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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Operators and teams we've worked 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. When a workflow spans multiple services - an onboarding sequence touching your CRM, billing system, provisioning layer, and email provider - any step can fail silently, leave state inconsistent, or trigger duplicate side effects. Temporal's durable execution model solves this: workflow code runs as if it never fails because Temporal persists every step's state and replays execution automatically after any crash or timeout.
The problem is that Temporal's programming model - activities, signals, queries, child workflows, and the deterministic code requirement - has a real learning curve. Teams that skip the architectural groundwork ship workflows that are hard to test, impossible to version, and painful to debug. Revenue Institute gets you past that curve. We audit your automation pain points, map them to Temporal's execution primitives, and build for maintainability - proper retry policies, heartbeating, versioning via the patching API, and worker configuration tuned to your load profile.
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 from the start - separating orchestration logic in workflow code from side-effectful operations in activities, setting 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 a failed step cleanly rolls back prior steps - transactional guarantees across services with no shared database.
Worker fleet configuration and scaling
Temporal separates the server from your workers, and misconfiguring worker concurrency, task queue routing, or sticky execution is a common production problem. We size your worker fleet for actual throughput, set up separate task queues per SLA, and document the scaling triggers 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 patching API so you can deploy new logic without breaking in-flight executions - a critical capability 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 operational use, and set up alerting on execution latency, activity failure rates, and stuck workflows - so you see what needs attention without digging through raw event histories.
AI agent and long-running automation orchestration
Temporal is the backbone for multi-step AI agent workflows where calls to AI models, tool executions, and human-in-the-loop approvals must be orchestrated reliably over minutes or hours. We design these using Temporal signals and queries to handle async approvals without polling loops or fragile webhooks.
Our framework
How a Temporal engagement with us runs
Discovery and mapping
We map your existing automation landscape - what runs on cron, what is queued in SQS or RabbitMQ, what fails silently, and which business processes carry the highest cost of failure - then map the top three to five workflows to Temporal primitives before writing code.
Build and review
We implement workflows in your language of choice using Temporal's SDK, structure activities and workers correctly, write deterministic code that passes replay testing, and set retry and timeout policies per integration - with code review focused on determinism, idempotency, and 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 covering how to add new workflows, use the patching API, and diagnose stuck or failed executions - so you own and operate the system independently.
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 records every event in a workflow's lifecycle to a persistent event history and replays it to reconstruct state after any crash or restart. You can write a workflow that sends an email, waits three days, calls a billing API, and provisions a resource - and if the worker dies, execution resumes exactly where it left off without a single line of checkpoint or retry code. That is a meaningful improvement over cron jobs, manual dead-letter handling, or Step Functions state machines requiring JSON definitions for every branch.
The adoption cost is real. Temporal's determinism requirement - 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, or making a direct HTTP request inside workflow code all violate determinism and cause bugs that only surface after a failure in production. The fix is to move non-deterministic operations into activities. Teams that skip Temporal's workflow replayer ship code that works in development and breaks 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 directly impacts throughput and cost: max concurrent workflow task and activity task settings control parallel execution counts, and misconfiguring either wastes capacity or causes resource exhaustion. Task queue design matters - routing high-priority workflows to dedicated queues prevents low-priority background jobs from starving customer-facing workflows. Teams that skip learning how to interpret event histories and diagnose timeouts accumulate stuck executions they cannot resolve.
Versioning is the other reality teams underestimate. Once a workflow is in production, you cannot change its code in a way that alters the event sequence - doing so causes replay failures for in-flight executions. Temporal's patching API lets you introduce conditional branches running new logic for new executions while preserving old behavior, but using it correctly requires a clear deprecation process. Teams that skip a versioning practice end up unable to deploy changes without waiting for in-flight executions to complete or forcing terminations. Getting this right from the start is one of the highest-value things an implementation partner brings.
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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