AI products
with a point of view.
Choose useful AI workflows, evaluate them and keep people in control.
Choose an AI Use Case with a Clear Boundary
Evaluate an AI product use case by its task, error consequences, available evidence, review effort, and fallback before committing to a broad assistant.
Design Human Review That Actually Helps an AI Workflow
Design AI review around specific checks, source context, editability, and decision ownership so a human approval step supports a useful product workflow.
Build an AI Evaluation Dataset from Real Product Tasks
Create an AI evaluation dataset with representative inputs, difficult cases, expected behavior, review criteria, and controlled updates tied to product decisions.
Prepare a Retrieval Corpus for an AI Feature
Prepare product knowledge for retrieval with ownership, document scope, freshness, access boundaries, and checks that distinguish retrieval from answer failures.
Communicate AI Uncertainty with Useful Next Actions
Design AI uncertainty messages that identify missing context or weak support, offer clarification and source review, and preserve progress without invented confidence.
Bound AI Tool Permissions by the User Task
Define AI tool permissions with task-specific scope, server checks, previewable changes, explicit approval, and recovery so generated intent cannot exceed user authority.
Design a Manual Fallback for an AI Feature
Create an AI fallback that preserves inputs and progress, explains incomplete work, supports manual completion, and can be exercised when the feature is unavailable.
Design Prompt Input as a Product Interface
Create AI prompt input with a clear task boundary, useful examples, structured context, editable requests, and recovery that reduces guessing in product workflows.
Define AI Data Boundaries Before Integration
Map the data used by an AI workflow, limit inputs and retention, separate permissions from interface visibility, and verify boundaries across retrieval and feedback.
Set an AI Latency Budget for the Workflow
Plan AI response time around the user task, feedback states, cancellation, progressive results, and quality tradeoffs rather than judging generation speed alone.
Evaluate AI Cost Against Completed Work
Assess AI operating cost through task completion, context use, retries, review burden, and explicit quality gates, without choosing a provider from headline pricing.
Test AI Features with Untrusted Content
Evaluate AI handling of untrusted documents and messages through adversarial examples, source boundaries, permission checks, and controlled tests of proposed actions.
Set AI Release Gates and Regression Checks
Define AI release gates using task evidence, consequential failure cases, data boundaries, recovery, and regression checks that remain meaningful as the feature changes.
The best AI features feel like a natural part of the product
Choose AI features around real tasks, design review and recovery, evaluate realistic outputs, and keep users informed about limitations before expanding use.
Build it. Brand it.
Bring it to market.
From product development to brand strategy and launch marketing, let’s find the right next step for your idea.