Package funnel
AI feature slice
One vertical slice of an AI workflow—streaming UI, tool states, and evaluation hooks you can extend safely.
Visual preview
The “hard part” made visible.
Streaming response
Summarize this ticket and draft a reply.
Here’s a draft you can send:
Tool state
retrieving…
Cancel
safe to stop
Streaming UX with tool states
AI features fail in the UI first: loading, cancellations, and partial responses. This preview shows the kind of stateful streaming experience you get in a slice.
- Pending/streaming/error states
- Cancel + stale guards
- Eval hooks
Fit
Ideal for
- Teams validating an AI-assisted workflow before committing to a large surface area
- Products that need streaming UX + robust empty/error states from day one
- Engineering leads who want provider abstraction so models can change without UI rewrites
Not the right fit
- Training custom models or owning ML infrastructure
- Full multi-agent orchestration across many services as the first deliverable
What you get
Outlined so you can map this to your website, app, or internal tool—before we lock milestones.
Streaming UX
- Token/stream UI states: pending, streaming, completed, failed
- Cancellation + stale response guards for fast navigators
Provider boundary
- Interface that isolates vendor SDK differences from your domain logic
- Configuration pattern for model selection per environment
Evaluation & safety hooks
- Checklist: regression prompts, golden outputs, and red-team notes
- Logging boundaries: what to capture for debugging vs what to avoid storing
Roadmap
How you go from 0 → MVP, with artifacts you can hold me to.
Growth curve
We ramp capability, not chaos—each step ends with shippable artifacts and a clearer “next.”
Step 1 · MVP slice spec · Days 1–2
From idea → a defined AI workflow slice with success criteria and failure rules.
- Success rubric (quality/latency/fail states) + guardrails
- Provider boundary plan (don’t leak vendor SDK into UI)
- Eval checklist + regression prompts
Step 2 · Build the MVP · Days 3–7
From 0 → a shippable AI feature with streaming UX and real tool states.
- Streaming UI states (pending/streaming/done/failed)
- Cancellation + stale response guards
- Provider abstraction interface (swap models safely)
Step 3 · Ship + extend · End of Week 2
From MVP → a maintainable extension path: logging, safety notes, and next slices outlined.
- Logging boundaries + safety notes
- Golden prompts + expected outputs
- Support window for iteration feedback
Example scenarios
Illustrative—not a fixed menu. Your product gets the same structure, scoped to your stack and release target.
Support copilot (internal)
Agent suggests replies from your knowledge base; human approves before send—full happy path + escalation when confidence is low.
Document Q&A over uploads
User attaches PDFs; system chunks, retrieves, and answers with citations; empty states for unsupported file types.
Guided onboarding assistant
Step-by-step questions with tool calls to validate API keys or account state, stopping cleanly when prerequisites fail.
How we'll run it
- 1Pick one workflow slice and define success: latency, quality, and human-in-the-loop rules.
- 2Implement UI + server boundary with the provider abstraction in place.
- 3Run eval checklist; hand off extension points for the next slices.
Scoped milestones, async-friendly reviews, and a written handoff so ownership is obvious on day one.
Next steps
Continue to the site funnel to confirm this package, request a variant, or switch to a fully custom scope with the intake form.