Describe your API. Reuse it everywhere.

Describe your API in a prompt — get a working workflow scaffold

Open the AI Assistant in the workflow builder and describe the API layer you want in natural language — "a product page API that fetches catalogue, inventory, and pricing in parallel and merges the responses" — and younifyd generates a complete workflow scaffold as a starting point.

The generated workflow includes routes, HTTP triggers, connector steps, parallel execution blocks for independent calls, and merge steps with JSONata transformations. Iterate with follow-up prompts as your requirements evolve, refine in the visual builder, and deploy when you're ready.

AI-generated workflows are a scaffold that eliminates the blank-canvas problem. You review every route and step before production — but experienced engineers reach a working starting point in minutes, not hours.

~10minfrom prompt to first workflow draft

Build your business logic once — call it from your storefront, an MCP tool, or a webhook

A workflow route is an API endpoint — the same route that answers your storefront's product page call is the same route an MCP server can expose as a tool for AI agents, running through the identical execution engine with the identical business logic every time. Nothing is duplicated to make a route agent-callable.

For workflows behind different trigger types — a storefront-facing HTTP route, a webhook-triggered automation — the Invoke Workflow connector lets one workflow call directly into another by slug. Put your core business logic in one shared workflow, then wire thin, trigger-specific entry points that call into it. Change the shared logic once and every entry point picks it up immediately.

This is what makes a rule like "a customer can add a maximum of 10 of the same item" reliable: define it once as a validation step inside the shared workflow, and it's enforced identically whether the request came from your storefront, an AI agent's tool call, or an inbound webhook.

1 workflowreused across storefront, MCP, and webhooks

Response time = slowest call, not the sum of all calls

When your product page needs data from six services, calling them sequentially adds their latencies together — 80ms + 95ms + 110ms + 80ms + 70ms + 90ms = 525ms. younifyd fans out to all independent services simultaneously. The response arrives in 110ms — the time of the slowest single call — not 525ms.

Group independent calls in a parallel step and younifyd executes all of them concurrently, then continues once they've all resolved. Steps that depend on another step's result go outside the parallel block, in the order they need to run — you control the shape, younifyd handles the concurrent execution.

Any step can be marked to continue past its own failure instead of stopping the whole workflow — useful when a non-critical call (an analytics ping, an optional enrichment) shouldn't be allowed to take down the response for everything else.

60%+typical latency reduction

Reshape any response without touching your backend

Upstream services return data in the shape they own. Your clients need data in the shape they can use. younifyd handles the translation between the two without requiring changes to either side.

Transformations run inline on every response using JSONata (a declarative JSON query and transformation language), TypeScript functions for complex logic, or low-code field mapping rules for simple remapping. Rename fields, filter arrays, merge data from multiple upstream calls, compute derived values, and format dates and currencies — all within the orchestration step.

Because transformations run in the orchestration layer, they apply consistently to every consumer of the workflow. The web storefront, mobile app, and partner API all receive the same correctly shaped data. When an upstream service changes a field name, you update the transformation once.

1 placeto update when upstream APIs change

Reject bad data at the boundary before it reaches your services

Without validation at the orchestration boundary, malformed requests propagate into your backend services before you know they are invalid. A missing required field silently produces an incorrect database record. An integer where a string was expected causes a runtime panic three service hops downstream.

Add a schema validation step anywhere in a workflow and younifyd validates the data against a JSON Schema definition. If validation fails, the workflow stops and returns a configurable HTTP error response — the error message, status code, and response body are all configurable per validation rule.

Point the same validation step at an upstream service's response instead of the inbound request, and a service that changes its API contract without notice produces a clear, visible error rather than silently corrupting your composed response.

configurablevalidation and error response, per step

Cut upstream load by 60–80% without touching your services

Caching in younifyd is per-step with a configurable TTL, not a blunt full-response cache. A product catalogue that changes every few minutes gets a 5-minute TTL. Pricing that changes hourly gets a 60-second TTL. Inventory at checkout — where staleness is unacceptable — gets no cache at all.

Cache keys are templated per step, so you control exactly what varies the cache entry. Include the customer segment in the key for personalised pricing; leave it out for shared catalogue data so every customer hits the same cached entry.

per-stepconfigurable TTL and cache key

Analytics and logging without adding latency to your response

Some workflow steps don't need to complete before the response returns. Sending an event to your data warehouse, logging a user action to an analytics platform, or triggering a notification are all operations where the customer doesn't wait for confirmation — and shouldn't.

Fire-and-forget steps execute asynchronously in the background. The workflow proceeds to the response step immediately, and the fire-and-forget step runs in parallel without blocking. Your response time reflects only the synchronous work; the background work happens independently.

This pattern eliminates a common performance antipattern: sequential calls to non-critical services that add latency to every user interaction. With fire-and-forget, data warehouse events, audit logs, and notification triggers add exactly 0ms to your user-facing response time.

0mslatency added by analytics calls