7 posts tagged with "Automation"

The easiest way to make an AI agent dangerous in a CRM is to let it act from a search result.

Search results feel like context. They have names, ids, owners, timestamps, and sometimes a stage. That is enough to produce a confident paragraph. It is not enough to change a customer record.

Copper made this obvious during the rebuild because the useful questions all started vague: the account in this Gmail thread, the renewal in proposal, the customer-success handoff, the stale task nobody owns. The Copper MCP server now treats those questions as account work, not table lookups.

Most "AI for call centers" demos stop at call history: fetch a recent call, summarize the transcript, and move on. That is useful for a screenshot. It does not help a support or sales team run the queue.

Picture this instead. Ten calls were missed while the team was in a meeting. Two came from existing customers. One came through a number that should have been assigned to the sales queue. Three agents are marked unavailable. The tags are inconsistent, so the weekly report undercounts escalations. A manager wants the follow-up list now, not a CSV export.

We see the same pattern across support teams: the hard part is rarely one missing field. It is the scattered context around the call.

We rebuilt the Aircall MCP server around that operations loop: validate the connection, read the account shape, list and inspect calls, match contacts, understand teams and numbers, then make narrow updates only where Aircall supports them.

Most "AI for e-commerce" demos stop at the same trick: ask for a product, get a row back. That demos well. It does not run a store.

Picture this instead. Your summer collection went live yesterday. Half the size and colour combos are silently set to hidden. Two of the categories on the homepage are empty because every product underneath is active=0. A "Mother's Day -20%" cart rule expired last week but is still showing on the storefront. Customers are checking out, but the carrier for one shipping zone has been disabled. Nobody noticed.

That is not a screenshot question. That is what a useful agent has to walk: products, variants, stock, categories, prices, cart rules, carriers, zones, and the customer messages people are leaving when checkout breaks.

We rebuilt the PrestaShop MCP server around that shape. Catalog, order book, and localization get separate tools per resource, not one flat JSON blob.

Most ATS automation starts with a shallow question: can an agent create, read, update, and delete candidates?

That is the wrong first question. Recruiting work is not a generic CRUD table. It is a workflow with company boundaries, positions, pipeline stages, candidate metadata, hiring-team notes, and a real audit trail. If the tool surface only says "update candidate," the agent still has to guess which company, which role, which stage, and which action is safe.

We rebuilt the Breezy HR MCP server around the workflow shape instead: discover companies, read pipelines, inspect positions, list candidates, fetch metadata when needed, create or update candidates, move them through stages, and archive positions for cleanup.

Most "AI for research papers" demos stop at search: find a paper, summarize it, maybe extract a citation. Useful for a screenshot, useless for a real review.

Picture this instead. You have 240 papers saved in Mendeley for a RAG-evaluation review. Forty are missing DOIs. Eighteen have a citation record but no attached PDF. Six are duplicates from earlier exploratory searches. Your shared group library has 30 newer papers your collaborator added last week that you have not seen yet. None of that shows up in a "search the web" demo.

We rebuilt the Mendeley MCP server around that mess. An agent now works with your library as a library — saved papers, missing metadata, PDF files, folders, annotations, groups, trash, and all.

We ran into a problem a few weeks ago that none of our tools could solve. It wasn't a technical problem — the code was fine, the infra was fine. We just needed someone to go do a thing on a website. Sign up, click around, grab some information, paste it into a form. Repeat a bunch of times.

AI couldn't do it. The sites had captchas, email verification, multi-step flows. We tried browser automation and it broke immediately. We needed a person.

So we thought: what if our AI agent could just hire one?

Every AI platform supports MCP tools. But what happens when the tool you need doesn't exist yet? Maybe you're working with an internal API, a niche SaaS, or a service that nobody has wrapped in MCP. Until now, you'd write a custom server.

Now your AI can do it itself.