The context layer for B2B revenue teams
Your recorded calls, CRM and docs, filed into a company brain your team owns. Outbound and content run on top of it, and every campaign makes the next one smarter.
Entries filed from calls
The team pastes into ChatGPT all day. The output is generic and nothing it learns is kept.
What wins deals lives in one person's head. When they leave, so does the why.
Hours of buyers explaining what they need, and nobody opens a transcript.
"I've watched a company pay for Gong while nobody opened a transcript."Fractional RevOps lead · public post · 2026
Reps "spending thousands on Claude tokens."Sales leader, relayed in a public comment · 2026
Your raw material goes in once. It's sorted into a versioned repo of what your company knows, and every skill and campaign reads from it instead of starting cold.
output
Tools I work with, on your accounts and your billing. I pick from your stack first. No affiliation with any of them.
Every call and campaign writes back to the brain
In a recorded interview, Pylon's co-founder says B2B companies were starting to talk to their customers in "shared Slack channels" instead of just email. The build turns that into who to write to: job posts that put customers in Slack.
Not a client engagement and not a measured result. It uses public interviews only; a client version runs on your own recorded calls.
The repo I run the agency on · counted 10 Nov 2026
Every claim in your repo carries a status, and it only moves up when the evidence does. So your AI never sells a guess as if it were true.
"New AEs ramp slowly because they learn the pitch from the website."
Real pages from my own repo · I run the agency on the same rules
CLAUDE.md
One fact, one home. A number or claim is stated once, in its canonical file, with its source and date inline; everywhere else links to it.
Never present HYPOTHESIS content to the founder as settled fact.
objection-handling.md · O3
"Can't we build this ourselves?"
The quickstart is 10% of the job. The other 90% is curation taste, grounding docs against transcript evidence, wiring it into your CRM and outbound stack, and maintaining it so it doesn't fossilize.
A brief from the CRM, public signals and your own calls with similar buyers: which pain to open with, which objection to expect.
Brief · Northwind (Series A, 60 people) signal First Head of Sales, 3 wks ago pain "new AEs learn the pitch from the site" matches 4 of your won deals expect "we already have Gong" → objections.md#O2 open Ask how the new hire is ramping
Pains in the buyer's exact words, objections tallied, ICP notes updated, each written into the repo with the call it came from.
call-northwind.md + pain "we lose the why when a rep leaves" + objection O2 seen (7th this quarter) + icp ops lead joins the 2nd call ~ buyer-pains.md P2 moved up to #1 → logged in decisions.md
New hires read the repo instead of shadowing for a quarter. The voice file lets the team write like the founder.
objections.md 8 objections · 31 calls voice.md 12 real samples onboarding.md first 30 days, from calls lost-deals/ why, in buyers' words status: HYPOTHESIS until 3 calls confirm
Each step is useful on its own, and you decide on the next one only after you've seen the last.
A scored read of where your revenue context lives today, plus a 90-day plan.
Your calls, CRM and docs become a repo your team owns, with 2–3 working skills on top.
I keep the brain current and run one engine on it: outbound or content.
I'm not SOC 2 certified and won't pretend to be. The security story is the architecture: your repo, your accounts, your keys.
A private repo under your GitHub org. I'm a collaborator you can remove in one click.
CRM, Clay, sequencer and Claude run on your accounts and billing. Nothing goes through mine.
One repo and one workspace per client. What I learn is method, never your data.
A standard NDA, mine or yours, before I see anything.
I'm Dixant Puram. archive-ark is founder-led: I do the work myself, from Mumbai. I'm taking three founding clients to start.
No case studies yet, which is why the first step is a small, paid audit that credits toward the build if you go ahead.
Yes, and you should try it. Pasting calls into a chat gets you one good answer that's gone tomorrow. The work is sorting what the calls say into files your team and your AI read every time, checking them against the evidence, wiring them into your CRM and outbound, and keeping them current.
Those tools each hold a piece, and none of them knows what the others know. The context layer sits under the tools you already pay for, so they stop producing generic output.
A private repo in your GitHub org with your ICP, pains, objections, voice and decisions, written from your own calls. On top of it, 2–3 skills your power user runs without me, and the day-1 metric measured on day 21.
No. One person becomes the power user. They use GitHub Desktop and the Claude desktop app, no terminal, and I train them until they run every skill alone.
To start, you just need to record calls at all. The Sprint works from 15–50 transcripts. If you have fewer, the audit tells you what to capture first.
I keep my hours on your workday. Calls run Monday to Friday from the US morning into early afternoon Eastern, and the booking page shows the times in your own time zone. All the work lives in your repo, so you can check progress whenever you like.
It depends on the step and the team, so I cover it on a short call. The audit is always paid, and its fee credits toward the Sprint if you book within 30 days.
20 minutes, with me. I'll tell you straight whether a context layer fixes it. If it doesn't, I'll say so.
A question, a task you want checked, or a no-thanks. I read every message myself.