Release confidence
AURA
AURA produces a release confidence score for every software release, validating across API, UI, backend state, and data layers.
Learn more about AURA
AI product development
AI product development.
Software where the model is the core, not a feature: agents inside real workflows, computer vision on your domain data, and evals, fallbacks, and telemetry from the first commit.
Evals
Designed in from the first commit
Fallbacks
So the behavior holds up under real use
AURA
Validates every release across API, UI, backend state, and data
The problem we solve
| AI-layered | AI-native | |
|---|---|---|
| Architecture | An AI feature added to an existing app, often as a chat tab. | Built around the model from the start: the workflow, data, and interface assume AI. |
| In production | The demo impresses; the production version is unreliable, hard to evaluate, and impossible to debug when it drifts. | Evaluations, fallbacks, and telemetry designed in from the first commit, so the behavior holds up under real use. |
| Agents | Demo chatboxes. | Agents that operate inside real workflows, with hand-off boundaries you can audit. |
| Cognitive load | Stays constant. The human still thinks; the system still just executes. Read the post | Decreases over time. |
What this includes
Agent interfaces
Agents that operate inside real workflows, not demo chatboxes. Support agents, internal tools, and embedded copilots with hand-off boundaries you can audit.
Computer vision
Visual recognition and quality inspection trained on your domain data. Built for production floor, warehouse, clinical, and field environments.
AI-driven workflows
Backend flows and system-level triggers that remove manual handoffs. Scoped to the workflow, not the feature list.
Model integration
LLMs, custom models, and third-party AI APIs integrated with evals, fallbacks, and telemetry designed in from the start.
Designed in from the first commit
- agent boundariesHand-off boundaries you can audit, for agents inside real workflows.
- evalsEvaluations, so the AI behaves reliably in production.
- fallbacksSo the behavior holds up under real use.
- telemetryEngineered in rather than retrofitted after incidents.
- release confidenceEvery release validated with AURA across API, UI, backend state, and data layers.
Proof, on our own products.
We build our own AI-native products, so the practice is not theoretical. Both are built on the same AI-native approach we bring to client products.
Design governance
Tenet
Tenet captures why design decisions were made and surfaces that context when new decisions happen.
Learn more about Tenet
We build software where the model is the core, not a feature, with evals, fallbacks, and telemetry from the first commit.
Show us the workflowQuestions
Common questions about AI products.
AI product development is building products where AI is the core, not a feature pasted onto an older architecture. Models, agents, and automation are designed into the product from the first commit, with evaluations, fallbacks, and telemetry so the AI behaves reliably in production.
AI-layered products add an AI feature to an existing app, often as a chat tab. AI-native products are built around the model from the start: the workflow, data, and interface assume AI, and reliability is engineered in rather than retrofitted after incidents.
Agent interfaces that operate inside real workflows, computer vision trained on your domain data, AI-driven backend workflows that remove manual handoffs, and model integrations (LLMs, custom models, third-party APIs) with evals and fallbacks designed in.
We design release confidence in from the start: evaluations, fallbacks, and telemetry. We validate releases with AURA, our own release confidence platform, which checks behavior across API, UI, backend state, and data layers rather than relying on brittle scripts.
AI product development
Building something the model has to carry?
Tell us what you are building. We will show you how we would approach the AI core.