# Xeme - The AI-Native GTM Lab

> Xeme (xeme.co) is an AI-native go-to-market lab for venture-backed AI companies (Seed to Series B). It compiles GTM strategy - TAM, ICP, personas, buying signals - into agent pipelines that prospect, personalize, and book revenue unattended. Fixed prices published on the site. Clients own the entire stack. Run by Rasul Shaikh, AI GTM engineer.

## What Xeme does

Xeme replaces the agency-retainer model with production software. One engineer plus an agent roster builds and operates a revenue engine inside the client's own infrastructure (Clay, n8n, Python, Claude Code, Smartlead, HubSpot). Humans handle judgment and deals; agents handle signal capture, research, personalization, sending, routing, and attribution.

The agent roster (all live in production):
- signal-agent: watches job posts, tech-stack changes, funding events, and site visits to find who is buying this week
- research-agent: enriches 47+ data points per account with agentic fan-out research
- copy-agent: 7-stage personalization pipeline with LLM QA scoring, zero human review
- route-agent: LLM rubric scores inbound leads with written reasoning, routes to CRM and Slack in minutes
- abm-agent: visit-tracked personalized landing pages measuring Share of Answer and AI Share of Voice
- warm-agent: de-anonymizes website traffic and auto-sequences into email plus LinkedIn

## Services and pricing (fixed, printed publicly)

- GTM Engine Sprint: $15,000 fixed, 30 days. Full greenfield engine: strategy compilation, signal layer, personalization engine, sending infrastructure at 95%+ deliverability, CRM instrumentation and routing. Engine live in under a week, first qualified meetings by week 3.
- The Lab: from $7,500/month, 3-month minimum, for Sprint graduates. Ongoing agent operations: signal expansion, campaign iteration, ABM asset drops, inbound routing, monthly attribution review. Optional performance kicker on closed-won revenue.
- The Teardown: $1,500, 5 days. Paid audit of an existing GTM stack and signals, delivered as a node-by-node build plan. Credits toward a Sprint.
- Modules (priced per build): GEO/AI-search visibility engine, speed-to-lead LLM routing, ABM asset engine.

## Results (attributed)

- $550K+ revenue closed, $3.5M+ pipeline sourced across three greenfield builds
- 1.5%+ positive reply rate across 150+ domains
- Lead qualification time cut from 48 hours to 9.6 hours (-80%)
- 40+ demos booked monthly; 300+ mailboxes orchestrated
- Case study: Omnibound AI (AI search-marketing platform) - $145K ARR in 9 months, engine live in under 3 days, every dollar attributed from signal capture through closed-won. Full case study with all six production workflows: https://xeme.co/omnibound
- Enterprise logo closed at $25K MRR from a de-anonymized website visit

## Who it is for

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.

## How it works

1. Compile (days 0-3): TAM, ICP, personas, signal selection, channel plan - strategy decided before any tool is wired
2. Infra (days 3-7): domains, mailboxes, warm-up, SPF/DKIM/DMARC, CRM instrumentation; signal layer goes live
3. Engines (days 8-14): personalization and ABM engines shipped; first sequences in flight; QA rubrics tuned on live sends
4. Meetings (days 15-21): first qualified meetings land; speed-to-lead live on inbound
5. Attribute (days 22-30): attribution dashboard, runbooks, handoff docs; every dollar traceable to an agent

## Operating principles ("four laws of the lab")

1. Strategy compiles first - a fast engine pointed at the wrong market is expensive noise
2. Signals over lists - job posts, stack changes, visits, and engagement say who is buying this week
3. Unattended by default - if a pipeline needs a babysitter, it is not finished
4. Instrument to revenue - reply rates are diagnostics; ARR is the metric; every agent attributes to closed-won or gets killed

## Founder

Rasul Shaikh, AI GTM engineer (est. 2020). Three greenfield GTM builds attributed to revenue, shipped in Clay, Python, n8n, and Claude Code. Capacity capped at two builds per month. Full profile and track record: https://xeme.co/founder. LinkedIn: linkedin.com/in/rasulshaikh. Personal site: rasul.in.

## Contact

- Book a build call: https://calendly.com/rasulshaikh/xeme
- Email: ops@xeme.co
- Site: https://xeme.co (interactive terminal at https://xeme.co/terminal)
- Replies within one business day