BuildersDevelopersShipping discipline

Upcube for Builders & Developers

Ship AI features that behave like software: versioned, tested, monitored, and rollbackable. Upcube gives you clean APIs, eval gates, observability, and enforceable policy controls—so teams can move fast without turning production into a prompt experiment.

API-firstEvals before deployObservabilityGuardrails & policiesVersioned promptsReproducible runs
Position: AI in product should be treated like software—versioned, tested, monitored, and rollbackable.

Snapshot

A practical stack for shipping AI features with reliability, measurability, and governance.

API & SDK layer

Typed endpoints for chat, tools, retrieval, and structured outputs. Build features that stay stable as prompts and models evolve.

SDKs · Schemas
StreamingTool callingStructured outputsWebhooks

Eval harness

Regression tests for prompts, tool chains, and retrieval—before users discover the failure mode.

Golden sets · CI
ScorecardsRubricsCanary checksRelease gates

Observability & replay

Trace every run end-to-end, measure cost/latency, and replay incidents deterministically.

Traces · Replay
Cost per runLatencyError taxonomyRepro manifests

Policy & guardrails

Central rules for safety, data handling, and tool permissions—enforced at runtime.

Policies · Roles
RBACTool allowlistsData boundariesApprovals

Offerings

Modular building blocks. Start with one capability and expand to a disciplined AI layer across product and ops.

1) Product AI SDK (typed)

Build features that stay stable as prompts and models evolve—validation, retries, tool permissions, and structure are first-class.

  • • Structured outputs: schemas, validation, retries
  • • Tool calling: approved tools only, constrained params
  • • Retrieval: citations + source controls by default
  • • Streaming: fast UI with partial responses
  • • Multi-tenant ready: workspace scoping + quotas
Where this saves time
  • • Fewer “worked yesterday” regressions
  • • Less glue code around validation and retries
  • • Faster debugging with consistent run artifacts

2) Prompt & workflow registry (versioned)

Treat prompts and workflows like deployable assets—diffs, approvals, promotion flows, and rollbacks.

  • • Versions: change logs and ownership
  • • Templates: system rules + style packs
  • • Promotion: dev → staging → prod
  • • Diff view: what changed and why
  • • Access: who can edit vs publish
Example: promotion flow
  1. Draft in dev → run eval suite
  2. Review diff → approve to staging
  3. Canary traffic → monitor score + cost
  4. Promote to prod → keep rollback ready

3) Evals & release gates

Stop shipping “hope.” Ship measurable behavior with scorecards, thresholds, canaries, and rollback triggers.

  • • Golden sets: curated inputs + expected behavior
  • • Rubrics: correctness, safety, tone, citations
  • • Scorecards: pass/fail thresholds per capability
  • • CI: block releases on regressions
  • • Canary + rollback: confidence thresholds and fallbacks
Minimum eval suite
  • • 20 normal user requests (happy path)
  • • 10 edge cases (ambiguity, missing info)
  • • 10 adversarial prompts (injection, unsafe tools)
  • • 10 retrieval checks (citations + source correctness)
Design target: Make AI features easier to ship and harder to break—by making outputs testable, runs replayable, and governance enforceable.