Caddi vs. ChatGPTAsk ChatGPT twice, get two answers. Caddi runs your process the same way every time.
ChatGPT gives you answers; you still do the work. Caddi takes the action for you, running the multi-step process across your tools exactly the way you would have, as deterministic code that's cheaper per run, repeatable, and fast. Keep ChatGPT for the drafting; let Caddi run the volume.
- 99%of agent runs complete successfully
- ~45 minon average to build your first agent
- 1M+back-office tasks completed
Jason AlafganiUpdated Sep 23, 2026
The same prompt can come back two ways.
That's fine for a first draft. On a client record, a field that comes out differently on Tuesday than it did on Monday is a correction someone has to catch.
Writes a fresh answer to every prompt, which is exactly what makes it good at drafting and brainstorming.
Known steps run as code, so the same input takes the same path on every run. AI handles only the steps that need judgment, like reading a messy PDF.
Someone still carries the answer to the next app.
The model writes the summary. A person pastes it into the CRM, renames the file, files it in the DMS, then opens the next one.
Answers in the chat window. Work in your other tools is done by the person reading it, or built by your team on GPTs or the API.
Runs the whole process across 150+ tools, reading inboxes and PDFs and writing to your system of record, with nobody at the keyboard.
Turning a good prompt into a process is an engineering job.
Integrations, monitoring and upkeep when a tool changes don't come with a chat license, and your ops team shouldn't have to become developers to get them.
Automations are built by your team with GPTs or the API, and maintained by you.
Your ops lead describes the process in chat or on a screenshare, and Caddi builds and maintains it. About 45 minutes on average to the first agent.
Frequently asked questions
Can ChatGPT replace Caddi for automating workflows?
Not for production back-office work. ChatGPT is an assistant a person drives, you prompt it, then copy its output into your other tools by hand. Caddi does the multi-step work for you: it runs as deterministic code across 150+ integrations, reads varied PDFs and inboxes, and writes to your system of record unattended. ChatGPT is great for drafting and Q&A; Caddi is for getting repeatable operational work done. Most firms should keep both.
Should we use ChatGPT and Caddi together?
Yes. ChatGPT stays the assistant for drafting and questions in a person's day. Caddi Discover finds the repetitive work across your systems and Caddi runs the high-volume, high-accuracy part of it as deterministic code. If your firm also runs Claude or Microsoft Copilot, Discover can export findings to them as a skill or agent, and Caddi Govern monitors those runs next to Caddi's, so leadership sees what is automated, where, and what is still done by hand.
Is Caddi cheaper than using ChatGPT for the same work?
For high-volume operational work, usually yes. ChatGPT bills per seat and consumes model tokens on every request. Caddi's AI builds the automation from a recorded workflow, then production runs as deterministic code with AI only on the steps that need judgment, so each run is low-cost and predictable rather than paying for a model call at every step.
Does Caddi hallucinate like ChatGPT?
Any model step can get something wrong, Caddi's included. What Caddi changes is how much of the run is left to chance. Non-determinism compounds: a step that is right 90% of the time comes out clean only 48% of the time across seven steps. Caddi runs the fixed steps as deterministic code, the same way every time, and keeps AI to the steps that need judgment, each one scoped, logged, and reviewable. ChatGPT decides every step, which is fine for drafting and risky for unattended operational runs.
Is ChatGPT safe for confidential client data?
It requires care. Pasting confidential client information into a general chat assistant raises data-governance and PII concerns. Caddi runs within your stack with SOC 2 controls, role-based access, and audit logs, and its deterministic production runs don't send your data to an LLM on every step.