The Client
Global CX outsourcer, GBP 30-40M revenue, London-based.
6 AI signal agents, one selector, two writers: the split-signal architecture
A London-based CX outsourcer (GBP 30-40M revenue) needed personalization that doesn't read like a mail merge. Six specialized AI agents each hunt one signal type; a selector picks the strongest; two writers turn it into copy.
Agencies & TalentGlobal CX OutsourcerAI Agent Architecture
- 6Signal agents
- 859Companies scored
- 12Sending domains live
- 30KMonthly send capacity
The Problem
Generic outbound was invisible to enterprise CX buyers.
The Build
Multi-agent AI personalization: 6 signal hunters, transformation scoring, BPO exclusion gates.
The Outcome
859 companies scored and routed, 12 domains warmed, 30K monthly capacity ready to fire.
“The split-signal architecture solves a quality problem seen across every previous build: AI personalization that sounds plausible but is not grounded in a specific, verifiable company fact. Separating "find the signal" from "write the sentence", with a deterministic selector in between, produces a measurable quality improvement. Every campaign built since uses this pattern.”
Context
The client
A London-based customer experience outsourcer with operations on four continents. Instead of throwing headcount at every problem, they combine human expertise with agentic AI to replace FTE-heavy delivery with AI-orchestrated business processes. Revenue sits around GBP 30-40M, with 25M+ customer interactions handled per year across 20+ markets.
They came with no CRM access, no prior outbound copy, and one hard rule: the outreach had to feel specific to each enterprise buyer, not like another templated blast that a CX leader deletes on sight.
Constraints
Why this was hard
They had been burned.
Previous lead-gen providers delivered generic messaging and zero conversions. Quality over speed was a contractual expectation, not a platitude.
BPO exclusion was contractual.
Never email another outsourcer, call center, or direct competitor. That exclusion had to be enforced at the data layer, not left to the copy.
No existing assets.
No CRM, no analytics, no templates. Every layer, from data architecture to copy, had to be built from scratch.
Enterprise ICP, narrowly defined.
Target revenue GBP 100M-5B+, headcount 500-10,000, across retail, travel, home services, food on demand, and financial services, with a hard cap of 3-4 contacts per company.
Messaging had to match transformation maturity.
A company that treats outsourcing as a cost lever needs a fundamentally different email than one that sees it as a technology transformation.
Architecture
The system
One pipeline, built around a strict separation of jobs. A company universe passes a BPO exclusion gate, then six signal agents run in parallel, a selector picks the single strongest signal, and only then do two writer agents turn that clean fact into copy. Research and copywriting never happen in the same prompt.
Results
Everything that exists today
This engagement was a pre-send infrastructure build. There are no campaign results to report yet: the deliverable is the architecture and the assets below, all live and ready to fire.
- 859Companies scored & routed
- 12Sending domains warmed
- ~30,000Monthly send capacity
| Metric | Result |
|---|---|
| Companies scored & routed | 859 |
| Signal agents (one job each) | 6 |
| Selector + writer agents | 1 + 2 |
| Transformation maturity tiers | 3 |
| BPO exclusion layers | 5 |
| Sending domains warmed | 12 |
| Sender personas built | 4 |
| Monthly send capacity | ~30,000 |
| Campaign status | Pre-send, ready |
Fit
Who this is for
Companies burned by generic lead generation
that need to see the infrastructure before they trust the output. If your last provider called {{first_name}} personalization, this is the alternative: agents that find real signals and writers that ground every line in a verifiable fact.
Businesses with contractual exclusion requirements
where emailing the wrong company is a compliance failure, not just waste. The 5-layer exclusion gate enforces business-critical targeting at the data layer, with an audit trail at every stage.
Enterprise-scale outbound targeting segmented buyer types
where one message cannot serve all prospects. The 3-tier transformation model routes a structurally different value proposition to each buyer, not just a different subject line.
Stack
Tools used
ClayData Enrichment & AI Architecture
SmartLeadEmail Sequencing
ApolloLead Database
Claude AIAI Signal Detection
HypertideDomain Warm-Up
BetterContactEmail Enrichment
FindymailEmail Verification
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