Quoting, distributor relationships, and reorders run through email and a CRM nobody keeps current, so open quotes age out and quiet accounts go unnoticed. Go Fig builds and runs a Sales Administrative Assistant on your pipeline: it flags the aging quotes, watches accounts for reorder signals, and writes rep and distributor call notes back to the record. Nothing sends until you approve it, until you decide it can.
Go Fig builds and runs a Sales Administrative Assistant on a manufacturer's quote and account pipeline: it audits open quotes for missing follow-up, watches accounts for reorder and intent signals, drafts outreach, and writes rep and distributor call notes back to the CRM. Every action is approval-gated until you graduate what you trust. Because the engagement captures your data into infrastructure you own, the same layer then powers cost accounting and true cost by SKU. Your data stays in your environment and never trains an outside AI model.
Built on the systems manufacturing teams already run
Go Fig captures these into infrastructure you own, then builds and runs agents on top. Nothing your team has to configure.
Every night it finds the open quotes with no follow-up and the accounts that have gone quiet. It posts the list to the owner and, once you approve the rule, updates the record itself. Open quotes stop aging out because nobody was watching.
The rep or distributor call recording lands, and it pulls out the requirements, the objections, and the next step, writes them to the CRM, and drafts the follow-up. Your team stops paying the admin tax on every conversation, and the notes actually get written.
An ordering pattern shifts or an inbound inquiry arrives. It matches the account to the CRM, drafts the outreach, and alerts the rep, instead of the reorder window closing unnoticed.
Order-volume changes and buying signals at the accounts you care about. It catches the signal, drafts the outreach with the hook already in it, and queues it for the rep to approve.
Every action the assistant takes is either autonomous or approval-gated, and you set that per agent and per action. Most teams start with everything in approval mode: the assistant drafts, a person clicks send, and you graduate the actions you trust once you have watched them for a few weeks. Approvals land in Slack or email, where your team already works, and every one is logged.
True cost by SKU lived in five spreadsheets that never agreed. Now it is one reconciled model in the Excel my team already uses, refreshed daily, and our pricing and mix decisions finally stand on numbers I trust.
VP of Finance, Apex Aerospace Components Your data stays in your environment, is never handed to a public chatbot, and is never used to train an outside model. Every read, action, and approval an agent takes is logged, so you can always answer why it did what it did. That is the first question your revenue leader asks, and the first question your auditor asks.
How the Sales Administrative Assistant works for manufacturing teams.
It is a set of agents we build and run on your sales system. Each one has skills (what it knows how to do), triggers (when it wakes up, either a schedule or a change in your data), and actions (what it does, either on its own or queued for your approval). It is not a chatbot your team has to remember to open. It runs whether or not anyone is watching.
Only if you tell it to. Every action is either autonomous or approval-gated, and you set that per agent and per action. Most teams start with everything in approval mode: the assistant drafts, a person clicks send, and you graduate the actions you trust once you have watched them for a few weeks. Approvals land in Slack or email, where your team already works.
No. Go Fig captures the data out of your CRM, your ERP (NetSuite, Business Central, Epicor), and your production systems into infrastructure you own, then builds and runs agents on top. You keep the systems you already run. The agents do the work between them that nobody on your team has time for, and everything they do writes back to those systems.
It starts on your quote and account pipeline, where aging quotes and quiet accounts cost the most. Once that data is captured into infrastructure you own, the same layer powers the back office. Teams commonly expand into true cost by SKU, line, and customer, refreshed daily and traceable to the work order.
Your data stays in your environment, is never handed to a public chatbot, and never trains an outside model. Every read, action, and approval an agent takes is logged, so you can always answer why it did what it did. That trail is the first thing your revenue leader asks for, and the first thing an auditor asks for.
Free, and senior-led. An advisor maps your sales system with you: where the data lives, where work piles up, and which agents would take it off your team first. No generic demo.
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