Automations that don’t silently die.
Everyone demos a workflow canvas. The canvas is the easy part. What decides whether you trust automation with revenue is what happens on day 40: when a run is mid-wait and you edit the workflow, when a provider times out, when someone asks why a customer got two emails. And before any of that: whether the person who knows the process can build it at all.
- Runs survive restarts
- Yes
- In-flight runs on edit
- Finish on original version
- Per-run execution trace
- Every step
- Failed step handling
- Retry with backoff, then alert
- Build by describing it
- Typed, dictated, or by voice
- Test runs
- Nothing sent, nothing changed
Describe it, and it’s built
Type what you want (or press the mic and say it, or talk it through out loud) and UnifyLoop drafts the automation using your real pipelines, fields, team, and connected channels. It asks a short question when a choice genuinely matters, tells you what it assumed, and flags anything that sends messages. Nothing goes live until you review it and press Publish.
- Typed, dictated, or a spoken conversation that builds the flow while you talk
- Built from your own data, so it can’t invent a field or a stage that doesn’t exist
- Every draft is checked by the same validator as a hand-built workflow, and repaired before you see it
- Change it by asking (“also tell me in Slack when the deal is over $10k”) and accept the change on the canvas
- When a run fails, ask why, and get the cause and a suggested fix in plain English
Durable: a run in progress is a stored fact
Every step a run completes is persisted before the next one starts. A deploy, a restart, or a crashed worker doesn’t lose a run; it picks up from the last completed step. A workflow that waits three days for a reply is genuinely waiting three days, not holding something fragile in memory and hoping nothing changes.
- Triggers: a record created, updated, a specific field changed, moved stage, or past its SLA; a reply received; an email opened, clicked, or bounced; someone unsubscribed; a meeting booked or cancelled; a call completed; a segment entered; a form or app posting to a private webhook; a set number of days before or after a date on the record; or a daily, weekly, or monthly schedule
- Steps: branch, A/B split, “continue only if”, wait, wait until business hours, wait for a reply, find, create, or update records, move a stage, add to or remove from a list, add a note, assign an owner by round-robin, create a task, send an email or an approved WhatsApp template, notify a teammate (in the app or by email), post to Slack, start another automation, call a signed webhook or any API, and AI steps that draft text, sort a reply into the right path, or pull details out of it
- Thirty ready-made templates across speed to lead, nurture, meetings, pipeline, retention, reviews, team alerts, and website forms
- Goals end a sequence the moment someone replies or books, and suppression lists keep customers out of prospecting flows
Versioned: editing a workflow doesn’t rewrite history
When you publish a change, runs already in flight continue on the version they started on, and new runs use the new version. This is the failure mode that makes teams stop trusting automation elsewhere: you fix a typo in step two, and a thousand people sitting in a step-six wait are silently moved onto a path that was never designed for them. Each run records which version it executed.
- In-flight runs complete on their original version
- Publishing is explicit: drafts don’t affect live runs, and old versions can be restored
- Every run keeps a step-by-step trace with inputs, outputs, and timings
- Failed steps retry with backoff for up to a few hours, then alert the owner instead of vanishing
- Fix a step and retry a failed run on the new version
Debuggable: “why did this happen?” has an answer
Open any record’s timeline and see which automations touched it and when. Open any run and see the branch it took and the condition value that sent it there. When a customer gets a message they shouldn’t have, you can find the cause in a minute instead of reasoning backwards from an outcome.
- “Why didn’t this record enroll?” answers with the exact rule that kept it out
- Per-step counts on the canvas show where people drop off, and goals show what converted
How is this different from other visual builders?
The canvas looks similar. The engine underneath is the difference: durable step persistence, explicit versioning for in-flight runs, and a per-run trace. Those three are what determine whether automation still works after six months of edits.
Can automations wait for days or weeks?
Yes. Long waits are a normal step type: a run can wait for a fixed time, until a date on the record, or for a reply with a timeout, and it holds that state durably rather than depending on a process staying alive.
What stops an automation loop from messaging someone repeatedly?
Re-entry rules per workflow (once ever, again after a set time that defaults to 24 hours, or every time), one active run per record, and loop guards that stop automations triggering each other. Consent is enforced at the send layer, and automated WhatsApp messages wait for 8am–9pm in the recipient’s timezone. Even a misconfigured automation can’t message someone who opted out, or send free-form WhatsApp outside the 24-hour window.
Can I test before going live?
Yes. Test against a real record and you see exactly what would happen (the email it would send, the fields it would change, the path it would take), with nothing sent and nothing changed. You can choose whether to assume the contact replies or not, to walk each path. Publish when the branches do what you expect.
Can the AI publish or send anything by itself?
No. The AI drafts automations; a person reviews and publishes them. AI steps inside an automation write, sort, or extract text. They never send on their own, and every send still passes consent and quiet-hours checks.
Steps 3 and 6 of the customer journey.
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