Workflow Automation - Temporal
Distributed workflows that survive
infrastructure failures without custom retry logic
We design, build, and operationalize Temporal workflows for mid-market teams running complex multi-step processes - so a crashed server or a flaky API no longer kills a business-critical job halfway through.
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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
Home-grown retry logic and cron jobs are costing you more than you think
Most mid-market engineering teams reach a point where their automation stack is a patchwork of cron jobs, message queues, and hand-rolled retry logic stitched together with hope. When a workflow spans multiple services - say, a customer onboarding sequence that touches your CRM, billing system, provisioning layer, and email provider - any one of those steps can fail silently, leave state inconsistent, or trigger duplicate side effects when someone manually reruns the job. Temporal's durable execution model solves this at the foundation: your workflow code runs as if it never fails, because Temporal persists every step's state in its event history and replays execution automatically after any crash or timeout. The problem is that Temporal's programming model - activities, workflows, signals, queries, child workflows, and the distinction between deterministic and non-deterministic code - has a real learning curve, and teams that skip the architectural groundwork ship workflows that are hard to test, impossible to version safely, and painful to debug in production.
Revenue Institute brings the implementation experience to get you past that curve fast. We audit your existing automation pain points, map them to Temporal's execution primitives correctly, and build workflows that are structured for long-term maintainability - including proper activity retry policies, heartbeating for long-running tasks, versioning strategies using Temporal's patching API, and worker configuration tuned to your actual load profile. You get durable automation without the months of trial and error.
What we do with Temporal
What we build inside your Temporal environment
Workflow architecture and activity design
We map your business processes to Temporal's execution model correctly from the start - separating orchestration logic in workflow code from side-effectful operations in activities, setting appropriate start-to-close and schedule-to-start timeouts, and defining retry policies per activity type so transient failures recover automatically without duplicating work.
Saga pattern implementation for distributed transactions
When a multi-step process touches several external systems, partial failures leave data inconsistent. We implement saga compensation logic inside Temporal workflows so that if step four fails, steps one through three are cleanly rolled back or compensated - giving you transactional guarantees across services that have no shared database.
Worker fleet configuration and scaling
Temporal separates the server from your worker processes, and misconfiguring worker concurrency, task queue routing, or sticky execution settings is a common production problem. We size and configure your worker fleet for your actual throughput, set up separate task queues for workflows with different SLA requirements, and document the scaling triggers your team needs to watch.
Versioning and safe workflow migration
Temporal's determinism requirement means you cannot change running workflow code arbitrarily. We implement versioning using Temporal's built-in patching API and workflow versioning patterns so you can deploy new logic without breaking in-flight executions - a critical capability that teams often skip until it causes a production incident.
Observability and Temporal Web UI instrumentation
We wire your workflows to emit structured metrics and traces, configure Temporal's Web UI for your team's operational use, and set up alerting on workflow execution latency, activity failure rates, and stuck workflows. You get operational visibility into what is running, what is waiting, and what needs attention - without digging through raw event histories.
AI agent and long-running automation orchestration
Temporal is increasingly the backbone for multi-step AI agent workflows where individual LLM calls, tool executions, and human-in-the-loop approval steps need to be orchestrated reliably over minutes or hours. We design these patterns using Temporal signals and queries to handle async human approvals and external triggers without polling loops or fragile webhooks.
Our framework
How a Temporal engagement with us runs
Discovery and mapping
We spend the first phase understanding your existing automation landscape - what is running on cron, what is queued in SQS or RabbitMQ, what is failing silently, and what business processes have the highest cost of failure. We identify the three to five workflows that will deliver the most operational stability and map them to Temporal primitives before writing a line of code.
Build and review
We implement the target workflows in your language of choice using Temporal's SDK, structure activities and workers correctly, write deterministic workflow code that passes replay testing, and set retry and timeout policies appropriate to each integration. We conduct code review focused on Temporal-specific correctness - determinism, idempotency, and proper use of side effects.
Handoff and enablement
We deploy to your environment, validate production behavior using Temporal's event history and Web UI, and run working sessions with your engineering team covering how to add new workflows, how to use the patching API for future changes, and how to diagnose stuck or failed executions. You own the system and can operate it without us.
Why Temporal's programming model is both its strength and its steepest adoption cost
Temporal's core promise is durable execution: your workflow function runs as if the underlying infrastructure never fails. Temporal achieves this by recording every event in a workflow's lifecycle to a persistent event history and replaying that history to reconstruct workflow state after any crash or restart. The result is that you can write a workflow that sends an email, waits three days for a response, calls a billing API, and provisions a resource - and if the worker process dies at any point, execution resumes exactly where it left off without you writing a single line of checkpoint or retry code. For mid-market teams running complex multi-step processes across several systems, that is a meaningful operational improvement over cron jobs, message queues with manual dead-letter handling, or Step Functions state machines that require JSON definitions for every branch.
The adoption cost is real, though. Temporal's determinism requirement - the rule that workflow code must produce the same output given the same event history on every replay - catches teams off guard. Calling a random number generator, reading the current time with a standard library call, or making a direct HTTP request inside workflow code all violate determinism and cause subtle, hard-to-diagnose bugs that only surface when a workflow replays after a failure. The fix is to move all non-deterministic operations into activities, but understanding where that boundary sits and how to test for violations requires experience with the execution model. Temporal provides a workflow replayer for testing, but teams that skip this step ship workflows that appear to work in development and break in production under real failure conditions.
What production Temporal operations actually look like at mid-market scale
Running Temporal in production involves more than deploying the server and writing workflows. Worker configuration has a direct impact on throughput and cost: the max concurrent workflow task and activity task settings on each worker control how many executions run in parallel, and setting these too high causes resource exhaustion while setting them too low leaves capacity unused. Task queue design matters as well - routing high-priority workflows to dedicated task queues with dedicated workers prevents a surge of low-priority background jobs from starving time-sensitive customer-facing workflows. Temporal's Web UI gives you visibility into running and completed executions, but interpreting event histories and diagnosing stuck workflows requires familiarity with how Temporal schedules tasks and handles timeouts. Teams that treat Temporal as a black box and skip this operational knowledge accumulate a backlog of stuck executions they do not know how to resolve.
Versioning is the other operational reality that mid-market teams underestimate. Once a workflow is running in production, you cannot change its code in a way that alters the sequence of events it produces - doing so causes replay failures for in-flight executions. Temporal's patching API lets you introduce conditional branches that execute new logic for new executions while preserving old behavior for existing ones, but using it correctly requires discipline and a clear deprecation process. Teams that do not establish a versioning practice early end up unable to deploy changes to long-running workflows without waiting for all in-flight executions to complete or forcing terminations. Getting this right from the start is one of the highest-value things an experienced implementation partner brings to a Temporal engagement.
We're vendor-agnostic
Other Workflow Automation platforms we specialize in
Not sure Temporal is the right fit? We implement and optimize these too - and we'll tell you honestly which one fits your business.
Temporal questions, answered
We already use AWS Step Functions. Why would we switch to Temporal?
Step Functions is a managed service with low operational overhead, which is a real advantage. Temporal wins when your workflows need to run for hours or days, when you want to write orchestration logic in code rather than JSON state machine definitions, or when you need features like signals, queries, and child workflows that Step Functions does not support natively. The trade-off is that Temporal requires you to run and operate the server yourself or use Temporal Cloud. If your workflows are simple and short-lived, Step Functions may be the right call.
Should we use Temporal Cloud or self-host the Temporal server?
For most mid-market teams, Temporal Cloud removes a meaningful operational burden - you are not managing Cassandra or PostgreSQL persistence, cluster upgrades, or server availability. The cost is a usage-based fee on top of your compute. Self-hosting makes sense if you have strict data residency requirements, an existing platform engineering team comfortable with the operational overhead, or very high workflow volumes where the economics shift. We help you model both options against your actual requirements before you commit.
How long does a typical Temporal implementation take?
A focused engagement covering two to three production workflows - including architecture, build, testing, and team enablement - typically runs four to eight weeks depending on the complexity of the integrations involved. The longer end applies when workflows touch many external systems with inconsistent APIs or when the team needs more depth on the programming model. We scope based on your specific workflows, not a generic estimate.
Our engineering team is small. Can they maintain Temporal workflows after you leave?
Yes, and that is explicitly part of how we structure the engagement. Temporal's SDK is standard Go, Java, Python, or TypeScript code - your engineers do not need to learn a proprietary DSL. We document the patterns we use, run working sessions on the Temporal-specific concepts that trip people up (determinism, versioning, activity idempotency), and leave the team with runbooks for the operational tasks they will actually encounter.
What does Temporal actually persist, and is our data safe inside it?
Temporal persists workflow event histories - the inputs, outputs, and state transitions of your workflow executions. This means sensitive data passed as workflow or activity inputs is stored in the Temporal persistence layer. For regulated industries or sensitive payloads, the standard approach is to pass references (IDs, storage keys) rather than raw data as workflow inputs, and retrieve the actual data inside activities. We design your data handling patterns with this in mind from the start.
Can Temporal orchestrate workflows that involve human approval steps?
Yes, and this is one of Temporal's stronger use cases. You can pause a workflow indefinitely waiting for a signal - an external event sent to the workflow via Temporal's API - and resume execution when the signal arrives. This is how you implement human-in-the-loop steps like approval gates, document review, or exception handling without polling a database or building a separate state machine. We implement these patterns regularly for onboarding, compliance, and AI agent workflows.
We have existing workflows running in production. Do we have to rewrite everything at once?
No. Temporal works well as an incremental adoption - you can migrate one workflow at a time while leaving others on their existing infrastructure. The typical starting point is the workflow with the highest failure cost or the most complex retry logic, not a big-bang rewrite. We help you identify the right first candidate and build a migration path that does not require taking existing automation offline.
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