Configured on day one. Still yours on day two hundred.
No blank workspace, no implementation project. Unify Loop installs a pack for your industry, takes your data and channels, and then gets out of the way while you define what matters — once.
Answer one question
Onboarding asks what your business sells. Pick an industry and the pack installs — objects with real fields, a pipeline with real stages, automations already running, and a dashboard. You are looking at a working workspace, not an empty one.
- Seven industry packs, or start blank if you would rather
- Everything installed is configuration you can rename or delete
- Takes minutes, not a professional-services engagement
Bring your data and your channels
Import contacts and records by CSV with column mapping onto whichever objects you have. Connect your calendar, your email, and your messaging numbers — or bring your own provider credentials and pay your carrier directly.
- CSV import with mapping, or the API for anything larger
- SMS, WhatsApp, and email arrive in one thread per contact
- Consent state, opt-outs, and quiet hours enforced from the first message
Define what matters once
Build the filters your team actually works from — the morning list, the at-risk accounts, the deals going quiet. Each one is a definition you can then reuse as a campaign audience, an automation condition, or a line on a dashboard.
- One filter format across views, segments, automations, and reports
- Filter across related records, not just the object in front of you
- Ask the AI in plain language and keep the filter it wrote
Let it act, then check the trace
Turn on the automations that matter and read what they did. Every run keeps a step-by-step trace with timings and the conditions it evaluated, so you can expand what the system handles as you build confidence rather than all at once.
- Test against a real record before publishing, with nothing sent externally
- AI drafts for approval first; expand its autonomy when you have read a few weeks of drafts
- In-flight runs finish on the version they started on
Every outcome feeds the next loop.
The reason this is one system rather than four tools is that the learning has somewhere to go. What converted, what went quiet, and what the AI got wrong are all recorded as events on the same records the automations read.
Leads, conversations, campaigns, and deals stop living in four tools that disagree. Everything attaches to one record on one timeline.
Filter, segment, and question your data with one shared engine — so the answer on the dashboard matches the answer in the view.
Durable, versioned workflows and AI that can act inside limits you set. Runs survive restarts and edits instead of quietly dying.
What converted, what went quiet, what the AI got wrong. Recorded as events, so the next loop starts better informed than the last.
Pick the pack that matches what you sell.
Getting started, answered.
How long does setup actually take?
An afternoon to a working workspace: the pack installs in minutes, and most of your time goes on importing data and connecting channels. Configuring automations to match your process is the part that takes real thought, and you can do it incrementally rather than up front.
Do I need a developer or an implementation partner?
No. Objects, fields, pipelines, filters, and automations are all configured in the product. You would only want the API for a data warehouse sync or pushing product events in.
Can I run this alongside my current CRM while I evaluate?
Yes, and it is the most common way teams start — one automation running here while the existing CRM stays the system of record, then migrating once it has proved itself.
What if I get the data model wrong at the start?
You will, a bit, and that is fine. Widening a field applies immediately; narrowing one shows you exactly how many records conflict before you confirm. Renaming never breaks a workflow or view, because labels are separate from identifiers.
Who does the AI talk to first — us or our customers?
Your call, per workflow. Most teams start with AI drafting internally for a human to send, and only enable autonomous sending on the narrow cases (first response, scheduling) once they have read the drafts for a couple of weeks.
An afternoon to a working workspace.
Free trial, no card, and a pack installed before you have finished your coffee.
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