Case studies

Three systems, and the numbers we would rather not publish

Every figure below comes out of the client's own operating systems, with denominators attached. The cohorts that produced zero are here too. Clients are anonymized at their request; the numbers are not.

178,712
contacts qualified and source-attributed across the three engagements
2 of 3
engagements retired a bet on published evidence, denominators attached
Six sources
of market signal monitored continuously, with a measured pass rate at every stage
01 / Non-human identity platform

90 days to a working GTM system: the data that showed which offers convert

Workload IAM for AI agents, sold to security leaders. The quarter had one job: find the offer that converts. It did, and it retired the ones that don't.

Offer testing Scoring and routing Stack consolidation
Read case study 01
Interested rate by offer family
67%
40%
7.4%
0%
Conference invite
Happy hour
Research report
Mass outreach
110,984
contacts qualified
14
buyer-grade conversations
From qualified TAM to a rep's queue
27,800
Qualified TAM
4,176
Scored on awareness by readiness
15% of TAM
747
In-market
18% of scored
51
Sales-ready, routed to a rep
6.8% of in-market
0.8%
bounce, zero spam flags
100%
of 67,728 contacts attributed
02 / AI trust and safety platform

From a list engine to an activation engine, in four months

The April audit found 56,000 of 59,000 CRM contacts with no recorded activity. Four months later the funnel is visible end to end and the infrastructure is built. The infrastructure passed the test. The offer did not.

Attribution Deliverability Signal layer
Read case study 02
03 / Infrastructure software

Unmonitored market signals into an always-on intelligence system

The buying conversation happens in code repositories and technical forums long before anyone fills a form. Nobody was watching. The system's job was to say NO: 82% of captured signals never reached the team.

Signal ingestion De-anonymization Alert routing
Read case study 03
829 captured, 150 delivered
829
signal rows captured
150
delivered to the team, 18% of what was captured
82%
filtered out before a human saw it. A prioritization layer, not a firehose.
How we measure

Every figure is pulled from the operating systems themselves: the CRMs, the sending platforms, the engagement tools, the Demand Compass™ ledger, with baselines drawn from the initial audits, and was shared with each client before being shared publicly. Reply classifications are human-reviewed; positives exclude auto-replies. Scored records retain per-lead evidence links (the signal, the post, the source), so claims can be audited row by row. We publish the cohorts that produced zero. The finding is the product. We do not report "influenced pipeline" and we do not publish multiples we can't show the math for. There are no projections, multiples or influenced-pipeline figures anywhere on this page.

See the demand you can't see. Then the leaks that lose it.

The audit shows you the demand you can't see. The diagnostic shows you why your system misses it. The engine makes sure you never miss it again.