Xeme is an AI-native GTM lab for venture-backed AI companies. We compile strategy - TAM, ICP, personas, signals - into agent pipelines that prospect, personalize, and book revenue unattended. Built in Clay, n8n, Python, and Claude Code. You own the stack.
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how it runs
One loop, end to end. Most teams run these as four departments; the lab runs them as one pipeline.
Job posts, stack changes, funding events, site visits. The lab watches who is buying this week, not who exists.
TAM, ICP, personas, and channel plan get compiled into agent configs before anything is wired.
Agents research, personalize, send, route, and follow up - unattended, across email, LinkedIn, and ABM pages.
Every meeting and dollar traces back to the agent that sourced it. What doesn't attribute gets killed.
the problem
The deck promised pipeline. Now the board expects roughly 3x pipeline growth on a budget that funds 1.2x headcount. The old answer was an SDR team and an agency retainer. The AI-native answer is a compounding system run by agents - with humans only on judgment and deals.
A fast engine pointed at the wrong market is expensive noise. So strategy compiles first - then the roster takes over.
the roster
Every agent below is live in production - built, shipped, and attributed to revenue. Humans handle judgment and deals; the roster handles everything else. Nobody at the lab manages people; the operator manages agents. The two flagship engines are broken down node by node below.
Watches job posts, tech-stack changes, site visits, and LinkedIn engagement. Knows who is in-market this week - not who merely exists.
Role mapping → intel → gap analysis → positioning → generation. Per-contact emails shipped with zero human review.
Async fan-out across 100+ domains per batch with headless enrichment of JS-rendered pages. Analyst-days of intel, no analyst.
Visit-tracked personalized landing pages measuring Share of Answer and AI Share of Voice, plus per-account video in sequence.
An LLM rubric scores fit and intent with reasoning, pushes routed context into the CRM, and pings the closer while the lead is still warm.
Resolves anonymous traffic to person and account, enriches through Clay, and auto-sequences into email + LinkedIn the same day.
inside the engines
Most agencies show you a logo wall. The lab shows you the pipeline diagrams. These are the production systems behind the numbers - every stage named, every tool listed, every output measured.
Personalization is not a first-name merge tag. It is a research pipeline that ends in a sentence only this prospect could receive. Every contact that enters the queue passes through seven stages before a single word is sent.
Parse the contact: title, seniority, team scope, tenure. Map to a persona with a KPI hypothesis - what number is this person judged on, and what breaks it.
Crawl the account: site, docs, pricing, changelog, careers page. Headless rendering for JS-heavy pages, so modern AI-company sites read the same as static ones.
Attach the trigger that put them in queue - the job post, the stack change, the repeat site visit, the LinkedIn engagement - with a timestamp. Timing is the personalization.
Compare their current motion against category benchmarks - search visibility, AI Share of Voice, outbound footprint, hiring pattern - and isolate the one credible gap worth naming in writing.
Choose the angle for this specific contact: displacement, expansion, or timing. One claim, one proof point, mapped to the KPI from stage one. No angle survives without evidence attached.
Draft the email: subject plus a body under 90 words. First line references the signal, middle carries the gap, close is a single low-friction CTA. Style rules enforced by rubric, not vibes.
A second model grades factuality, specificity, and spam risk from 0-10. Below threshold, it regenerates with the failure reason in context. Above, it routes to mailbox rotation and enters sequence. No human reads it first.
ABM usually means a slide and a Sales Nav list. Here it means a per-account asset the buyer can visit, a feedback loop that watches them visit it, and a sequence that reacts in the same hour. Built for six-figure deals where three stakeholders have to say yes.
Tier the list on signals, not firmographics alone: fresh funding, GTM hiring, stack adoption, engagement history. Tier 1 gets the full treatment below; Tier 2 and 3 get scaled versions.
47+ data points per account: team map, tech stack, active initiatives, content footprint, and a baseline of their Share of Answer and AI Share of Voice in their own category.
A personalized landing page per account - their name, their gap analysis, their numbers - generated from a template system and visit-tracked. Tier 1 accounts also get a per-account video recorded against that page.
Multi-channel sequencing timed off the original signal: email carries the page, LinkedIn carries the relationship, and every touch lands inside the buying window rather than a static cadence.
Page revisits, video watch-through, reply sentiment, and de-anonymized colleague visits re-score the account in real time. When an account heats up, the closer gets a Slack ping while it is still hot.
The human enters with a full context brief in the CRM: signal history, assets viewed, stakeholders touched, suggested talking points. Agents did the 95%; the closer does the 5% that matters.
Every inbound source - forms, replies, de-anonymized visits - lands in one queue with full context attached.
A rubric grades fit and intent with written reasoning, not a black-box number. Disqualifiers get a polite auto-path.
Qualified leads push into the CRM with the reasoning attached, and the right human gets pinged in Slack immediately.
A context-aware first reply or booking link goes out while the lead is still on your site, not tomorrow morning.
Measure how often ChatGPT, Claude, Perplexity, and AI Overviews mention you versus competitors across your category's real buying prompts.
Rebuild key pages into self-contained, citable passages: claim, number, and context in one block, crawlable HTML, llms.txt in place.
Publish the assets AI engines actually cite: comparisons, benchmarks, teardown data - distributed where crawlers and communities pick them up.
Re-run the prompt panel monthly. Share of Answer becomes a tracked pipeline channel with its own attribution line.
the build
The Sprint is not a discovery phase. It is a build schedule with dated deliverables, and it has shipped three greenfield engines on this exact cadence.
Strategy sprint: TAM, ICP, personas, signal selection, channel plan. Nothing gets wired before this ships. Roughly two hours of founder time.
Domains, mailboxes, warm-up, SPF/DKIM/DMARC, CRM instrumentation. The signal layer goes live and starts capturing.
Personalization and ABM engines shipped. First sequences in flight; QA rubrics tuned on live sends, not test data.
First qualified meetings land. Speed-to-lead goes live on inbound. Iteration driven by reply data, per contact tier.
Attribution dashboard, runbooks, and handoff docs. Every dollar traceable to an agent. You own everything; the Lab is optional from here.
operating system
TAM, ICP, personas, and signal selection are decided before a single tool is wired. A fast engine pointed at the wrong market is expensive noise.
Static lists say who exists. Signals - job posts, stack changes, visits, engagement - say who is buying this week. The lab sequences on the second.
If a pipeline needs a babysitter, it is not finished. Agents score, retry, branch, and ship copy without review.
Reply rates are diagnostics; ARR is the metric. Every agent attributes to closed-won or gets killed.
the difference
engagements
Fixed scopes, prices on the page, and you own everything we ship. No lock-in, no discovery-call theater.
The full engine, greenfield: strategy compilation → signal layer → personalization engine → sending infrastructure at 95%+ deliverability → CRM instrumentation and routing.
Ongoing agent operations after the Sprint: signal expansion, campaign iteration, ABM asset drops, inbound routing, and a monthly attribution review.
Attach to either engagement.
A paid audit of your current GTM stack and signals, delivered as a node-by-node build plan. The fastest way to see how the lab thinks.
proof, not promises
case study · omnibound ai · build no. 3
One engineer, one AI system, for Omnibound AI, the AI search-marketing platform. Engine live in under 3 days, every dollar attributed - signal capture through closed-won. Read the full case study →
what buyers say
// drafted from real build outcomes - confirm final wording with each client before ship
Engine live in under three days - faster than our previous agency finished onboarding. Nine months later it's $145K ARR, and every dollar traces back to the system.
Xeme built our GTM engine from zero: 250+ sending domains, qualification cut from two days to under ten hours, and $180K+ in new ARR inside six months.
They broke us into enterprise accounts we'd chased for a year. Targeting accuracy up 45%, and the asset-led ABM motion did the heavy lifting.
lab notes
When your champion Googles the lab, they should find the exact workflows we'd build for you - node diagrams, prompts, and real numbers. Trust compiles before the first email lands. That is the whole point.
the loadout
The lab runs the most technology-forward stack in GTM - and publishes what works. Every tool below has shipped in production. If your stack already includes some of it, we build around what you have - no rip-and-replace.
the operator
Xeme is run by Rasul Shaikh - an engineer with three greenfield GTM builds attributed to revenue, shipped in Clay, Python, n8n, and Claude Code. Production systems that run unattended, not agency decks.
You work with the person who builds the system. Scoping, architecture, shipping, attribution - one brain, zero handoffs. Capacity is capped at two builds per month for exactly that reason.
questions
Everything a skeptical founder or first GTM hire asks before wiring the lab into their motion.
Venture-backed AI/ML companies from Seed to Series B with a sales-led or hybrid motion - founders doing founder-led sales, first GTM hires, or revenue leaders whose SDR math stopped working. Not a fit: pure PLG with no sales motion, or anyone who wants rented SDR bodies.
Hire one - later, for the judgment work. An SDR doing manual research and sends costs more than a Sprint per quarter and produces less pipeline than an instrumented engine. The teams that win give their best person agents, not a team to manage.
Yes. Everything is built inside your infrastructure - Clay, HubSpot or Attio, n8n, sending tools, domains. When we part ways, the engine stays and keeps running. No lock-in.
The engine goes live in under a week. First qualified meetings typically land by week 3, with the full system instrumented at 30 days. The day-by-day schedule is published above.
Roughly two hours of founder or GTM-lead time in the first week for the strategy compile, access to your CRM and domains, and sign-off on the signal plan. After that the roster runs; you take the meetings.
Usually the most useful case. Most Sprints start from a half-built stack: the Teardown maps what to keep, and the Sprint rebuilds the engine around it instead of starting over. Existing tools lower the build time, not the value.
Fixed-fee Sprint, flat monthly Lab subscription, with an optional performance kicker on closed-won revenue. Incentives aligned, cash flow sane, no attribution fights. Prices are printed on this page on purpose.
Multi-domain setup, SPF/DKIM/DMARC, warm-up protocols, and inbox rotation - 95%+ deliverability sustained across 150+ domains in production, not on a slide. Volume scales only as reputation allows.
The engineer. No juniors, no offshore pods, no handoffs - which is also why capacity is capped at two builds per month. The agents do the volume; one human does the judgment.
The engine stays. It lives in your accounts with runbooks and handoff docs, and Lab clients get a 30-day wind-down with a full transfer session. Systems that only work while you pay a retainer are not systems.
open channel
One call to scope your engine. We reply within one business day.
● ACCEPTING Q3 CLIENTS · 2 BUILD SLOTS PER MONTH
