giving AI reliable, reusable access to your internal tools and data.
A sentence on the problem, your cloud, and your timeline is enough to start. We reply within a business day.