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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Operators and teams we've worked 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 teams hit the same wall: they prototype a job in a day then spend weeks wrestling with concurrency limits, fan-out patterns flooding the queue, and idempotency gaps causing duplicate side effects. The dashboard shows a run failed; it does not tell you why your CRM sync created the same contact three times.
Revenue Institute scopes implementations correctly from the start. We audit your job definitions, identify retry and concurrency anti-patterns, restructure task graphs to use Trigger.dev's wait and checkpoint primitives, and wire up alerting so operations teams know when something breaks. The result is a background job layer your team can maintain 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 - what belongs in a single task, what should fan out into child tasks, and where to use wait primitives versus external polling. Poor task decomposition is the top reason jobs become unmaintainable within months.
Durable execution and retry strategy
Trigger.dev's durable execution lets a job survive a server restart mid-run, but only if checkpoints are structured correctly. We implement retry policies, idempotency keys, and failure branches per job so a transient API error does not corrupt records or send 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 - calls to AI models, tool use, structured output validation, and conditional branching - using Trigger.dev's native AI task support so runs stay observable and resumable.
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 file upload, a scheduled cron - without polling loops or fragile Zapier chains inside a critical revenue process.
Concurrency controls and queue management
Unbounded concurrency gets you 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 execution history and logs per task. We extend it with structured log outputs, failure alerting routed to Slack or PagerDuty, and runbooks so an operations manager - not just the engineer who built it - can escalate a 2 a.m. failure.
Our framework
How a Trigger.dev engagement runs
Audit and scope
We review your existing Trigger.dev jobs, event sources, and the downstream systems each job touches. We identify retry gaps, missing idempotency, concurrency risks, and jobs silently failing or producing duplicate writes - then deliver a prioritized fix list before writing code.
Build and stabilize
We refactor or build jobs using Trigger.dev's TypeScript SDK, structuring tasks, waits, and child task fan-outs for your workload. Every job ships with retry logic, idempotency handling, and structured logging, tested 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. We walk your engineers through the architecture so they can extend it independently. Ongoing retainer support is available 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 ceiling with existing tools. Zapier and Make work for simple linear automations but fall apart when a workflow needs conditional branching, an AI model call, or a runtime beyond a few seconds. Standard serverless functions on Lambda or Vercel have tight time limits and no durability - if the function crashes mid-run, the work is lost. Trigger.dev solves both: durable, resumable TypeScript background jobs on managed infrastructure with a real-time dashboard showing every task, retry, and log line.
Its concurrency controls, queue management, and native support for long-running AI tasks make it the right fit for operations outgrowing no-code automation but not ready for a Temporal cluster or self-hosted BullMQ. Trigger.dev rewards careful task decomposition and idempotency upfront. Teams that treat it like a fancier cron runner end up with jobs that are hard to debug, prone to duplicate side effects, and difficult to extend.
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 is readable without deep context. Retry policies are set per task based on the failure modes of the API or database being called. Every task writing to an external system uses an idempotency key so a retry does not create a duplicate CRM record or send a second confirmation email. Long-running jobs use wait primitives to checkpoint progress.
Observability is where most teams underinvest. Trigger.dev's run dashboard serves engineers, but operations teams need alerts in the tools they already watch - Slack, PagerDuty, a shared inbox - and need to know which records were affected, not just that a job failed. The teams that get the most from Trigger.dev treat job failure as an operational event: structured log outputs, failure routing, and runbooks written before a job ships 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.
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