Episode 10
This week on Redacted, we bring back longtime friend Parker Mayes, founder of Signify, for a guest episode that's almost entirely a live demo. It's a company-talk-heavy conversation where Parker walks through the actual agents running his sales pipeline in real time, but the founder lessons land just as hard: where AI should live versus where a human has to step in, and why a non-technical founder with the right AI sidekick might not need a technical co-founder at all.
Who the Guest Is
Parker Mayes is the founder of Signify, an AI sales intelligence platform that sits between a prospect database (Apollo, ZoomInfo, or your own list) and your CRM, scoring and enriching companies every day based on real buying signals. Signify launched a few months ago and already counts 25–30 companies as a mix of beta testers and paying customers. Before Signify, Parker spent four years running a go-to-market agency built around high-volume cold email, calls, and LinkedIn outreach for B2B clients and the frustration with low-trust, low-response outbound at scale is what pushed him to build Signify in the first place. Before that, about five years ago, he co-founded LetsGo!, a startup focused on date nights for couples, living with four other founders in a Raleigh startup house. He also runs the Triangle Startup Collective's monthly AI meetup, where Taylor spoke earlier this week, which is also the reason this episode came together.
What We Cover
From agency to product: Parker spent four years running a high-volume cold-outreach agency, doing around 5,000 touches a month for B2B clients, before realizing the real value was the targeting engine underneath it, which became Signify.
What Signify does: It sits between a prospect database like Apollo, ZoomInfo, or a scraped list and your CRM, scoring and enriching companies daily based on custom buying signals like new hires, company news, job postings, and website changes, so reps know who to reach out to today, not just who exists.
Real numbers: Signify launched a few months ago with 25–30 companies already using it, a mix of beta testers and paying customers. Pricing is flat at $200/month per seat, including 100 company scans and roughly 10 phone/email unlocks a day.
Why the incumbents haven’t built this: Parker says it costs “a couple dollars a day” to scan 100 companies given how much scraping and searching is involved, which works at his scale but would be “completely unreasonable” for a database with 300 million prospects.
Four daily automations: Every rep on Parker’s team runs the same pipeline each day: run the scan, send roughly 30 LinkedIn connection requests, add newly connected people to the sequence and send message one, and advance everyone else already in the pipeline to message two or three.
“Interested is a human activity”: Parker never lets AI touch a warm lead. Cold outreach is automated end-to-end, voice-matched to his own texting style, down to lowercase openers and “LMK,” but the moment someone replies, a human takes over, including scripted, personalized selfie-video follow-ups.
The screenshot build: His newest automation runs a live custom demo for a prospect, screenshots the exact signals pulled for them, and sends it as a personalized LinkedIn message, with three concurrent browser tabs running in parallel so it can message three people at once. It took about a week and a half to get working end to end.
On not needing a technical co-founder: Pushing back on a VC who told him he needed one to raise, Parker’s take is, “I’m just a sales co-founder... there’s now no reason that you can’t have that as one person as long as you have a sidekick that’s your developer, who’s just cracked and never wants to talk to anyone and just grind.” He uses Claude Code himself for small features, saying recoloring pipeline badges took “6 lines of code” and “5 seconds.”
The restaurant tangent: David and Taylor compare notes from Offline. Expanding an existing restaurant account into its other locations takes far more context, including open support tickets, contract status, and staff turnover, than cold outreach, and the industry itself is “so incestuous” that true cold leads are rare.
The stack: Signify runs almost entirely on Anthropic’s API. Claude Sonnet is the workhorse model, with a GPT fallback, and the live pipeline automations ran on Claude in Cowork with browser automation through Claude and Chrome.
Origin story: Parker and David’s first real conversation happened roughly five years ago through a mutual NC State connection, a memory Parker still brings up of David cutting the call short to go be with his kids.
As Parker puts it, the biggest thing holding most people back isn’t the build time; it’s the assumption that it’ll take months when it might take an afternoon. Enjoy the conversation.
It’s best viewed on YouTube to fully see the examples (make sure to subscribe!)
But also available on all audio podcast players through Tweener Talks!
PLUS we have a new spot for show notes and files discussed in the episode. Check it out: https://github.com/instanttaylor/redacted-podcast
We need your [Redacted] AI experiences for upcoming episodes! Who’s the most underrated AI builder you know? Someone running real systems inside a real business? Send us a message at contact@tweenerfund.com because we want to get them on the show!
What’s Next?
New episodes drop twice a month/every other Wednesday. If you want to be on the show as a guest and show your [REDACTED] builds, email us here.