Workflow Automation - Trigger.dev
Background jobs that ship to production
without breaking everything else
We design, build, and stabilize Trigger.dev workflows for mid-market operations teams - covering event-driven jobs, long-running tasks, and AI agent orchestration so your engineers stop firefighting queues and start shipping.
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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 Trigger.dev builds stall before they reach reliable production
Trigger.dev is genuinely powerful - TypeScript-native background jobs, durable execution, built-in retries, real-time run visibility, and first-class support for long-running AI tasks. But mid-market engineering teams consistently hit the same wall: they prototype a job in a day and then spend weeks wrestling with concurrency limits, fan-out patterns that flood the queue, idempotency gaps that cause duplicate side effects, and environment parity issues between local dev and the cloud runner. The observability dashboard shows you that a run failed; it does not tell you why your CRM sync wrote the same contact three times or why your nightly enrichment job silently timed out at step four of twelve.
Revenue Institute brings Trigger.dev implementations that are scoped correctly from the start. We audit your existing job definitions, identify retry and concurrency anti-patterns, restructure task graphs so long-running workflows use Trigger.dev's wait and checkpoint primitives properly, and wire up alerting so operations teams - not just engineers - know when something is wrong. The result is a background job layer your team can actually maintain and extend without a specialist on call.
What we do with Trigger.dev
What we build inside your Trigger.dev environment
Job architecture and task graph design
We map your operational workflows to Trigger.dev's task model - deciding what belongs in a single task, what should fan out into child tasks, and where to use Trigger.dev's built-in wait primitives versus external polling. Poor task decomposition is the most common reason jobs become unmaintainable within three months of launch.
Durable execution and retry strategy
Trigger.dev's durable execution model means a job can survive a server restart mid-run, but only if you structure checkpoints correctly. We implement retry policies, idempotency keys, and failure branches for each job so a transient API error does not corrupt downstream records or trigger duplicate notifications.
Long-running AI agent task orchestration
Trigger.dev is purpose-built for AI workloads that exceed normal serverless timeouts. We build multi-step agent pipelines - LLM calls, tool use, structured output validation, and conditional branching - using Trigger.dev's native AI task support so runs stay observable and resumable rather than disappearing into a black box.
Event-driven trigger configuration
We connect your CRM, data warehouse, webhooks, and internal APIs to Trigger.dev's event system so jobs fire on the right signals - a deal stage change, a new file upload, a scheduled cron - without polling loops or fragile Zapier chains sitting in the middle of a critical revenue process.
Concurrency controls and queue management
Unbounded concurrency is the fastest way to get rate-limited by every third-party API your jobs touch. We configure Trigger.dev's concurrency limits and queue priorities so high-value jobs run immediately, batch enrichment jobs yield to them, and your API quotas stay intact across all environments.
Observability, alerting, and runbooks
Trigger.dev's run dashboard gives you execution history and logs per task. We extend that with structured log outputs, failure alerting routed to Slack or PagerDuty, and written runbooks so an operations manager - not just the engineer who built the job - can diagnose and escalate a failure at 2 a.m.
Our framework
How a Trigger.dev engagement runs
Audit and scope
We review your existing Trigger.dev jobs, your event sources, and the downstream systems each job touches. We identify retry gaps, missing idempotency, concurrency risks, and any jobs that are silently failing or producing duplicate writes. You get a prioritized fix list before we write a line of code.
Build and stabilize
We refactor or build jobs using Trigger.dev's TypeScript SDK, structuring tasks, waits, and child task fan-outs correctly for your workload. Every job ships with retry logic, idempotency handling, and structured logging. We test against your staging environment with realistic load before touching production.
Handoff and documentation
We deliver working jobs, environment configuration, alerting rules, and plain-language runbooks your team can follow. We walk your engineers through the architecture so they can extend it without us. Ongoing retainer support is available if you want a partner for the next phase of automation.
Why Trigger.dev earns a place in a mid-market automation stack
Most mid-market teams reach for Trigger.dev after hitting the same ceiling with their existing tools. Zapier and Make work for simple linear automations but fall apart when a workflow needs to branch conditionally, call an AI model, process a large batch, or run for longer than a few seconds. Standard serverless functions on AWS Lambda or Vercel have tight execution time limits and no built-in durability - if the function crashes mid-run, the work is lost and you have no record of where it stopped. Trigger.dev solves both problems. It gives you durable, resumable background jobs written in TypeScript, deployed to managed infrastructure, with a real-time execution dashboard that shows you every task, every retry, and every log line for every run.
The platform's concurrency controls, built-in queue management, and native support for long-running AI tasks make it a practical choice for operations that are outgrowing no-code automation but are not ready to build and maintain a full Temporal cluster or a self-hosted BullMQ setup. The trade-off is that Trigger.dev rewards teams who think carefully about task decomposition and idempotency upfront. Teams that treat it like a slightly fancier cron runner end up with jobs that are hard to debug, prone to duplicate side effects, and difficult to extend as the business changes.
What production-grade Trigger.dev actually looks like
A well-built Trigger.dev implementation has a few consistent characteristics. Each job is decomposed into discrete tasks with clear inputs and outputs, so the execution graph in the dashboard is readable by someone who did not write the code. Retry policies are set per task based on the failure modes of the specific API or database being called - not left at the default. Every task that writes to an external system uses an idempotency key so a retry after a transient failure does not create a duplicate record in your CRM or send a customer a second confirmation email. Long-running jobs use Trigger.dev's wait primitives to checkpoint progress rather than holding a connection open and hoping nothing times out.
Observability is the part most teams underinvest in. Trigger.dev's run dashboard is useful for engineers, but operations teams need alerts routed to the tools they already watch - Slack channels, PagerDuty, or a shared inbox. They need to know not just that a job failed, but which records were affected and what the next step is. The teams that get the most out of Trigger.dev are the ones who treat job failure as an operational event that needs a response, not just a log entry that an engineer will eventually notice. That means structured log outputs, failure routing, and runbooks written before the job goes to production - not after the first incident.
We're vendor-agnostic
Other Workflow Automation platforms we specialize in
Not sure Trigger.dev is the right fit? We implement and optimize these too - and we'll tell you honestly which one fits your business.
Trigger.dev questions, answered
We already have some Trigger.dev jobs running. Can you improve what we have rather than rebuild from scratch?
Yes, and that is usually the right starting point. We audit your existing job definitions first and identify which ones have structural problems - missing retries, no idempotency, unbounded concurrency - versus which ones just need better observability. A full rebuild is rarely necessary. Most engagements are part refactor, part net-new build for workflows that do not exist yet.
How is Trigger.dev different from just using a queue like BullMQ or a workflow tool like Temporal?
Trigger.dev sits in a specific position: it is TypeScript-native, deploys without you managing your own queue infrastructure, and has first-class support for long-running and AI workloads that would time out on a standard serverless function. BullMQ requires you to run and maintain Redis. Temporal is more powerful but carries significant operational overhead. Trigger.dev is the right fit when your team wants durable background jobs without running infrastructure, and when your jobs involve AI calls or multi-step processes that can run for minutes or hours.
What kind of mid-market use cases do you typically implement on Trigger.dev?
The most common ones we see are CRM enrichment pipelines that run on new contact creation, nightly data sync jobs between a product database and a data warehouse, AI-driven document processing workflows, post-sale onboarding automation triggered by deal stage changes, and multi-step lead scoring jobs that call several APIs in sequence. Any process that is currently running inside a cron script, a fragile Zapier chain, or a serverless function that keeps timing out is a candidate.
Our engineering team is small. Will they be able to maintain Trigger.dev jobs after you hand off?
That is a real concern and we design for it explicitly. Trigger.dev's TypeScript SDK means your developers work in a language they already know. We structure jobs so each task has a single clear responsibility, write inline comments explaining non-obvious retry decisions, and deliver runbooks that describe what to do when a specific job fails. A junior developer should be able to extend an existing job without needing to understand the full architecture from scratch.
Does Trigger.dev work well with AI frameworks like LangChain or the Vercel AI SDK?
Yes. Trigger.dev has built-in support for long-running AI tasks and integrates cleanly with the Vercel AI SDK, OpenAI's API, and Anthropic's API. The key advantage over running AI calls inside a standard API route or serverless function is that Trigger.dev jobs can run for much longer, survive infrastructure interruptions mid-run, and give you a full execution log of every step. We have built multi-step agent workflows on Trigger.dev where each tool call is a discrete task with its own retry policy and log output.
What does a typical engagement cost and how long does it take?
We do not publish fixed pricing because scope varies significantly - a single job refactor is a different engagement from building a full event-driven automation layer across your CRM, data warehouse, and product database. After an initial audit call we give you a scoped proposal with a fixed price and timeline. Most focused builds are complete within four to eight weeks. We will tell you honestly if your problem is smaller than you think and does not need a full engagement.
Can you connect Trigger.dev to our existing CRM or data warehouse without a full re-architecture?
Usually yes. Trigger.dev jobs are just TypeScript, so they can call any API, connect to any database, or consume any webhook your existing systems already expose. We do not need to replace your CRM or your warehouse - we build jobs that read from and write to them using their existing APIs or direct database connections. The most common integration work involves setting up reliable event triggers and making sure writes are idempotent so a retry does not create duplicate records.
Make Trigger.dev actually earn its license fee.
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