heyarnoux.Collaboration

Fractional GTM x AI

Let's Build Your GTM x AI Engine.

Implementation, not advice. Done with you. I go inside your company and we build your GTM engine together: the signals, the workflows, the agents, the reporting. I run the build with you week by week, and place GTM Engineers in your team when build capacity is the bottleneck. 20+ times, B2B and B2C.

Two ways in

Pick how we work together.

Two standalone options. Head coach engagement for the full integration, or GTM Engineers in your team when you have the strategy and need build capacity. Pick one. Add the other later if you want.

Option 01 · Full engagement

I'm your Architect (and your head coach).

I design the architecture, lead the build and stand up your Center of Excellence, embedded in your team. GTM Engineers join when build capacity is the bottleneck.

Embedded Sprints + Marathons Center of Excellence

What you get

  • I'm your architect and head coach. The steering seat: architecture, sequencing, weekly cadence, C-suite translation.
  • Audit lives inside the work. Stakeholder interviews, maturity scorecard, agentic architecture, ranked pilot backlog.
  • Sprints ship quick wins. 2 to 4 week pilots in production. First wins inside 90 days.
  • Marathons harden and embed. Pilots scale to production. Training, playbooks, runbooks, monitoring.
  • Center of Excellence inside your org. A permanent function that owns GTM x AI after I leave.
  • 20+ implementations across B2B and B2C. Same playbook. Same traps avoided.
Book a fit call →

Option 02 · Dev placement

GTM Engineers, in your team.

Trained on my playbooks, up and running in days, not months. Each builds 2 to 3 proofs of concept in parallel, with my architecture and weekly oversight. The bottleneck is almost never ideas. It's build capacity.

Dev placement Cancel anytime Money-back month 1

What you get

  • Strong GTM x AI engineers embedded in your Slack, repos and daily rhythm.
  • Full-stack: React, Next.js, Node, Python, AWS / Azure / GCP, Supabase + Vercel for early stage.
  • AI-native: Claude Code, agentic workflows, MCP servers, automation. No vibe coding.
  • I architect and oversee weekly. Each engineer runs 2 to 3 POCs in parallel.
  • Easy cancellation. Swap the engineer if fit isn't right. First-month money-back.

Investment

EUR 5,500 to 7,500 / engineer / month

Book a scoping call →

Flagship case · GTM x AI in production

A €2.5B fintech rebuilt its GTM function from zero. I architected it.

Backbase runs the platform 120+ banks sit on. Over the past year I led its GTM x AI transformation into a GTM OS the team owns and runs. Their CMO published the numbers.

10xAccount coverage
per marketer
85%+New pipeline
YoY
60%+Deal size
YoY
Read the Backbase case study →

The exact engine this page sells, built and shipped.

Human AI

The engagement

Three layers. Built to stack.

Always starts with the Sprint. The retainer follows. Engineers plug in when build capacity is the bottleneck.

The engine in motion

Chaos in. Playbooks out.

What the engine actually does, every day. The buzzword storm becomes a working machine that ships playbooks that move revenue and cut costs.

Want the full methodology? How I take a company AI-native, one BU at a time.

Read the transformation playbook →

An example engine

What an engine might look like.

You don't need to absorb the left side. That's the engine room: data, stack, governance. The plays on the right are what most clients pick first. The shape stays the same across engagements. Yours will look different in detail.

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Data Sources

Internal data

Your operational tools

  • CRM + pipeline
  • Product + analytics
  • Customer success signals
  • Call intelligence

External signals

Public + 3rd-party

  • Web + competitor intel
  • Account intent signals
  • Industry + regulatory

The Stack

Application layer

Where work happens

  • Integration engine
  • Workflow orchestration
  • AI agents
  • Observability + logs

Data layer

Where data lives

  • Database + warehouse
  • Vector store
  • Auth + access control
  • Row-level security

AI Processing

LLM Gateway

The secure pipe

  • Zero data retention
  • Model-agnostic routing
  • Token + cost tracking
  • Compliance guardrails

LLMs

Best model per task

  • Closed + open models
  • EU-region options
  • Local / private when needed

Governance

EU AI Act, ISO, SOC 2

  • Audit trails
  • PII + redaction
  • Human in the loop

Playbooks

Signal Engine Live

Score 1,000s of accounts on intent + fit. Hot ones get pushed to AEs daily.

Programmatic SEO Live

Auto-generated pages at scale. Topic clusters, internal linking, GEO-ready.

Performance Ads Automation Building

Creative briefs, copy variants, targeting, bidding. Closed loop with conversion data.

Self-Learning CRM Building

Data hygiene, auto-enrichment, segmentation that updates itself. Bad data dies.

Voice Agent Planned

Out-of-hours and callback queue. Lower-stakes calls handled.

Customer Health Cockpit Planned

Churn signals, expansion alerts, renewal forecast. CS gets early warning.

Where do we start?

Pick the first agent we ship together.

Your backlog gets cut at the Sprint workshop. Quick wins ship in 2-4 weeks. The rest gets sequenced as marathons over quarters. What's the bottleneck you'd ship first?

Browse the full backlog

The vital trifecta

What makes a GTM x AI engine actually ship.

Three legs. All three needed. Most teams have one, sometimes two. The third is the binding constraint. We close it together.

In place

Leg 01

Leadership mandate

A sponsor at the top who actually wants this. Permission to move fast. Air cover when something breaks. Without it, the work stalls in committee.

Extend

Leg 02

Infrastructure

A stack you can ship against. Data accessible, tools API-able, security non-blocking. Most teams have the foundation, we extend it for AI workloads.

The work

Leg 03

The team

GTM x AI engineers embedded in your daily rhythm. The binding constraint for most orgs. Players from my bench plug in, or I coach yours up.

What I bring

Pattern recognition, not theory.

20+

Implementations

Same playbook, run 20+ times across B2B and B2C. You won't pay for me to learn how this fails.

∞

Architectural guidance

Agent design, MCP stack, vendor selection, compliance guardrails. Decisions that compound or rot for years, locked at the right time.

0

Lock-in risk

Sprint stops at workshop if numbers don't add up. Engineers refund the first month. Retainer cancellable monthly after the minimum.

How it runs

A typical 120 days.

Week 1 to 2
Stakeholder interviews. I sit with your specialists, sponsors, compliance, product / IT. Each session surfaces facts, pain, the buyback list.
Week 3 to 4
Architecture, scorecard, ranked backlog. Maturity-gap rated. Quick wins ranked. Agentic architecture drafted. Roadmap deck shaped.
End of Sprint
Two workshops + executive readout. CEO or sponsor in the room. Path locked. Sprint retro hands off to the retainer.
Week 5 to 12
Retainer engages. Weekly cadence, steering meetings, KPI dashboards. First pilots ship from the ranked backlog. Engineers plug in if build capacity is the bottleneck.
Week 13 to 17
Compounding mode. Pilots scale to production. Backlog turns over. Bigger bets unlocked. Quarterly recalibration with your sponsor on the horizon.

The work

The plays we build.

A catalog drawn from 20+ implementations across B2B and B2C. Your backlog gets cut from this list, scored on impact × ease, sequenced at the Sprint workshop. Nothing here is theoretical, every play has shipped to production somewhere.

Content + SEO + GEO

  • Programmatic SEO pages
  • GEO, get cited in LLM answers
  • Blog brief assembler
  • Product description writer
  • Newsletter generator

Account Intelligence + ABM

  • Signal engine, score accounts by intent
  • Per-account research dossier
  • ABM one-pager generator
  • Competitive radar
  • Buyer-committee mapping

Outbound + Inbound

  • Personalized outbound at scale
  • Inbound qualification bot
  • Reply triage + routing
  • Re-engagement campaigns
  • Event follow-up

Conversion + Performance

  • Ad creative engine
  • Landing page generator + A/B
  • Conversion health monitor
  • Lead scoring + routing
  • Pricing page personalization

Lifecycle + LTV

  • Cart abandonment recovery
  • Win-back + loyalty campaigns
  • Channel-attributed LTV
  • Next-best-SKU recommender
  • Predictive lifecycle model

Pipeline + Forecasting

  • Pipeline + QBR auto-gen
  • Forecast model
  • Win/loss analysis
  • Churn prediction
  • Revenue attribution

Customer Success + Retention

  • Health-score cockpit
  • Renewal forecast
  • Onboarding playbook
  • CS QBR auto-gen
  • Expansion alerts

Sales Enablement

  • Call intelligence + coaching
  • Deal review bot
  • Battle card auto-update
  • Proposal generator
  • Pricing recommender

Brand + Reviews + PR

  • Review monitor + auto-reply
  • Brand mention sentiment
  • Customer story generator
  • Influencer outreach
  • PR / earned media tracker

Voice + Conversational

  • Out-of-hours voice agent
  • Callback queue automation
  • Inbound voice triage
  • Call summarizer
  • Sales coaching agent

Internal Brain

  • Executive RAG brain
  • Decision log
  • New-hire onboarding agent
  • Vendor selection assistant
  • Strategy doc consolidator

Ops + Data

  • CRM gap analysis + repair
  • Data hygiene bot
  • SaaS audit
  • Integration repair
  • Comp / quota model

Example backlog · first 90 days.

Cut from a real engagement. Your version gets reshaped at the Sprint workshop, scored on impact and ease, sequenced for compounding momentum.

Play Impact Ease Time to ship Type
Pipeline + QBR reporting auto-gen High Easy 2 to 3 weeks Quick win
Per-account research bot High Easy 2 to 3 weeks Quick win
Signal engine, score target accounts High Medium 4 to 6 weeks Marathon
Performance marketing creative engine Medium Easy 2 to 3 weeks Quick win
Sales call intelligence + coaching High Medium 3 to 4 weeks Marathon
Customer health-score cockpit High Medium 4 to 6 weeks Marathon
RAG executive brain High Medium 4 to 6 weeks Marathon
Re-engagement campaign automation Medium Easy 2 weeks Quick win
Programmatic SEO + GEO engine High Hard 6 to 8 weeks Marathon
Out-of-hours voice agent High Hard 6 to 8 weeks Marathon
Channel-attributed LTV model High Hard 6 to 8 weeks Marathon

Running it

Your GTM x AI OS, in motion.

What the dashboard looks like 90 days in. KPIs, plays running, alerts triggered, this week's wins. Real shape, illustrative numbers.

gtm-os · weekly view
Pipeline coverage
3.4x
↑ 0.6x vs last quarter
Signal → meeting
11.2%
↑ from 4.1% baseline
Win rate
28%
↑ 6 pts
Churn, annual
7.1%
↓ 2.3 pts
Active plays · 8 running, 1 scheduled, 1 paused
  • Signal engine · 4,127 accounts scored daily
  • Pipeline + QBR auto-gen · Mondays 6am
  • Per-account research bot · on-demand
  • Outbound sequence builder · 38 sequences live
  • Health-score cockpit · 612 accounts monitored
  • Call intelligence · 100% calls processed
  • Programmatic SEO · 1,840 pages indexed
  • RAG executive brain · 47 queries this week
  • Re-engagement campaign · launches Tuesday
  • Voice agent · pilot complete, decision pending
Who's hot today · 6 alerts
  • CriticalAccount #142 · CEO + CFO on /pricing 4x this week
  • HighAccount #87 · renewal in 14 days, score dropping
  • HighAccount #213 · 3 buying signals in 48 hrs
  • MedAccount #44 · added 8 new users
  • MedAccount #168 · funded yesterday
  • LowAccount #29 · re-engaged after 41 days
This week · what shipped
  • SHIPPEDABM asset generator · 47 personalized one-pagers sent to target accounts
  • SHIPPEDBattle card auto-update · 12 cards refreshed from competitor moves
  • SHIPPEDCS health alert · 9 accounts flagged before renewal, 6 saves in motion
  • SHIPPEDProgrammatic SEO · 23 new pages indexed, 8 ranking top 10
  • SHIPPEDInbound qualification bot · 142 leads triaged, 18 booked direct

Case study · B2C growth OS

Programmatic SEO + paid that optimizes itself.

A fast-scaling B2C brand (anonymized) rebuilt acquisition into one growth OS: an SEO engine built for the AI-answer era, and paid advertising that tunes itself daily. Owned traffic that compounds, spend that chases customers who stay.

12,000+pages 4.2xorganic 41%lower CPA
Read the case study →

Next step

30 minutes. Decide together.

A fit call covers your context, the gaps, what the Sprint would scope, and whether it makes sense to start. No pitch deck. No CRM follow-up sequence.

On the call we cover
Book a fit call → Read the playbook Email me direct
Companies I've worked with
Google  ·  Backbase  ·  SkyShowtime  ·  Raiffeisen Bank  ·  SOSU  ·  Feedzai  ·  GoodHabitz  ·  Marktlink  ·  Tribes Media  ·  Everconvert  ·  Growth Tribe  ·  and 10 more
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