

$
Funds raised
Monthly users in 2 months
Paid subscribers
AI-Native Identity Resolution
Intentify
We deployed AI-native identity resolution and managed nurturing systems that unify fragmented lead data, score intent, and convert traffic into qualified commercial demand.
Year
2026
Industry
Marketing Technology & Demand Generation
SERVICE USED
AI-Native Identity Resolution, AI-Managed Lead Nurturing
Challenge
Disconnected tools and manual qualification slowed conversion as lead volume and complexity increased.
(BP® — the problem)
Lead data lived across separate tools, so qualification depended on manual review and follow-up decisions, creating delays, duplicate records, inconsistent routing, and rising cost per qualified lead as volume grew.

(BP® — solution)
Designing systems that turn fragmented data into qualified demand
In most companies, lead generation is not one process but many. Traffic, forms, enrichment, scoring, and CRM updates live in separate tools, and the work of connecting them falls on people. As volume grows, this model stops scaling.
The solution is not to add another tool, but to redesign how demand moves.
Instead of fragmented steps, the lead lifecycle is structured into a continuous system where data, decisions, and actions are connected. AI is introduced not as a feature, but as part of the execution layer — enriching records, resolving identities, scoring intent, and triggering the next action in real time.
In practice, this means rebuilding the acquisition workflow around a few core principles:
• continuous data flow from capture to CRM instead of manual handoffs
• identity resolution across consented touchpoints instead of duplicate records
• embedded scoring and decision logic inside every nurture sequence
• consent, suppression, and retention rules enforced by the system itself
A key focus is removing dependency on manual qualification. Contacts are resolved, scored, and routed with defined logic, so outcomes stay consistent regardless of who reviews them. This reduces variability and stabilizes cost per qualified lead.
Integration plays a critical role. Existing channels are not replaced but connected — email, SMS, landing pages, paid traffic, and CRM operate as one workflow, with identity resolution linking fragmented touchpoints into a single customer record.
The same architecture now runs across six verticals — finance, real estate, healthcare, insurance, technology, and marketing — holding acquisition costs near $3 per lead, well below category benchmarks.
Over time, acquisition shifts from reactive to structured. Teams spend less time managing campaigns and more time acting on qualified demand. As volume increases, the system absorbs complexity instead of passing it on to people.
The result is not just automation, but a different way of acquiring customers — where demand is continuous, decisions are consistent, and revenue actions follow the data.

(BP® — client review)
They treated acquisition as a system, not a campaign. We now run six verticals on the same engine without adding headcount.

Mike Rodgen
CEO


$
Funds raised
Monthly users in 2 months
Paid subscribers
AI-Native Identity Resolution
Intentify
We deployed AI-native identity resolution and managed nurturing systems that unify fragmented lead data, score intent, and convert traffic into qualified commercial demand.
Year
2026
Industry
Marketing Technology & Demand Generation
SERVICE USED
AI-Native Identity Resolution, AI-Managed Lead Nurturing
Challenge
Disconnected tools and manual qualification slowed conversion as lead volume and complexity increased.
(BP® — the problem)
Lead data lived across separate tools, so qualification depended on manual review and follow-up decisions, creating delays, duplicate records, inconsistent routing, and rising cost per qualified lead as volume grew.

(BP® — solution)
Designing systems that turn fragmented data into qualified demand
In most companies, lead generation is not one process but many. Traffic, forms, enrichment, scoring, and CRM updates live in separate tools, and the work of connecting them falls on people. As volume grows, this model stops scaling.
The solution is not to add another tool, but to redesign how demand moves.
Instead of fragmented steps, the lead lifecycle is structured into a continuous system where data, decisions, and actions are connected. AI is introduced not as a feature, but as part of the execution layer — enriching records, resolving identities, scoring intent, and triggering the next action in real time.
In practice, this means rebuilding the acquisition workflow around a few core principles:
• continuous data flow from capture to CRM instead of manual handoffs
• identity resolution across consented touchpoints instead of duplicate records
• embedded scoring and decision logic inside every nurture sequence
• consent, suppression, and retention rules enforced by the system itself
A key focus is removing dependency on manual qualification. Contacts are resolved, scored, and routed with defined logic, so outcomes stay consistent regardless of who reviews them. This reduces variability and stabilizes cost per qualified lead.
Integration plays a critical role. Existing channels are not replaced but connected — email, SMS, landing pages, paid traffic, and CRM operate as one workflow, with identity resolution linking fragmented touchpoints into a single customer record.
The same architecture now runs across six verticals — finance, real estate, healthcare, insurance, technology, and marketing — holding acquisition costs near $3 per lead, well below category benchmarks.
Over time, acquisition shifts from reactive to structured. Teams spend less time managing campaigns and more time acting on qualified demand. As volume increases, the system absorbs complexity instead of passing it on to people.
The result is not just automation, but a different way of acquiring customers — where demand is continuous, decisions are consistent, and revenue actions follow the data.

(BP® — client review)
They treated acquisition as a system, not a campaign. We now run six verticals on the same engine without adding headcount.

Mike Rodgen
CEO


$
Funds raised
Monthly users in 2 months
AI-Native Identity Resolution
Intentify
We deployed AI-native identity resolution and managed nurturing systems that unify fragmented lead data, score intent, and convert traffic into qualified commercial demand.
Year
2026
Industry
Marketing Technology & Demand Generation
SERVICE USED
AI-Native Identity Resolution, AI-Managed Lead Nurturing
Challenge
Disconnected tools and manual qualification slowed conversion as lead volume and complexity increased.
(BP® — the problem)
Lead data lived across separate tools, so qualification depended on manual review and follow-up decisions, creating delays, duplicate records, inconsistent routing, and rising cost per qualified lead as volume grew.

(BP® — solution)
Designing systems that turn fragmented data into qualified demand
In most companies, lead generation is not one process but many. Traffic, forms, enrichment, scoring, and CRM updates live in separate tools, and the work of connecting them falls on people. As volume grows, this model stops scaling.
The solution is not to add another tool, but to redesign how demand moves.
Instead of fragmented steps, the lead lifecycle is structured into a continuous system where data, decisions, and actions are connected. AI is introduced not as a feature, but as part of the execution layer — enriching records, resolving identities, scoring intent, and triggering the next action in real time.
In practice, this means rebuilding the acquisition workflow around a few core principles:
• continuous data flow from capture to CRM instead of manual handoffs
• identity resolution across consented touchpoints instead of duplicate records
• embedded scoring and decision logic inside every nurture sequence
• consent, suppression, and retention rules enforced by the system itself
A key focus is removing dependency on manual qualification. Contacts are resolved, scored, and routed with defined logic, so outcomes stay consistent regardless of who reviews them. This reduces variability and stabilizes cost per qualified lead.
Integration plays a critical role. Existing channels are not replaced but connected — email, SMS, landing pages, paid traffic, and CRM operate as one workflow, with identity resolution linking fragmented touchpoints into a single customer record.
The same architecture now runs across six verticals — finance, real estate, healthcare, insurance, technology, and marketing — holding acquisition costs near $3 per lead, well below category benchmarks.
Over time, acquisition shifts from reactive to structured. Teams spend less time managing campaigns and more time acting on qualified demand. As volume increases, the system absorbs complexity instead of passing it on to people.
The result is not just automation, but a different way of acquiring customers — where demand is continuous, decisions are consistent, and revenue actions follow the data.

(BP® — client review)
They treated acquisition as a system, not a campaign. We now run six verticals on the same engine without adding headcount.

Mike Rodgen
CEO


$
Funds raised
Monthly users in 2 months
Paid subscribers
AI-Native Identity Resolution
Intentify
We deployed AI-native identity resolution and managed nurturing systems that unify fragmented lead data, score intent, and convert traffic into qualified commercial demand.
Year
2026
Industry
Marketing Technology & Demand Generation
SERVICE USED
AI-Native Identity Resolution, AI-Managed Lead Nurturing
Challenge
Disconnected tools and manual qualification slowed conversion as lead volume and complexity increased.
(BP® — the problem)
Lead data lived across separate tools, so qualification depended on manual review and follow-up decisions, creating delays, duplicate records, inconsistent routing, and rising cost per qualified lead as volume grew.

(BP® — solution)
Designing systems that turn fragmented data into qualified demand
In most companies, lead generation is not one process but many. Traffic, forms, enrichment, scoring, and CRM updates live in separate tools, and the work of connecting them falls on people. As volume grows, this model stops scaling.
The solution is not to add another tool, but to redesign how demand moves.
Instead of fragmented steps, the lead lifecycle is structured into a continuous system where data, decisions, and actions are connected. AI is introduced not as a feature, but as part of the execution layer — enriching records, resolving identities, scoring intent, and triggering the next action in real time.
In practice, this means rebuilding the acquisition workflow around a few core principles:
• continuous data flow from capture to CRM instead of manual handoffs
• identity resolution across consented touchpoints instead of duplicate records
• embedded scoring and decision logic inside every nurture sequence
• consent, suppression, and retention rules enforced by the system itself
A key focus is removing dependency on manual qualification. Contacts are resolved, scored, and routed with defined logic, so outcomes stay consistent regardless of who reviews them. This reduces variability and stabilizes cost per qualified lead.
Integration plays a critical role. Existing channels are not replaced but connected — email, SMS, landing pages, paid traffic, and CRM operate as one workflow, with identity resolution linking fragmented touchpoints into a single customer record.
The same architecture now runs across six verticals — finance, real estate, healthcare, insurance, technology, and marketing — holding acquisition costs near $3 per lead, well below category benchmarks.
Over time, acquisition shifts from reactive to structured. Teams spend less time managing campaigns and more time acting on qualified demand. As volume increases, the system absorbs complexity instead of passing it on to people.
The result is not just automation, but a different way of acquiring customers — where demand is continuous, decisions are consistent, and revenue actions follow the data.

(BP® — client review)
They treated acquisition as a system, not a campaign. We now run six verticals on the same engine without adding headcount.

Mike Rodgen
CEO
(qtf® — 05)
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