Durable execution for AI workflows
Trusted by 100+ innovative teams
What we build
We implement Temporal, BullMQ, Inngest, and custom orchestration layers that make your AI backend durable, observable, and scalable, so agent failures become retries, not outages.
Built for teams like yours
How we deliver
Map your workflows, identify high-impact opportunities, and quantify ROI potential.
Build a focused MVP for your highest-impact use case in 4-6 weeks.
Harden, monitor, and expand — leveraging existing infrastructure for each new capability.
4-8 weeks
pilot to production
95%+
milestone adherence
99.3%
SLA stability
AI Workflow Orchestration Implementation
Use the same rollout pattern we apply in production programs: architecture review, risk controls, and measurable milestones from pilot to scale.
4-8 weeks
pilot to production timeline
95%+
delivery milestone adherence
99.3%
observed SLA stability in ops programs
Deep dive
Modern AI features rarely live in a single LLM call. A production AI feature is usually a multi-step workflow: validate input, fetch context, embed and retrieve, call the model, post-process, persist results, retry on failure. When that workflow runs across services with timeouts, network failures, and partial successes, you need durable execution — not just a queue.
Workflow orchestration is the layer that makes long-running, multi-step AI workflows reliable in production. We help engineering teams choose the right orchestration layer and ship workflows that survive the failure modes naive architectures don't anticipate.
A common pattern: drop AI tasks onto a Redis queue, have workers process them. This works for short, idempotent tasks. It breaks down quickly for real AI workflows.
These are solved problems in workflow engines. The point is to use the right layer rather than rebuild it badly.
Temporal is the production choice when workflows are long-running, span multiple steps, or need strong guarantees. The mental model: workflows are code that survives crashes. The Temporal server replays workflow state from a durable history, so your workflow code can run for hours, days, or weeks without losing progress on a crash.
Where Temporal shines:
The cost: operational complexity. Running Temporal yourself means a Cassandra/PostgreSQL cluster, the Temporal server, and worker processes. Temporal Cloud removes the ops burden at a per-action price.
BullMQ — the modern, TypeScript-first successor to Bull — sits at the lighter end of the spectrum. It runs on Redis and gives you queues, scheduled jobs, repeatable jobs, and basic flow composition. For Node/TypeScript teams already using Redis, the operational footprint is essentially zero.
Where BullMQ fits:
The limit: BullMQ does not durably store workflow state. If a worker crashes mid-LLM-call, you re-enqueue the job and start over. For 30-second tasks that is fine. For 30-minute multi-step workflows, it is not.
Inngest is a newer entrant that treats workflows as functions composed of discrete steps. Each step's result is durably stored, so resuming after a crash skips already-completed steps. The developer experience is closer to "regular code" than Temporal — you write step.run handlers and Inngest handles the durability.
Where Inngest fits:
Inngest Cloud is the managed offering; self-hosted is available for compliance-sensitive deployments.
The decision usually comes down to three questions:
We have shipped production workflows on all three. The wrong tool is the one chosen by familiarity rather than fit.
A few patterns we use across most engagements:
For most engagements, we typically run workflow orchestration as a 4–8 week engagement. Week 1 is workflow discovery — mapping your real workflows, their failure modes, and their cost profile. Weeks 2–6 are implementation: orchestration layer setup, workflow code, observability, and load testing. Weeks 7–8 are hardening: chaos testing, cost ceilings, runbook handoff.
The deliverable is a system the client team can operate. We invest heavily in observability — per-step traces, retry visibility, cost telemetry — because workflow systems that nobody can debug end up replaced within 6 months.
The wrong orchestration layer compounds slowly — it works in development, mostly works in staging, and fails at the worst possible time in production. The right one fades into the background and lets the team ship features.
Not necessarily. If your pipeline is straightforward (2 to 3 steps, short execution) and failures are rare, BullMQ or simple async processing is sufficient. We evaluate your workflow complexity, failure patterns, and reliability requirements before recommending. Many teams start with BullMQ and only move to Temporal when workflow complexity demands it.
A basic Temporal setup with 2 to 3 AI workflows takes 3 to 4 weeks. A full implementation with multiple workflow types, observability dashboards, and production hardening takes 6 to 8 weeks. BullMQ implementations are faster, typically 1 to 2 weeks for a production-ready setup.
Yes. We integrate orchestration layers into existing AI backends without a full rewrite. We identify the workflows that benefit most from durable execution, extract them into Temporal or BullMQ jobs, and connect them to your existing services. This incremental approach minimizes disruption.
We offer ongoing management as a separate retainer. Most teams prefer Temporal Cloud for managed infrastructure and handle their own worker deployments after enablement. We provide runbooks and on-call support options for teams that need help during the initial production period. We can also set up monitoring and alerting so your team has the observability to self-manage with confidence.
Frequent iteration is exactly the scenario where orchestration architecture matters most. We implement Temporal's workflow versioning pattern so you can deploy pipeline changes without breaking in-flight executions. For BullMQ, we design the job structure to isolate change impact. Either way, the orchestration layer is designed to accommodate the iteration velocity typical of early-stage AI product development.
Explore related services, insights, case studies, and planning tools for your next implementation step.
Delivery available from Bengaluru and Coimbatore teams, with remote implementation across India.
Case Studies
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Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002