Workflow model
The job, people, systems, evidence, exceptions, and outcome the system needs to support.
AI features that belong in the product
Capabilities shaped around a specific user job, product context, and experience people already understand.
The most useful AI features feel like a natural extension of the product. We look for a specific user job where model capability can improve the result, then test the idea in context before asking the team to support it in production.
Where this can help
Often a good fit
- SaaS products with a high-friction user job
- Vertical software with differentiated workflow
- Products replacing manual setup or analysis
Probably not the right fit
- Adding AI to satisfy a roadmap label
- Features with no product context
- Unbounded generation where correctness is essential
What the engagement can produce
Integrated capability
A useful interface connected to approved data, tools, permissions, and operating context.
Review and recovery
Visible handoffs, exception handling, evidence, and human decisions where needed.
Learning record
Examples, feedback, and measures that show whether the capability is genuinely helping.
How we work together
Choose the job
Start with a valuable, bounded piece of work instead of a vague AI category.
Connect context
Use the systems, information, and policies people already rely on.
Build with evidence
Keep source material, calculations, and actions inspectable as the capability takes shape.
Learn in use
Review real cases and improve the system with the people responsible for the outcome.
What may be involved technically
- Product context
- Streaming UX
- Model routing
- Usage limits
- Evaluation
- Privacy and tenancy
Questions teams often ask first
Can you work inside our application?
Yes. We integrate with existing design systems, identity, data contracts, and release process.
How do we avoid provider lock-in?
Keep product behavior and evaluation separate from provider APIs, then test alternatives against the same cases.