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Applied AI GTM company

Applied AI for go‑to‑market, one outcome at a time.

Forward-deployed engineering and agents inside your Clay, CRM and sending domains, for AI startups from Seed to Series B. We aim at one number you pick and stay until it moves.

Book a 27-minute call See pricing
  • Built inside your stack
  • Fixed prices on the page
  • You own everything
  • A person on every decision that matters

Not a mock-up. Xeme's own engine, numbers from 14 October 2026.

Our own engine

Xeme runs on its own engine.

The same kind of system we build for clients, running on Xeme's pipeline: HubSpot in, Clay research and scoring, verified email, a copy check, and everything written back with a reason.

Drawn from the live workflows942 enriched · 584 verified · 242 of 275 pass
Sourcen8nClayn8nWhere it landsHubSpot942contacts, all enrichedInboundform and Cal.com bookingsxeme_02HubSpot to Clay importResearchone pass per accountICP scoreA 147, B 183, C 183, D 117Email waterfall584verified, 10 providersCopy check242 / 275buyer emails passxeme_01Clay to HubSpot, safe re-runInbound routercontact, note, task in 30 sHubSpottiers, notes, follow-up tasksxeme_03to warehouse, every 6 hoursWarehouseask in plain English
  1. HubSpot, 942 contactsxeme_02 imports them into Clay
  2. Research and ICP scoreone pass per account, tiers A to D with reasons
  3. Email waterfall, 584 verified10 providers, then validation
  4. Copy check, 242 of 275 passdraft, check, fix, check again
  5. xeme_01 syncs to HubSpottiers, notes and follow-up tasks
  6. xeme_03 to the warehouseevery 6 hours, questions in plain English
  7. Inbound routerform or booking to contact, note and task in about 30 seconds

Built on the platforms you already own

  • Clay
  • HubSpot
  • Salesforce
  • n8n
  • Claude
  • Supabase
  • Smartlead
  • Notion

What we build

Four kinds of work. One outcome each.

Each ends with a named process running in your systems, a person on the decision, and a number measured before and after.

01 · Forward-deployed engineering

An engineer in your stack until it runs

Built on the tools you already pay for, one change at a time, tested on real records, then handed over with a runbook and an owner.

UNKAPPED: inbound qualification from 4 hours to under 3 minutes, 698 unit and 143 end-to-end tests.

02 · Applied AI

One process, one owner, one number

A narrow job: read, decide, draft, write back. A score on every output; below the bar, a person. A test set from your past cases.

Relay for DefenderMate: Claude drafts every reply and nothing sends until a person decides.

03 · GTM engineering

Pipeline as a system, not a campaign

Signals scored with a reason, accounts enriched once with a credit cap, routing with rules, every touch logged in your CRM.

RemoteState: qualification from 48 hours to 9.6, $180K ARR growth in six months.

04 · Enterprise AI transformation

Audit, design, deploy, run

One department process at a time: baselines first, then what each agent reads and writes, one change a week, a weekly review.

Most production work so far is in revenue teams. The live system below is the clearest example.

Live in production

Meeting intelligence, every recorded call.

Built in-house by Rasul at UNKAPPED. Every recorded outside call becomes one record tied to the right client, with a summary a person reads first, and nothing reaches a client or the CRM without a gate.

n8n / the real workflow22 workflows · 15 live · 1,159 tests · 17 Oct 2026

Every recorded call, one record a person trusts.

01 CAPTURE02 ONE RECORD PER CALL03 SUMMARY AND REVIEW04 AFTER THE CALL05 OPSTRIGGERLIVEmi_1a_capture_google_meet17 mailboxes, every 2 hTRIGGERLIVEmi_1b_capture_granolawebhook plus 2 h sweepTRIGGERLIVEmi_1c_capture_firefliesevery 2 hTRIGGERDEMOmi_1d_capture_fathomdemo account onlyTRIGGERLIVEmi_1e_capture_tldvevery 2 hTRIGGERLIVEmi_1f_capture_notes_formcalls with no recorderWORKFLOWLIVEmi_2_save_recordingeach recording onceWORKFLOWLIVEmi_3_merge_into_meetingshourly, 31 into 15AGENTLIVEmi_4_tag_call_typeClaude Haiku, hourlyWORKFLOWLIVEmi_5_find_client_in_hubspotHubSpot, read onlyPATH IN mi_4LIVEunsure callsto a person, marked unsureAGENTLIVEmi_6_summarize_callcard and thread, v2.4WORKFLOWLIVEmi_9a_review_buttonsapprove or fix, one clickAGENTBUILTmi_7_update_account_memoryrolling notes per clientAGENTBUILTmi_8_score_callscored by call typeWORKFLOWBUILTmi_search_1_index_callschunks into vectorsAGENTBUILTmi_search_2_ask_formplain English, citedWORKFLOWOFFmi_9b_syncup_tasksdrafts, approve firstWORKFLOWOFFmi_9c_hubspot_writebacknotes and tasksWORKFLOWLIVEmi_ops_google_tokenkeeps Meet accessWORKFLOWLIVEmi_ops_error_alertsevery failure, loudWORKFLOWLIVEmi_ops_daily_healthdaily coverage checkWORKFLOWBUILTmi_ops_drift_checklive vs repo123456ops watch all 22 workflowszero-retention AIno transcript in run historyclient names only in the DB
  1. 1Every capture door saves each recording once.
  2. 2Recordings of the same call merge into one meeting: 31 became 15.
  3. 3Unsure calls go to a person, marked unsure.
  4. 4A person approves in one click before anything moves.
  5. 5Task drafts and CRM write-back stay off until approved.
  6. 6A daily coverage check and loud error alerts watch all 22.
Redrawn from the live n8n map, structure only. Mint AGENT tab = a step with a model call inside. Amber = built, not live yet. Dashed = off or demo. Built in-house by Rasul at UNKAPPED; not a Xeme client result.
22n8n workflows
15live now
1,159tests pass
31 → 15recordings to meetings
  • Zero-retention AI
  • 12-month purge
  • No-AI clients never processed
  • Client names only in the database
  • No transcript kept in run history
  • Code de-identified, private repo

How a Sprint runs

Thirty days, plan to a running engine.

A build schedule, not a discovery phase. About two hours of your time in week one, plus access to your CRM and domains.

  1. Step 01 · Days 0 to 3

    Plan

    The outcome and the guardrails. TAM, ICP, personas and signals agreed before anything is wired.

  2. Step 02 · Days 3 to 7

    Set up

    Domains, mailboxes, warm-up, SPF, DKIM, DMARC, CRM fields. Signal feeds go live.

  3. Step 03 · Days 8 to 14

    Build

    Engines run against past replies, lost deals and closed calls before a live mailbox. You correct them.

  4. Step 04 · Days 15 to 21

    Meetings

    First qualified meetings. Inbound scored and routed. Copy follows the reply data.

  5. Step 05 · Days 22 to 30

    Hand over

    Attribution in your CRM, runbooks, handover. The engine keeps running in your accounts.

The agents

Research, draft, check with a second model, route, follow up, and log every step to the CRM.

The people

Set the rules, correct the first runs, decide anything unusual, and take the meetings.

Inside one engine

Seven steps to one email worth reading.

The personalization engine Rasul built in-house at Omnibound AI: 95%+ deliverability and 1.5%+ positive replies across 150+ sending domains.

  1. Role mapTitle and team, mapped to the number this person answers for.Clay, Sales Navigator
  2. ResearchSite, docs, pricing, changelog, careers.Firecrawl, Playwright
  3. SignalThe trigger, with a date.Clearcue, Trigify, RB2B
  4. GapOne gap worth naming.Ahrefs, Claude
  5. AngleOne claim, one proof point.Claude
  6. DraftUnder 90 words, signal first.Claude, OpenAI, Clay
  7. Check and sendA second model scores facts and spam risk; below the bar it rewrites.LLM judge, Smartlead

Pricing

Fixed scopes, printed here.

Everything that ships runs in your accounts and stays yours.

The Teardown
$1.5K
5 days

A paid audit of your GTM stack and signals, delivered as a node-by-node build plan.

  • Credits toward a Sprint
  • Useful if you already run Clay and outbound
Ask about the Teardown
GTM Engine Sprint
$15K
Fixed, 30 days

The whole engine: plan, signals, personalization, sending setup, CRM tracking and routing.

  • Engine live in under a week
  • First qualified meetings by week 3
  • Every dollar attributed in your CRM
Book a call
The Lab
$7.5K
From, per month. 3-month minimum.

Running the engine after a Sprint: new signals, campaign changes, ABM pages, monthly attribution review.

  • Optional performance fee on closed-won
  • Anything that does not attribute gets cut
Talk about the Lab

Add-on modules, priced per build: AI search visibility, an agent for one process outside sales, founder content for LinkedIn and X, speed-to-lead routing.

Proof

In-house results, with sources.

Systems Rasul built inside each company, before or alongside Xeme. Not Xeme client results. Details on request.

Sep 2026 to now

UNKAPPED

Founding forward-deployed engineer

  • 4 hours to under 3 minutes inbound qualification, brief engine of 8 n8n workflows
  • Meeting intelligence: 22 workflows, 15 live, 1,159 tests
  • 1,200+ ready-to-send copies from a Clay engine with a credit cap and a QA gate
  • 300+ mailboxes from Clay to PlusVibe by API
Apr 2023 to Jun 2025

RemoteState

Software development firm

  • $550K closed-won and $3.5M total pipeline
  • $180K ARR growth in six months
  • 48h to 9.6h lead qualification with an AI scoring model
Oct 2025 to Aug 2026

Omnibound AI

AI search marketing platform

  • Under 3 days to deploy a campaign, down from 2 weeks
  • 1.5%+ positive replies across 150+ domains at 95%+ deliverability
  • 500+ programmatic ABM pages, each visit logged
Aug to Sep 2026

DefenderMate

CTEM security, through OneGTMLab

  • Relay: reply triage with a person on every send
  • 183 commits in 16 days, 16 Edge Functions, an MCP connector
Dec 2022 to Apr 2023

FalconWise Tech

Cybersecurity, Dubai

  • 10+ SQLs a month from zero pipeline
  • Targeting accuracy up 45%
Sep 2024 to now

Clay Creator

Official creator program

  • Enrichment waterfalls, ICP scoring, signal routing and CRM sync, with credit caps and QA gates

The stack

Built on what you already own.

Nothing gets migrated, and models stay swappable. The dashed layer is what agents connect to when a process needs it.

01 · Run on past builds

Models and agents

  • Claude and Claude Code
  • Anthropic API
  • OpenAI API
  • Gemini
  • MCP servers
  • OpenRouter
02 · Run on past builds

Automation and data

  • n8n
  • Python
  • Supabase
  • PostgreSQL
  • Apify
  • Firecrawl
  • Playwright
  • Modal
  • Vercel
  • GitHub
03 · Run on past builds

GTM data, signals and sending

  • Clay
  • Clearcue
  • Trigify
  • RB2B
  • PhantomBuster
  • Sales Navigator
  • Apollo
  • Smartlead
  • Instantly
  • HeyReach
04 · Run on past builds

CRM and calls

  • HubSpot
  • Salesforce
  • Attio
  • Pipedrive
  • Fathom
  • Sendspark
05 · Run on past builds

AI search and analytics

  • Ahrefs
  • Semrush
  • Perplexity
  • Search Console
  • GA4
  • Mixpanel
06 · Connected when needed

Connected through their APIs when a process needs them

  • Zendesk
  • Intercom
  • Jira
  • Notion
  • Airtable
  • SAP
  • NetSuite
  • ServiceNow
  • Workday
  • Snowflake
  • Databricks
  • AWS
  • Google Cloud
  • Azure

Ask which of the dashed layer we have shipped on, and we will tell you.

Rasul Shaikh

Who runs it

One engineer, scoping to handover.

Rasul Shaikh. AI GTM engineer since 2020, founding forward-deployed engineer at UNKAPPED, official Clay Creator. Pune, works remote.

You bought the AI. You bought the GTM tools. The number still hasn’t moved. That’s the gap I work in.

His day job is at UNKAPPED; Xeme is the practice he runs alongside it, so it takes on a few builds at a time.

Full track record LinkedIn Field notes rasul.in

Questions

Asked often, answered plainly.

Who is a fit?

Venture-backed AI and ML companies from Seed to Series B with a sales-led or hybrid motion: founders still 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 teams that want rented SDRs.

What is a forward-deployed engineer?

An engineer who works inside your team and your systems, maps how the work actually moves, builds on the tools you already pay for, and stays until it runs in production. Then hands it over with a runbook and a named owner on your side.

What do you mean by applied AI?

AI tied to one named business process, running on your own data and systems, with an owner, a review gate and a number it is judged on. Buying seats for a chat tool is not applied AI.

What is GTM engineering?

Building pipeline as a system rather than a campaign: signals captured and scored with a reason, accounts enriched once, routing with rules, every touch logged in the CRM, and closed deals feeding back into targeting.

Can you get us into ChatGPT and other AI search answers?

We can measure it and move it, but nobody can promise it. Answers change from run to run and each engine cites different sources, so we track a panel of real buying prompts every month and report the trend next to pipeline.

Do agents send anything without a person?

You decide. Outbound email can go out after a second model checks it, once you have approved the rules and the first runs. Replies to prospects, anything that touches money, and anything below the confidence bar wait for a person.

What do you need from us?

About two hours of founder or GTM-lead time in the first week, access to your CRM and domains, and sign-off on the signal plan. After that you review the first runs and take the meetings.

Do we own what you build?

Yes. It is built in your Clay, your CRM, your n8n and your domains. When the work ends, the engine stays and keeps running, with runbooks and handover docs.

We already run Clay and outbound. Is this still useful?

Usually that is the most useful case. The Teardown maps what to keep, and the Sprint rebuilds the engine around it. Existing tools shorten the build.

Next step

Start with a 27-minute call.

Bring the number you want to move. If Xeme is not the right fit, we will say so and point you to someone who is.

Book a call Ask about the Teardown

Replies within one business day. ops@xeme.co