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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$250M+
Pipeline generated
42%
Average pipeline growth
18.3%
Average budget saved
Results from actual client engagements.
Trusted by the teams we build 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, and then production surfaces the gaps: functions that silently time out past Inngest's 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 that have no meaningful failure boundary between steps. The Inngest dashboard shows retries happening, but nobody has defined what a terminal failure actually 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 that Inngest does not configure for you 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 so that as upstream systems change, Inngest functions do not silently receive malformed data. This includes naming conventions, required versus optional fields, and a versioning strategy that lets you evolve events without breaking running function instances mid-execution.
Step function boundary architecture
Inngest's step.run, step.sleep, and step.waitForEvent primitives are powerful but easy to misuse. We map your business process to the right step boundaries so that 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
Fan-out workflows that spawn hundreds of function runs can saturate a downstream CRM or payment API in seconds. We configure Inngest's built-in concurrency keys and throttle controls to match your actual third-party API rate limits, preventing cascading failures without slowing down the happy path.
AI agent and multi-step LLM orchestration
Inngest is increasingly used to orchestrate multi-step AI pipelines where each LLM call is a discrete, retriable step. We structure these pipelines so that a failed tool call or a context-window overflow does not silently corrupt downstream steps, and so that the full trace of an agent run is visible in Inngest's run history for debugging.
Failure handling and dead-letter strategy
Inngest retries by default, but it does not decide what a terminal failure means for your business. 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
Inngest's dev server is excellent for local iteration, but 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 that the gap between dev server and production is explicit and tested.
Our framework
How an Inngest engagement with us runs
Audit and architecture
We start by reviewing your existing Inngest functions, event schemas, and any queuing or cron infrastructure Inngest is meant to replace. We identify retry logic gaps, missing concurrency controls, and step boundaries that will cause problems at scale. You get a written architecture document before we write a line of code.
Build and instrumentation
We write or refactor your Inngest functions against the agreed architecture, configure the Inngest serve handler for your framework, set up concurrency and throttle rules, and add structured logging at each step boundary so that every function run produces a readable trace in the Inngest dashboard and in your existing observability stack.
Handoff and runbook
We document every function, event, and failure path in a runbook your team can actually use. We walk your engineers through the Inngest dashboard, explain how to read a failed run trace, and define 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's core value is straightforward: it turns ordinary HTTP-served functions into durable, retriable, observable workflows without requiring you to manage a message broker, a worker pool, or a separate scheduling service. The step.run primitive checkpoints execution so that 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 that have been duct-taping cron jobs to webhooks and hoping nothing drops, this is a meaningful architectural improvement. The Inngest dev server also deserves credit - being able to replay real production events locally against your function code compresses the feedback loop in a way that most queue-based systems do not offer.
The operational debt appears when teams treat Inngest as a drop-in replacement for a cron job without rethinking the function design. The most common failure mode is a single large function with no meaningful step boundaries, which means every retry re-executes the entire function from the start - including any side effects that already succeeded. The second most common failure mode is fan-out without concurrency controls: a triggering event spawns hundreds of child function runs that simultaneously call a downstream API with a rate limit, producing a cascade of 429 errors that Inngest dutifully retries, making the problem worse. Neither of these is a flaw in Inngest - they are design decisions the team has to make explicitly, and most teams 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 at the mid-market level has a few consistent characteristics. The event catalog is documented and versioned, so that when a sending system changes its payload shape, the receiving function either handles the new shape explicitly or fails loudly rather than silently processing bad data. Concurrency keys are set on any function that touches a rate-limited external API, and those limits are derived from the 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 into the Inngest 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 into your existing monitoring stack. Teams that skip this step find themselves debugging production incidents by clicking through the Inngest dashboard one run at a time, which is slow and does not scale. Revenue Institute wires structured logging into each step.run call from the start, so that a failed run produces a traceable record in whatever observability tool the team already uses - whether that is Datadog, Axiom, or a simpler logging service. The goal is a workflow layer that an engineer who did not build it can diagnose and fix on a Sunday night without calling the person who wrote the original function.
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.
Make Inngest actually earn its license fee.
Tell us your two biggest bottlenecks and we'll send back a custom Inngest implementation blueprint - by email, no call required.
- A specific plan for your Inngest stack, not a generic pitch
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