Data & AI Consultancy Case Study - US Market Entry, 3,617 Contacts

The Client

A 300-person European data & AI consultancy with deep enterprise credentials. Reference clients include global reinsurers, pharmaceutical companies, automakers, and private equity firms. Their strongest differentiator: a certified Palantir Foundry partnership spanning 6+ years and 150+ projects. They help large enterprises onboard, scale, and maximize ROI on the Foundry platform, including complex data integrations, operational use case development, internal team enablement, and platform operations at scale.

They also run a general data & AI consulting practice across other technology stacks, covering the same industries but with weaker differentiation against competitors.

Their European pipeline was healthy, built through founder-led sales, conference presence, and inbound. But they had a strategic mandate to expand into the US market, and the gap between "we should be selling in the US" and "we have a pipeline in the US" was total. No SDR function. No outbound infrastructure. No brand recognition in North America. One US-based team member, with everyone else operating on a 5-7 hour timezone delay from Europe.

The Challenge

This was not a "send more emails" problem. This was a market entry problem with compounding constraints:

Zero outbound infrastructure. No SDR team, no sending domains, no inbox warm-up, no sequences, no enrichment pipelines. Everything had to be built from scratch, not bolted onto an existing system.
The US market is completely new. European success does not transfer. The reference clients, the brand equity, the conference circuit, none of it carries weight with a VP of Data at a US insurer who has never heard of them. Every email has to earn credibility from the first sentence.
The most valuable contacts are also the most fragile. The client identified approximately 150-170 confirmed Palantir Foundry users across their target verticals, companies with active Foundry deployments where the consultancy's 6-year, 150-project track record would be immediately relevant. These contacts represent the highest-probability pipeline. Burning them with unproven messaging before reply rates and positioning were validated would waste the single best asset the campaign had.
Four distinct verticals, each requiring different positioning. Insurance & Reinsurance, Banking & Wealth Management, Life Sciences (biotech, pharma, biopharma), and Medical Devices. A data platform problem at a reinsurer looks nothing like a data platform problem at a medical device manufacturer. Generic "we do data & AI" messaging would be invisible.
Timezone operations gap. With one US-based team member and the rest of the organization in Europe, warm replies hitting the inbox at 2pm Eastern would sit unanswered until 8am CET the next day. The campaign had to be designed around this constraint.
HubSpot data skewed entirely European. The client's CRM was rich with European contacts but nearly empty for the US market. There was no existing list to warm-start from, no historical US engagement data to inform targeting, and no prior US campaign performance to benchmark against.

The Solution

I built a six-layer system: phased campaign architecture, Palantir signal detection, case study-driven personalization, size-calibrated diagnosis language, unified DNC compliance, and infrastructure at scale.

1

Phased Campaign Architecture

The single most important strategic decision was not what to send, it was what order to send it in. I designed a two-phase approach that treated the 150-170 confirmed Palantir contacts as the most protected asset in the campaign:

Phase 1: Four Parallel Vertical Campaigns (Non-Palantir): Insurance & Reinsurance, Banking & Wealth Management, Life Sciences, and Medical Devices. These campaigns target high-ICP companies that are not confirmed Palantir users. The goal: nail the messaging, measure reply rates per vertical, identify which angles and proof points generate responses, and iterate, all without touching the high-value Palantir contact pool.
Phase 2: Palantir Foundry-Focused Campaigns: 409 contacts. Approximately 150-170 confirmed Palantir/Foundry users, with the remainder being high-ICP or probable Foundry users based on signal research. This phase launches only after Phase 1 has generated enough data to validate positioning, subject lines, and CTA patterns.

This is the difference between "launching six campaigns" and "building an intelligence system that gets smarter before it reaches the contacts that matter most."

2

Palantir Foundry Signal Detection

For Phase 2, identifying which companies actively use Palantir Foundry was the enrichment challenge that defined the campaign. This is not a standard firmographic field, and no database has a "Foundry user: yes/no" toggle. I built a multi-source signal detection system that searched for Foundry mentions across four channels:

Job postings: companies hiring for roles that reference Foundry, Palantir, or specific Foundry modules (strongest hiring signal)
LinkedIn activity: employee posts, company updates, and profile descriptions mentioning Foundry deployments
Press releases: partnership announcements, implementation milestones, platform expansion disclosures
Blog posts and technical content: engineering blogs, case studies, and conference presentations referencing Foundry architecture

Each company received a signal strength categorization (Strong, Medium, or Weak) that directly influenced email copy. A company with a strong Foundry signal gets a specific, confident reference to their deployment. A company with a weak signal gets industry-relevant positioning without assuming Foundry usage.

3

Case Study-Driven Personalization: 85+ Quantified Proof Points

Generic "we helped companies like yours" lines do not work in enterprise data & AI. Decision-makers at this level want specificity: what was the problem, what was the outcome, and can you quantify it.

29 insurance proof points: including outcomes like $70M exposure eliminated, 40% premium increase through pricing model optimization, and claims processing automation across multi-billion-dollar portfolios
28 banking proof points: regulatory reporting automation, wealth management platform modernization, risk model deployment on production data
28 pharmaceutical/life sciences proof points: clinical trial data integration, supply chain optimization, approximately 1M CHF in annual savings from operational analytics

Each proof point was tagged with: industry, service line, pain point addressed, quantified outcome, relevant persona, and whether it involved Foundry or general data & AI work. The email copy system matched proof points to recipients based on vertical + company size + detected pain signals.

4

Five-Tier Company Size Diagnosis Language

Not every enterprise experiences data platform problems the same way. I built five company size tiers per vertical, each with calibrated diagnosis language:

Tier 1 (10,000+ employees): enterprise-scale framing: "operating at the scale where platform fragmentation creates measurable drag on decision velocity"
Tier 2 (5,000-9,999): growth-complexity framing: "past the point where adding headcount solves the data problem"
Tier 3 (1,000-4,999): platform maturity framing: "the data stack that got you here will not get you to the next stage"
Tier 4 (500-999): efficiency framing: "your data team is spending 60% of their time on integration work instead of insight work"
Tier 5 (200-499): strategic framing: "at your size, the first data platform decision is the one that compounds for a decade"

Each tier modifies the email's opening diagnosis, the case study selection, and the CTA framing. This is not five different campaigns, it is one campaign with five calibrated lenses, applied automatically via Clay enrichment data.

5

382-Entry Unified DNC List

In a campaign running six tracks across two phases with overlapping vertical targets, the deduplication challenge is real. I built a unified DNC list of 382 entries from three sources:

8 blacklisted domains: direct competitors, known hostile contacts, companies with explicit no-contact requests
325 HubSpot unsubscribed contacts: pulled from the client's CRM, representing every contact across their European database who had opted out of communications
44 competitors: data & AI consultancies, Palantir partners, and adjacent firms where outreach would be at best embarrassing and at worst intelligence leakage
5 internal domains: the client's own domains and subdomains, caught before they could appear as leads

This list caught 30 companies that appeared in both Phase 1 and Phase 2 contact pools. Without the unified DNC, those 30 companies would have received overlapping sequences from two different campaign tracks.

6

Infrastructure at Scale: 723 Inboxes, 14 Domains

Sending 3,617 contacts across six campaign tracks requires infrastructure that most agencies do not build for a single client:

723 inboxes provisioned and warmed across 14 domains
Wide inbox rotation to protect deliverability, and no single inbox sends more than a fraction of total volume
Warm-up started April 1, 2026, with a staged ramp over 2-3 weeks before any campaign emails go out
No links in any emails, a hard deliverability rule that eliminates click-tracking artifacts, reduces spam filter triggers, and forces the copy to earn the reply on its own merit
One contact per company, strictly enforced, with no "let me also email your colleague" escalation that signals mass outreach
All warm replies routed to the client's US-based team member for same-timezone follow-up

The Results

This campaign is active and in progress. Results below will be updated as performance data accumulates.

Metric	Result
Companies sourced	552
Companies after ICP filter	427
Total validated contacts	3,617
Campaign tracks	6 (4 vertical + 2 Palantir-focused)
Phase 2 Palantir contacts	409 (~150-170 confirmed Foundry users)
Inboxes provisioned	723
Sending domains	14
Case study proof points extracted	85+
Quantified outcomes catalogued	29 insurance, 28 banking, 28 pharma
DNC list entries	382
Cross-phase overlaps caught	30 companies
Company size tiers per vertical	5
Palantir signal sources	4 (job postings, LinkedIn, press, blogs)
Warmup start date	April 1, 2026
Phase 1 emails sent	Campaign Active
Phase 1 reply rate	Campaign Active
Phase 1 interested leads	Campaign Active
Phase 2 emails sent	Campaign Active
Phase 2 reply rate	Campaign Active
Phase 2 interested leads	Campaign Active
Best-performing vertical	Campaign Active
Total meetings booked	Campaign Active

The 85+ extracted case study proof points with quantified outcomes ($70M exposure eliminated, 40% premium increases, ~1M CHF annual savings) represent the kind of preparation that most outbound campaigns skip. The infrastructure numbers (723 inboxes, 14 domains, 382-entry DNC, 3,617 validated contacts across 6 tracks) represent what it actually takes to launch a US market entry for an enterprise consultancy. This is not a 200-lead test campaign. It is a full outbound engine built from nothing.

Who Is This For?

This approach works best for:

Enterprise services firms entering a new market with zero outbound infrastructure: if your pipeline has been founder-led, conference-driven, or inbound, and you need to build systematic outbound from scratch in a market where you have no brand, this is the architecture that gets you from zero to pipeline without the 18-month SDR hiring cycle.
Companies with a small pool of high-value targets they cannot afford to burn: if you have 100-200 contacts that represent your best pipeline opportunity and you are nervous about reaching them before your messaging is proven, the phased approach (validate on lower-stakes contacts first, then apply learnings to the high-value pool) protects your most important asset while still building momentum.
B2B firms with deep case study portfolios they are not leveraging in outbound: if you have dozens of quantified customer outcomes sitting in slide decks and PDF case studies but your cold emails say "we help companies like yours," you are leaving your strongest proof on the table. Extracting, tagging, and matching proof points to recipients by vertical and company size turns past delivery into present pipeline.