Agents, Signals & The Private Beta GTM Stack That Prints Pipeline
The edge of theory and reality — how I’ve been quietly building a next-generation GTM engine that aligns signals, buying groups, and agentic AI to pipeline.
I’ve been running a quiet private beta. A GTM stack that doesn’t live in a slide deck, or on a vendor’s roadmap, but in the wild: shaping accounts, spinning up buying groups, and handing pipeline straight to revenue.
This isn’t “future of marketing” fluff. It’s the workflow I’ve been quietly working on, testing live, and refining with real data. The goal: align the signals that matter to the pipeline that counts.
Because ABM isn’t the goal. Pipeline is. And the bridge between them? Buying groups, scored and orchestrated in real time.
In 2024 we brought full account reveal reporting, and in 2025 we linked it to landing page and website seasons. We’re now linking ad revenue closure across channels to high value actions on site, and scoring them - with the goal of creating an agile pipeline generating machine that turns ad impressions into pipeline
Why This Matters Now
The old playbook is breaking down:
CTRs tell you nothing about intent.
TALs are too blunt without live scoring.
ABM activation lags reality.
Discovery is dead because of zero click search. We need to re enlighten it with an upskilled paid media playbook
Modern GTM has to move faster, living and breathing in step with live market signals. 71% of B2B buyers are now millennial or younger. They’re not clicking ads and increasingly nor are they clicking the blue links on Google. 89% use LLMs to get the answers quicker, and we’re seeing the notorious B2B long cycles start to flatten. This needs a smarter interpretation layer to decipher noisy signals faster and rapidly deploy an agile GTM system into markets today - with live pipelines, dynamic creative and automated, agentic processes powering it 24/7
The stack I’ve been testing marries three core pillars:
FunnelFuel ABM Analytics → the on-site truth layer (real behavior, not guesses). Aims to capture all visits that map to accounts. Output is “ABM analytics”
Company Graph → the identity spine (who’s really behind the traffic). A multi signal layer that layers accounts over the analytics, using a multitude of signals - aiming to resolve as much traffic to account + persona as possible
HVA Scoring → the intent currency (fit × behavior × attention × role diversity). Web seasons are stitched to accounts and accounts are stitched to HVA’s. HVA’s act as bellwethers for intent and nurture towards pipe - so we’re scoring them to enable us to track and score each account and set GTM triggers off of. The scoring is bespoke to each client, so it can be tuned to meet each client’s known path to purchase. Machine learning can take converted accounts and reverse map the sessions they took over the weeks and months pre-purchase and at scale you decipher and reverse engineer the HVA patterns that proceed purchase. This unlocks a digital footprint of conversion inductive behaviour which can then be weaponised in the other direction - meaning we can work out an HVA predicated score which fires the starting pistol for the home straight 100m dash
And the kicker? Letting agentic AI operate the workflows so humans can focus on strategy, not logistics.
The Flow (Beta Stack, Simplified)
Traffic → FunnelFuel Analytics → Company Graph
→ HVA Scoring (fit x behaviour) → Live Segments with a 24/7 pipeline to enable an orchestrated GTM against them
→ Agentic Triggers (CRM, SDR, Ads, Email, Website)
→ Dynamic ABM + Buying Group Formation
→ Qualified Buying Group → Opportunity → Pipeline
Every step is wired to the next. No lag, no blind spots. When the signals align, the system moves.
The Scoring Model (Edge of Theory, Built for Reality)
This is where most GTM ops break. They score leads, or pageviews, or MQL checkboxes.
I’ve been building something different:
Base HVA points → aligned to funnel stage (pricing page ≠ blog view).
Attention multipliers → dwell, scroll, repeat visits. Signals that imply growing buying committee behaviour and ramp
Recency decay → scores fade if engagement drops. Procurement peaks and troughs, we’re aiming for a live and reactive system that chases the most current opportunities.
Fit multipliers → ICP overlays (industry, revenue, tech, region). We don’t want to be chasing poor fit prospects even when the signals align
Role diversity bonuses → multiple personas engaged = higher buying group probability.
The outcome isn’t “engaged lead.” It’s a live Qualified Buying Group (QBG) — an account mapped to opportunities, ready for sales.
Triggered GTM: When Signals Fire, Actions Happen
Here’s the beauty of the stack: you don’t wait. You trigger.
When QBG formed (score ≥85, ≥2 roles) → create opp in CRM, route to AE, launch SDR sequence, flip ABM creative to late-stage.
When mid-funnel surge (+30% in 7d) → expand TAL to near-neighbors, lift bids, run problem-solution creative, trigger exec-level email.
When dormant (21d silence) → reduce frequency, switch to education, SDR “park & watch” agent logs reactivation plays.
This isn’t campaign planning. It’s real-time GTM orchestration.
Agents as GTM Ops
I’ve mapped the workflows to agents, not humans:
Listener Agent → ingests signals, resolves identity.
Scoring Agent → applies HVAs, fit, and decay.
ICP Agent → enriches with firmographics, installs, history.
Orchestration Agent → writes to CRM, launches plays, routes tasks.
Sales Copilot → drafts AE notes, SDR sequences, role-specific talking points.
Creative Agent → rotates ABM ads and website CTAs per segment.
Insights Agent → weekly “what moved pipeline” + tuning recs.
Humans set the guardrails. Agents run the workflows.
Why This Is Pipeline-Centric GTM
Accounts are too abstract.
Leads are too narrow.
Buying groups are the operational bridge.
By anchoring to HVAs + fit, mapping to the company graph, and letting agents handle orchestration, the GTM engine finally runs on the same unit Sales does: pipeline opportunities.
The Edge You Can Steal
I’ve been pressure-testing this over the summer. It works. It’s not perfect yet — that’s why I’m calling it a private beta. But it already does what legacy GTM can’t:
Predictably form QBGs out of anonymous traffic.
Automate opp creation with live buying groups.
Orchestrate ABM dynamically, instead of via quarterly planning.
Shorten time-to-pipeline by cutting lag between signals and sales action.
Closing Thought
This is not a deck. It’s not “future vision.” It’s the private beta GTM stack I’ve been building, and it’s already generating pipeline.
It lives on the very edge of theory and reality — agentic AI wired into real signals, forming real buying groups, printing real pipeline.
The next phase is scaling it beyond beta. But for those reading here: you’ve just had a peek inside the machine.