Series C startup · AI-powered outbound

Scaling revenue through an AI-powered outbound engine

A signal-driven outbound engine that booked 84 meetings and generated $12 million in new pipeline without drastically expanding headcount.

An AI chat interface illuminated in electric blue

Meetings booked

$12M

New pipeline generated

Automated

Data enrichment and routing

Real-time

CRM updates and sales alerts

The challenge

After securing Series C funding, a fast-growing startup faced a common growth bottleneck: their outbound sales strategy relied heavily on manual prospecting and fragmented outreach.

To sustain momentum without drastically expanding headcount, the company needed an automated, signal-driven outbound engine centred on their Ideal Customer Profile (ICP).

Prior to our initiative, the startup’s sales development representatives (SDRs) spent significant time manually sourcing accounts, verifying prospect data, and tailoring messages individually. This manual approach created several structural limitations:

Inconsistent account prioritisation: lack of intent-data aggregation made it difficult to identify ready-to-buy accounts.
Data fragmentation: prospect details resided across disconnected tools, slowing down execution.
Limited personalisation: personalising at high volumes was unfeasible without automated enrichment layers.
Siloed communication: outreach occurred across disconnected email and LinkedIn channels without integrated tracking.
Delayed follow-ups: inbound responses and engagement signals took too long to reach account executives.

To achieve their ambitious revenue targets, the leadership team approved our proposal, building a unified system capable of automated list building, multi-channel orchestration, and direct integration with their CRM.

The approach

We suggested implementing a system operating across six core pillars:

Segmented campaign architecture

Campaigns were split into three tracks: volume-based, sub-vertical micro-segments, and signal-based tracks triggered by buyer intent (e.g., website visits).

Automated enrichment pipeline

Account data from Apollo was funnelled into Clay for automated enrichment and qualification against ICP models, creating a dynamic prospecting pool.

A/B tested messaging

A structured framework for positioning, offers, and AI-assisted personalisation was continuously tested to optimise engagement rates at scale.

Multi-channel execution

Outreach was coordinated via email (using Instantly for deliverability and volume control) and LinkedIn (via HeyReach for automated, coordinated touches).

Closed-loop CRM

Full bi-directional synchronisation ensured all engagement activity updated the CRM instantly. High-intent signals and positive replies triggered immediate Slack alerts for sales follow-up.

Performance optimisation

Continuous monitoring of reply rates and meetings booked allowed the team to refine signals, messaging, and segments based on real-world pipeline data.

Strategic shift

Operational area	Legacy approach	AI-engineered approach
Targeting	Manual account selection	Signal-based automated routing
Personalisation	Manual / static templates	Enriched, context-aware messaging
Channels	Disconnected single channels	Integrated email + LinkedIn sequences
Visibility	Siloed activity logs	Real-time CRM updates & Slack notifications
Optimisation	Ad-hoc revisions	Continuous data-driven A/B testing

The impact

By combining automated data enrichment, multi-channel execution, and real-time CRM synchronisation, the company transformed outbound prospecting into a predictable revenue generator—booking 84 meetings and generating $12 million in new pipeline.

84 meetings booked
$12 million in new pipeline generated
Outbound motion shifted from manual outreach to fully automated data enrichment and routing
Sales readiness shifted from reactive follow-up to instant, signal-driven Slack alerts
The initiative successfully replaced a manual, reactive process with a scalable, automated, signal-driven revenue system capable of generating enterprise pipeline predictably and at scale