infrai

Auditable Healthtech Welcome Email: Templates, API, Domain

The article advises healthtech teams to evaluate transactional email APIs (SendGrid, Resend, Postmark, Infrai) by verifying auditable evidence rather than feature lists. It defines five required artifacts for a verification-email workflow: API-accepted send, rendered body from an approved template revision, verified sending domain with DKIM rotation, correct suppression handling, and a retrievable delivery record that can be stored in the team's evidence store. The author demonstrates a pull-based reconciliation approach (example Python client calling Infrai's GET /v1/email/event/list) and recommends running synthetic-signup experiments that reuse a correlation ID and avoid production patient data. Infrai is presented as a practical API-first option that consolidates keys and billing but is pull-only for events and lacks SMTP relay or managed OTP; teams should choose providers based on observed evidence retention and operating-model fit.

Bulk LLM Text Classification with Tenant Chargeback

The article recommends treating tenant accounting as the primary artifact when performing bulk CSV moderation with LLMs: create a tenant-owned job with stable row IDs, estimate costs before submission, submit asynchronous batch classification (preferably chat classification with a closed label set), and attach returned results and export references to the same tenant ledger for reconciliation. The author provides an example TypeScript batch submission pattern (idempotency derived from the validated request, bounded retries, handling 429), argues for allocating costs at the job boundary and reconciling at the row level, and discusses when to call providers directly (Infrai, OpenAI, Anthropic, Google Gemini) versus renting batch execution.

Feature Flags API: React Polling with Defensive Defaults

Technical guidance recommending a polled feature-flags API as a configuration source for a React support console, with compiled fallback defaults in the frontend and a backend adapter that owns sensitive decisions (authorization, billing, retries). The author outlines invariants (defaults-first, monotonic safety, bounded staleness), client polling patterns (initialize from immutable defaults, single provider per tab, jittered intervals), error handling (backoff, honor Retry-After, preserve prior snapshot on malformed responses), and compares candidate flag providers (Infrai, LaunchDarkly, ConfigCat, Unleash) while advising separate observability tooling (Sentry, Datadog, Grafana) and providing a runnable Python backend adapter example.