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AI development

Give AI a useful job in your product.

Build AI features around a defined task, your data, and measurable behavior. We plan integrations, evaluate outputs, and design the controls a live product needs.

Overview

AI development adds model-based capabilities to a product or internal workflow. We begin with a task that can be described and evaluated, then examine the data, integrations, and human decisions around it. The implementation may involve retrieval, generated content, or an agent that uses approved tools. We treat evaluation, access controls, and failure handling as part of the product. Our founders have built AI coaching experiences and automation for product and engineering teams.

What we work on

  • Define the task, success criteria, and representative evaluation examples
  • Design data retrieval, model integration, and permitted tool access
  • Build the AI workflow and its product interface
  • Implement evaluation checks, monitoring, and fallback behavior

What you receive

  • A scoped AI workflow with documented data boundaries
  • A working integration and the agreed product experience
  • An evaluation set with acceptance criteria and recorded results
  • Operational notes covering model configuration, limits, and review needs

What shapes the cost?

Data preparation, retrieval complexity, tool integrations, evaluation depth, and privacy requirements affect development. Model and infrastructure usage add operating costs.

What shapes the timeline?

We begin with a representative task and evaluation before expanding the feature. Data access, quality issues, and integration permissions influence the schedule.

What to bring to the first conversation

Bring examples of the task, representative data you may use, acceptable and unacceptable outputs, system integrations, and privacy or review requirements.

Selected work

Experience behind the service

GrowthDay: source portfolio visual
Product engineering

Engineering for a platform that grew to 400K+ users.

Growing a personal development platform meant rebuilding its frontend, improving mobile checkout and giving the enterprise product room to grow.

400K+ Platform users
+9% Checkout conversion improvement

FAQ

Before we get started.

Do we need to train our own AI model?

That depends on the task and the evidence from evaluation. An existing model with suitable context or retrieval may be enough. We compare options against output quality, privacy, latency, and operating cost before committing to a more complex approach.

How do you check whether the AI is reliable?

We agree representative examples and failure cases for the actual task. Evaluation checks the outputs against those expectations, including when the system should decline or ask for help. A model change or workflow change should trigger relevant evaluation again.

Can the AI take actions in our other systems?

It can when the integration and permissions support that scope. We define the allowed actions, the identity used to perform them, and where a person must approve a consequential step. Tool access needs testing as well as the generated response.

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What's on your mind?

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