before we built https://t.co/n17Nhs79hM i was spending entire evenings researching companies and writing cold emails:
- pull a list from apollo
- open 50 tabs
- check every website
- figure out what to say
- write something half-decent
- repeat.
by lead 30 i was cutting corners.
scout does the part i couldn't keep up with; it researches every company properly, scores them on fit and timing, then writes outreach based on what it actually found.
not templates with a name swapped in - emails that reference real intel about their business.
that’s the gap we’re building for. we don’t think outbound is broken because people don’t try hard enough - it’s broken because doing it well doesn’t scale manually.
scout makes 'doing it properly' the default, not the exception.
we’re just getting started, and we’re shipping fast.
if you’re doing outbound and want to see what this looks like in practice, give it a spin and let me know what you think - free tier is available now.
(link to product in the comments below).
@tengyanAI bcg study in hbr earlier this year. workers managing multiple ai agents reported 12% more mental fatigue than normal work
working with agents for long hours day to day does feel more brain melting than regular work its true.
@rohanpaul_ai This exactly what we're doing over at @mirasystemsltd - we create bespoke systems for small businesses to help them integrate AI into their everyday pipelines.
@mattpocockuk this is what i have found also; my md is set up so that before ever executes anything it asks as many questions/obtains as much clarity as possible - works way better
@asaio87 its about learning about to counter the problem areas and using it for the right tasks as opposed to relying on it across the board - but your right, currently there's no way to avoid it
Introducing Claude Opus 4.7, our most capable Opus model yet.
It handles long-running tasks with more rigor, follows instructions more precisely, and verifies its own outputs before reporting back.
You can hand off your hardest work with less supervision.
got tired of doing the same research workflow manually for every prospect. so I spent a couple days building a tool that does it on capture.
the recon system:
- chrome extension pulls a company from sales nav/linkedin profile
- scores it against our ICP
- generates three ranked outreach angles
- drafts the full 3-touch sequence
- tracks the pipeline through to call booked.
integrated batch a/b testing also lets us monitor reuslts over time to see what patterns do/don't work on our targets.
the best part is its basically free to run: small llm calls and a database/frontend setup.
our answer to manual linkedin prospecting + outreach without paying for another service.
will share how well it actually works once we've run the outreach for a few weeks.
@GoldilocksOrbit edge cases are caught in human review - this is mainly a cheap to save the grunt work of finding leads/intel and suggested angles but I manually send the messages so I can catch edge cases there
@pascalkordon only real area of disadvantage is the marketing side - this is where having a team on hand would be super useful.
a man can only send so many emails and linkedin dms per day
@salesxsaas this is the thing full ai sdrs aren't as effective as human/hybrid models - even the sdr companies know this
the best systems are where the human still has input power - people know when they're talking to an ai
@EXM7777 i've found the longer/more numerous the ref docs/files are the more room there is for mistakes.
better to record things to a database and have it read from there
@Chris_Orlob yeah this is also people massively over estimating the raw ability of these models
in a lot of minds llms are all powerful wizards, which is no where near the case at present