Public business data is incomplete, duplicated, and changes often
B2B Services / Delivered project
AI Lead Generation System
A compliant research pipeline for identifying relevant accounts, enriching approved data, and preparing personalized outreach for review.
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StatusDelivered project
IndustryB2B Services
SystemData → Intelligence → Action
01 / Problem statementDelivered project · B2B Services
The operating problem
Revenue teams spend hours assembling account context, while uncontrolled scraping and automatic outreach introduce data-quality, compliance, and reputation risk.
Conditions the system must survive03 operating constraints
Relevance criteria differ by segment and campaign
Uncontrolled enrichment or outreach creates compliance and reputation risk
02 / How we approached it
From operating uncertainty to testable evidence.
The work was decomposed into four engineering decisions. Each one produced an artifact the customer could inspect, test, and carry into deployment.
01Frame
Define target-account criteria and permitted sources
Evidence produced
Target-account, source-permission, and exclusion policy
02Evaluate
Collect and normalize public business signals
Evidence produced
Entity-resolution and enrichment-quality benchmark
03Engineer
Score relevance with explainable features
Evidence produced
Explainable relevance scoring review with revenue teams
04Operationalize
Draft outreach for human approval and CRM logging
Evidence produced
CRM workflow with deduplication, approval, and sending controls
03 / Deployment record
Deployed around the workflow—not beside it.
The system boundary includes where inference runs, how evidence reaches existing tools, and how people handle uncertainty after launch.
Implementation statusDelivered project
01
TopologyCloud research and enrichment pipeline with isolated source connectors and approval queues.
02
IntegrationApproved public sources, CRM records, enrichment providers, and outreach tools connect through policy-scoped workflows.
03
OperationRecords are deduplicated and freshness-scored; humans approve targeting and outreach before any external action.
04 / Evaluation metrics
What must be measured before the system earns trust.
Evaluation covers model behavior, workflow burden, and production performance. The metric defines the gate; the customer baseline and acceptance threshold define the target.
01Evaluation gate
Account-match precision
How it is measured
Correct company and domain resolution among researched records.
What it decides
Controls CRM contamination and duplicate outreach.
02Evaluation gate
Enrichment completeness
How it is measured
Approved target fields populated with current, cited evidence.
What it decides
Measures whether research is decision-ready.
03Evaluation gate
Lead acceptance rate
How it is measured
Accounts accepted by sales or research reviewers.
What it decides
Tests whether scoring reflects commercial relevance.
04Evaluation gate
Correction and compliance rate
How it is measured
Records requiring correction, exclusion, or source-policy intervention.
What it decides
Sets automation boundaries and source policy.
05 / Customer perspective
Value has to appear in the customer’s operating day.
What matters in practice
The system should prepare a well-sourced account record and draft—not silently scrape, invent personalization, or send messages.
06 / Technology context
Tools follow the system—not the other way around.
Final architecture depends on data quality, operating conditions, integrations, risk, and evaluation criteria established during discovery.
07 / Continue exploring
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