Workflow Automation - Inngest
Durable, event-driven workflows
that survive the real world
We design and ship Inngest functions for mid-market operations teams - handling retries, fan-out, concurrency limits, and multi-step AI agent pipelines so your automation stops breaking silently in production.
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Operators and teams we've worked with
Most Inngest implementations stall before they reach production reliability
Inngest's event-driven, durable execution model is genuinely different from a cron job or a simple queue, and that difference trips up most teams. They wire up a few step functions, everything looks fine in the dev server, then production surfaces the gaps: functions that silently time out past default limits, fan-out jobs that hammer downstream APIs because nobody set concurrency controls, event payloads that drift in shape and break downstream steps with no alerting, and AI agent chains with no failure boundary between steps. The dashboard shows retries happening, but nobody has defined what a terminal failure means or what should happen next.
Revenue Institute comes in at the architecture level, not the tutorial level. We map your event schema, define step boundaries that match your actual retry tolerance, configure concurrency and rate-limit controls against your third-party API ceilings, and build the dead-letter and alerting layer Inngest does not configure out of the box. The result is a workflow layer you can hand to an on-call engineer at midnight without a tribal-knowledge briefing.
What we do with Inngest
What we build inside your Inngest environment
Event schema design and versioning
We define your event catalog with explicit payload contracts - naming conventions, required versus optional fields, and a versioning strategy - so upstream changes do not silently feed malformed data into running function instances.
Step function boundary architecture
We map your business process to the right step boundaries so retries are idempotent, long-running waits do not consume unnecessary resources, and each unit of work is independently observable in the Inngest function run history.
Concurrency, throttle, and rate-limit configuration
We configure Inngest's concurrency keys and throttle controls to match your actual third-party API rate limits, preventing fan-out workflows from saturating a downstream CRM or payment API without slowing down the happy path.
AI agent and multi-step LLM orchestration
We structure multi-step AI pipelines so each LLM call is a discrete, retriable step - a failed tool call or context-window overflow does not silently corrupt downstream steps, and the full trace of an agent run is visible in Inngest's run history for debugging.
Failure handling and dead-letter strategy
We build explicit failure handlers, configure max retry counts per function, and route exhausted runs to a dead-letter event that triggers alerting or a human review queue - so nothing disappears into a failed state unnoticed.
Local dev to production deployment pipeline
Teams frequently ship functions that work locally and break in production due to environment variable differences, signing key mismatches, or missing serve handler configuration. We standardize your deployment pipeline across environments so the gap between dev server and production is explicit and tested.
Our framework
How an Inngest engagement with us runs
Audit and architecture
We review your existing Inngest functions, event schemas, and any queuing or cron infrastructure Inngest is meant to replace - flagging retry logic gaps, missing concurrency controls, and step boundaries that break at scale. You get a written architecture document first.
Build and instrumentation
We write or refactor your Inngest functions against the agreed architecture, configure the serve handler for your framework, set up concurrency and throttle rules, and add structured logging at each step boundary - so every run produces a readable trace in your observability stack.
Handoff and runbook
We document every function, event, and failure path in a runbook your team can use - walking engineers through the Inngest dashboard and defining the on-call playbook for the failure modes most likely to surface in your specific workload.
Why Inngest wins for durable workflow orchestration - and where it creates new operational debt
Inngest turns ordinary HTTP-served functions into durable, retriable, observable workflows without making you manage a message broker, worker pool, or separate scheduler. The step.run primitive checkpoints execution so a transient failure retries only the failed step, not the entire function. The step.sleep and step.waitForEvent primitives let a function pause for hours or days without holding a server thread. For mid-market teams duct-taping cron jobs to webhooks, this is a meaningful improvement, and the dev server's ability to replay real production events locally compresses the feedback loop most queue-based systems cannot.
The operational debt appears when teams treat Inngest as a drop-in cron replacement without rethinking function design. The most common failure mode is a single large function with no meaningful step boundaries, so every retry re-executes the whole function from the start - including side effects that already succeeded. The second is fan-out without concurrency controls: a triggering event spawns hundreds of child runs that simultaneously call a rate-limited API, producing a cascade of 429 errors that Inngest dutifully retries, making it worse. These are decisions the team has to make explicitly, and most do not know to make them until something breaks in production.
What production Inngest operations actually look like for a mid-market team
A well-run Inngest deployment has a few consistent traits. The event catalog is documented and versioned, so when a sending system changes its payload shape, the receiving function either handles it explicitly or fails loudly rather than silently processing bad data. Concurrency keys are set on any function touching a rate-limited API, with limits from actual API documentation rather than guesswork. Every function has a defined maximum retry count and an explicit failure handler that routes exhausted runs somewhere a human will see them - not just the dashboard where they accumulate unnoticed.
The observability layer matters more than most teams expect. Inngest's built-in run history is useful for debugging individual failures, but it does not replace structured logging at each step boundary that feeds your existing monitoring stack. Teams that skip this debug production incidents by clicking through the dashboard one run at a time - slow and unscalable. Revenue Institute wires structured logging into each step.run call from the start, so a failed run produces a traceable record in whatever tool the team uses - Datadog, Axiom, or a simpler logging service. The goal is a workflow layer an engineer who did not build it can diagnose and fix on a Sunday night without calling the original author.
We're vendor-agnostic
Other Workflow Automation platforms we specialize in
Not sure Inngest is the right fit? We implement and optimize these too - and we'll tell you honestly which one fits your business.
Inngest questions, answered
We already have some Inngest functions running. Can you work with what we have rather than starting over?
Yes, and that is the more common starting point. We audit what is already deployed, identify the specific gaps - usually around retry idempotency, missing concurrency controls, or undefined failure states - and fix those in place. We only recommend rebuilding a function from scratch when the existing step structure makes safe modification impractical, and we explain the reasoning before touching anything.
How is Inngest different from just using a message queue like SQS or a cron scheduler?
Inngest gives you durable execution with built-in step-level retries, sleep and wait primitives that do not hold a thread, a local dev server that replays real events, and a dashboard that shows the full run history of every function invocation. A message queue requires you to build all of that infrastructure yourself. The trade-off is that Inngest adds a network hop through its event API and requires your functions to be served over HTTP, which is a real architectural constraint to plan around.
What languages and frameworks does Inngest support, and does that affect what you can build for us?
Inngest has official SDKs for TypeScript, Python, and Go. The TypeScript SDK is the most mature and has the broadest feature coverage. If your backend is in a language without an official SDK, we will tell you directly rather than work around it. Most of our mid-market clients are on Node.js or TypeScript backends, which is where Inngest's production track record is strongest.
Can Inngest handle the volume our business runs at, or is it more of a startup tool?
Inngest is used in production at meaningful scale, but volume tolerance depends heavily on your function design. Poorly bounded fan-out, missing concurrency keys, and synchronous blocking inside step functions all compress your effective throughput. We size your architecture against your actual event volume before committing to a design, and we are honest if a different tool is a better fit for your specific workload profile.
We want to use Inngest to orchestrate AI agent workflows. Is that a realistic use case right now?
It is realistic and increasingly common, but it requires deliberate design. Each LLM call should be its own step so that retries do not re-run expensive prior steps, timeouts need to be set explicitly because LLM calls can run long, and you need a clear policy for what happens when a tool call fails or returns an unexpected schema. We have built these pipelines and know where the failure modes concentrate.
What does Revenue Institute charge for an Inngest engagement, and how long does it take?
Scope drives both. A focused audit and fix of an existing Inngest deployment is a shorter engagement than a ground-up architecture for a multi-event, multi-function workflow system. We scope after the initial discovery call, not before it, because the honest answer depends on your current state, your event volume, and how many downstream systems the functions touch. We do not quote from a rate card before we understand the problem.
Do we need to move our entire automation stack to Inngest, or can it coexist with our existing tools?
Inngest works well as a targeted layer for specific workflow types - particularly anything that needs durable retries, long waits, or multi-step fan-out - without replacing your entire stack. Many of our clients run Inngest alongside an existing iPaaS or internal queue. We help you define which workloads belong in Inngest and which do not, so you are not forcing every automation through a tool that is not the right fit for it.
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