Jared Burke, AI Engineer for GTM and RevOps. Fractional GTM engineering in London.

Fractional. Embedded. Output that ships.

I’m your

Fractional

Agentic

AI-native

Full-stack

Revenue

Embedded

AI Engineer.

Forward-deployed AI engineering for GTM and RevOps. Agents build in parallel. I review every line. Watch revenue run on rails.

Book a call

Trusted by founders and revenue leaders at

Revenue infrastructure. Deployed with finesse.

I’ve shipped pipeline for global powerhouses, industry titans and the occasional avocado-on-toast lover. The systems work. The deals close.

see what I build

Why hire me fractionally?

Hire one engineer. Get a 10-person GTM and RevOps team that never sleeps.

Most fractional hires bring opinions. I bring working systems, and the agents that run them. Built in code, reviewed line by line, live by week two. You own every line.

Agentic

Eval-tested

Code you own

Human-reviewed

Live by week two

Case Studies

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Multiple agents. Many models. One engineer.

I build AI-native GTM and RevOps systems in code, running parallel agents and loop engineering so every workflow improves itself with each run. Nothing merges until I have read the diff.

What clients say

These engagements. Same engineer.

Real engagements. Real revenue lifts. Same engineer you’d be hiring fractionally.

“Creativity as well as disciplined iteration are key to cutting through the noise in GTM engineering. Jared brings both in buckets.”

Antony Cousins

CEO at Factmata

“Jared opened many doors for us and consulted on our RevOps, marketing and sales process to achieve a 23% qualified lead conversion rate.”

Martin Burt

CEO at ClearStake

“We’ve been greatly surprised with the ideas and techniques Jared brought to the table and built into our GTM engineering. He has helped us consistently develop and grow.”

Andrew Daly

Kinetic-ID

“We’ve come across many GTM engineers who claim they can deliver, though none execute at the level and transparency Jared has. A good mix of ethical automation and clever engineering.”

John Hammond

Director of Operations at MindK

“The crème de la crème of GTM engineering. Extremely professional, proactive, plans ten steps ahead. I see him as an extension of our team.”

Dexter Hutchings

Marketing Manager at Mindset.ai

“Jared has worked tirelessly to build the GTM engine for a very early stage startup and get it off the ground. We are not an easy customer, but he keeps coming back with energy, ideas and solid execution.”

Jon Dweck

Founder & CEO at Space32

“Jared has been a fantastic partner on our GTM engineering. His deep technical expertise ensured everything, from infrastructure to deliverability, was handled with precision.”

Matt Sniff

CEO at CultureBot

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Questions buyers ask.

What does a fractional GTM engineer do?

Builds and runs the technical side of go to market a few days a week. Signal and intent pipelines, enrichment, outbound infrastructure, CRM architecture, attribution and the automations between them. Senior engineering without a full-time hire.

How is intent data different from signal data?

A signal is a single event, like a funding round or a new VP of Sales. It tells you why to reach out. Intent is behaviour building up over weeks, like pricing-page visits and topic research. It tells you when an account is in market. The best programmes use both.

What are AI agent teams for sales and RevOps?

Agents that do repeatable work such as account research, drafting outreach, cleaning CRM records and routing leads. A person approves every send and every CRM write, so the team scales without losing control.

Do you work with teams that have no GTM stack yet?

Yes. I build from scratch or fix what is there, across HubSpot, Salesforce, Attio, Clay and your own warehouse.

How fast can you start?

Most engagements start within a week, and the first system is live in your stack by week two.

Do I own what you build?

Yes. The code, the workflows and the data are all yours. I keep only my pre-existing frameworks.

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What I ship into your stack.

Live systems. Real wiring. No service decks. Eight systems I build, deploy and hand over inside your GTM and RevOps stack. Three of them run below.

online

Signal Engine

ICP × Signal × Persona

Job changes, hiring, funding and site visits land in Postgres within minutes, score the account and fire the right play.

$ watch --accounts 10000→ replies 0.8% to 23%✓ £3.2M pipeline in five months

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Intent Engine

Behaviour · Topic surge · Timing

A signal says why to reach out. Intent says when. Site visits, pricing-page returns and topic surges, scored daily, show who is in market this week.

$ score --intent --window 14d

→ site, topic and review-site behaviour, scored daily

✓ outreach lands while they are in market

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Mission Control

Domains · Sending · Deliverability

Domains, inboxes and aligned SPF, DKIM and DMARC before a single send. Replies, not bounces.

$ tail -f outbound.log→ 100+ meetings a month✓ 16.1% meeting rate

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Revenue Graph

CRM · Attribution · Forecast

One CRM model your reps trust, with attribution that ties revenue to source and a forecast agent that flags deal risk early.

$ forecast --agent deal-risk→ £1.4M stalled pipeline recovered✓ forecast accuracy 31% to 92%

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Agent Teams

SDR · Research · Marketing Ops · RevOps

Agents run the daily work of SDR, research and RevOps. A human approves every send and every CRM write.

$ team up --sdr --research --revops→ agents run the daily work, a human approves at the gates✓ a team's output without the headcount

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Lifecycle Autopilot

Lead routing · SLAs · Drift detection

New leads routed in seconds, SLAs enforced and lifecycle stages kept honest, with drift caught before it reaches the forecast.

$ pulse --route new-leads→ first touch under 60 seconds✓ lead to meeting 6% to 24%

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Enrichment Loop

Waterfall · 29 providers · LLM

Waterfall enrichment across 29 providers on your own keys. Cost per row stays flat at 500 rows or 8 million.

$ enrich --waterfall→ email coverage 62% to 94%✓ data costs down 68%

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Content Machine

SEO · AI search · Publish loop

Search data picks the topic. Claude writes to a brief. Automated checks hold the voice. Drafts land in WordPress, and performance picks the next batch.

$ engine run --batch weekly

→ 16 of 16 drafts pass the brief checks

✓ about $0.19 per long-form article

See the case studies →What is GTM engineering? →Book a 30-minute session →

Get in touch

Building from scratch or fixing what's there?

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