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Valtair
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Vertical AISeed-stage founder

From MVP to a production-ready product in one quarter

We took a founder's promising prototype and turned it into a monitored, evaluated production system ready for its first paying customers, without losing the momentum of the early build.

Illustrative example. Named engagements are shared with client permission.

01

Context

A seed-stage founder had a prototype that demoed well but had no evaluation, no monitoring, and no architecture for reliable scale. Investors and early users needed something dependable.

02

Challenge

A promising prototype had no evaluation, no monitoring, and no path to reliable scale.

03

Product approach

We rebuilt on an architecture that treated evaluation and cost as first-class from the start, added monitoring and guardrails, and shipped in stages so the founder always had something to show.

04

System architecture

FrontendThe product experience, refined for the first cohort.
BackendA multi-tenant API with background processing.
AIModel orchestration with evaluation built in.
RetrievalDomain data grounding where needed.
IntegrationsCore third-party services for the workflow.
InfrastructureCloud deployment with monitoring and cost controls.
05

AI and data components

  • Model orchestration with routing
  • Evaluation harness against real inputs
  • Guardrails and failure handling
  • Cost controls
06

Backend and integrations

  • Multi-tenant API
  • Background processing
  • Third-party integrations
  • Monitoring and alerting
07

Engineering decisions

Evaluation before features

We built an evaluation harness first, so every later change could be measured rather than guessed at.

Ship in stages

We released in stages to keep the founder credible with users and investors throughout the build.

08

Responsible AI and safeguards

  • Quality measured with an evaluation harness
  • Monitoring for drift in production
  • Guardrails and failure handling
  • Honest product staging, no prototype dressed as production
09

Outcome

  • A production system ready for the first paying cohort
  • Predictable inference cost from day one
  • Confidence to scale, backed by measurement
10

Lessons

  • Evaluation built in early makes every later decision faster and safer.
  • Cost controls belong in the first architecture decision, not the last.
  • Shipping in stages keeps a founder credible while the system hardens.

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