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- Bring us one workflow. We’ll show you what agents can do with it.
Bring us one workflow. We’ll show you what agents can do with it.
Everyone's talking about agentic workflows but few know where to start.
So start small. Bring us one workflow that's slow, manual, or annoying, and we'll show you what it looks like with agents doing the boring parts.
We’ve been having fun with agents both with clients and internally. An interesting example - our team at Ravn records every call, both in sales and client project management. For years, (naturally) somebody had to open the CRM and type up notes and manually connect the gap between our CRM and our video recording platform. (I know what you’re thinking - yea, our CRM has it’s own video recorder that’s supposed to do this but frankly it does a really crummy job).
We built an agentic workflow on Mastra that kicks off the moment a recording lands. The agent reads the call, figures out the good stuff, and updates the CRM independently without the need for manual input.
Putting the agentic plumbing in place was simple. The hard part was the ambiguity. Real world conversations are messy and unstructured. There’s no label out of the box on the conversation type. The agents have to tell a sales call from a project check-in and when a current client mentions new work, they have to separate what's live from what's potential, and log them differently. That judgment is where most automation projects stall. It's also the part we spent the most time refining.
Want to see it run? Let's chat.
And bring your own ideas. If you need a nudge, these come up constantly:
Accounts receivable follow-up
Resume screening processes
Contract review and the legal fees that come with it
Agentic SDLC code factory
Four questions we ask before building:
Where's the pain? One workflow people complain about.
How does it work today? Map it, warts included.
What should happen instead? Should this be automated at all, and how will you know it worked?
What's it worth? Hours, dollars, deals ROI…
Without Ravn, it would have taken significant time and engineering effort to ship a production-grade AI agent. They didn't just build it. They pressure-tested every assumption and stayed in the room until it was actually working.
Sneak Peak: Real case studies using Claude producing measurable ROI
Pampered Chef: Ravn introduced agentic design to dev workflows cutting SDLC time by 45%
onXmaps: Ravn with Claude increased QA-score rule accuracy from 56% to 88%
Deecho: AI revenue coach for restaurants - how Ravn built audio processing workflows using Claude
Variabl: How Ravn accelerated build time by using Claude
Enerflo: Ravn and Enerflo team cut PR review time by 65% via Claude CodeMarq: Ravn cut build time from 3.5 months to 1.5 months using Claude Code and agentic SDLC workflow
