A visual automation builder for business workflows: an AI agent qualifies every new lead, routes it down the right branch and keeps the CRM up to date — with every step logged.
Inbound leads arrive faster than a sales team can triage them. Good leads wait hours for a reply, poor-fit leads take up rep time, and nobody can say for certain which follow-ups were sent or why a lead went to a particular person.
02The proposed solution
A workflow engine with a visual builder. A trigger starts each run, an AI step scores the lead, a condition branches on the score, and actions assign a representative, send a follow-up and update the CRM. Each run records its path, timings and a step-by-step log.
Sample organisation in the demo: Tessaly Cloud (fictional).
03Key features
What the demo does.
Visual workflow builder
Trigger, AI, condition and action nodes on a canvas; add, configure, disable or remove steps.
Conditional branching
Leads above a score threshold go to sales; the rest join a nurture sequence. Adjust the threshold and re-run.
Simulated execution
Run the workflow against sample leads and watch each node change state as the run progresses.
Execution history
Every run is listed with its outcome, path and duration, and can be inspected later.
Activity logs
Timestamped, step-level logs for each run, including failures and retries.
Task tracking
Follow-up tasks created by the workflow appear in a task list with their status.
04Technical approach
How it's engineered.
Technology stack
React
TypeScript
Workflow engine
Queue-based workers
LLM classification
CRM APIs
The production stack we would propose. The demo itself is built with Next.js, React and TypeScript and runs entirely in the browser.
01
Workflow as data
The workflow is a typed definition — trigger, steps, a condition and two branches. The builder edits that definition, and the engine interprets it, so the same model can be saved to a database later.
02
Deterministic simulation
The demo engine executes steps in the browser with realistic delays. The AI qualification step uses a transparent scoring rubric over the sample lead's fields, so results are repeatable.
03
Production architecture
Definitions stored per workspace, runs executed by queue workers with retries and idempotency keys, an LLM step for qualification, and connectors for CRM, email and messaging.
Things to try in the demo
The demo is a working front end over prepared sample data. It is not connected to a live AI model or a production backend, and it doesn't collect any information.
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Next.js · React · TypeScript
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