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Zapier and Make.com are genuinely good tools, until you hit conditional logic that branches more than two levels deep, an API that rate-limits at 100 requests per minute, or a workflow that needs to recover gracefully when one of eight steps fails. No-code tools don't handle that. They also don't integrate with internal systems that don't have connectors.
We build custom automation backends in Python and TypeScript. The output is a production codebase with error handling, retry logic, monitoring, and a runbook, not a Zap that silently fails at 3am and leaves records in an inconsistent state.
We also integrate AI classification and extraction into pipelines that need to make decisions, route this inbound document, extract these fields, determine which team should handle this record. The automation handles the mechanics; the model handles the judgment.
Tell us what you're building.
Four categories of workflow automation we build regularly. Each requires custom code because no-code tools either can't express the logic or can't scale to the volume.
Pipelines that handle partial failures gracefully: if step 3 of 8 fails, the system retries with exponential backoff, logs the failure, and notifies the right person. It does not silently drop the record.
Bidirectional or one-way data sync between systems with schema mismatches, data cleaning rules, deduplication logic, and conflict resolution. The kind of pipeline that Zapier's data mapper cannot express.
Workflows triggered by webhooks, database row changes, scheduled cron jobs, or message queue events. We design the trigger layer so your pipeline starts reliably and exactly once, no double-processing.
Pipelines where an LLM does the classification, extraction, or routing step: classify an inbound document, route it to the right team, extract structured fields, and write them to the target system, all without human intervention on the happy path.
We start with the failure modes, not the happy path. That's what separates automation that works in production from automation that works in demos.
01
We document the current manual process step by step, identify every decision point and conditional branch, and enumerate the edge cases and failure modes. This document becomes the acceptance criteria for the build.
02
We design the pipeline architecture, which systems connect, how credentials are managed, what the retry and error-handling strategy is, and how the pipeline is monitored. We choose Python or TypeScript based on your team's existing stack.
03
We build against a staging environment with representative data, not production. Every edge case from the failure mode analysis gets a test. We do not hand off code that passes only the happy path.
04
We deploy to infrastructure you own, set up monitoring and alerting, write the runbook, and hand off the codebase. Your team can extend or modify the pipeline without us.
No-code tools are excellent right up to a specific ceiling. The teams who come to us have usually hit it: the automation works most of the time, fails in ways nobody can see, and has become more fragile than the manual process it replaced. Four limits cause most of that.
Logic and state outgrow a filter step. Real processes branch, loop, wait on external state, and depend on what happened three steps ago. Expressing that in a chain of filter steps becomes a tangle nobody can safely change. We build it as actual code with a clear state model, so the branching that matters to your business is explicit and testable rather than buried in a visual editor.
Failure handling has to be real. The expensive failure is the silent one: a step errors at 3am, the record is left half-processed, and nobody notices until a customer does. We build bounded retries, idempotency so a re-run never double-processes, and dead-letter handling so a failed item is parked and surfaced rather than dropped. The pipeline fails loudly and recovers cleanly.
Some systems have no connector. The internal tool, the legacy ERP, the partner API with quirky auth and undocumented rate limits, these are exactly the systems a workflow most needs to touch and the ones no-code platforms do not support. We integrate against the real API, handle its authentication and rate limits properly, and are not blocked when a vendor never built a connector.
At volume, the economics flip. Per-task pricing is cheap at a few hundred runs a month and painful at a few hundred thousand. A pipeline processing real volume often costs more in no-code task fees than a custom build would cost to run outright, and you gain the control and observability the platform never gave you. We run that comparison on your numbers before recommending a build.
Custom automation is the right choice for specific situations. Here is when you probably don't need it.
Teams whose automation fits inside Zapier's simple trigger-action model
If your workflow is “when a new row appears in Sheet A, create a record in HubSpot” with no conditional logic and low volume, you don't need us. Zapier does that reliably for $50/month.
Teams without a developer to receive the codebase
We deliver a production codebase, not a managed service. Your team needs at least one person who can deploy to a server, update environment variables, and read a Python traceback when something goes wrong. If no one on your team can do that, we are not the right fit.
Describe the process you want to automate, the trigger, the steps, the edge cases, and the systems involved. We'll reply within one business day with a rough scope and price range.