Customer Companies (B2B)
Businesses across every other industry that run on your platform, your APIs, your data tools, your vertical software.
Runs on the platformWho it servesThe builder
The builder
is the buyer.
Technology companies and ISVs are the infrastructure layer for every other industry, SaaS platforms, cloud services, data tools, vertical software. Exclusively B2B, and the buyer is almost always a technical leader optimizing for product velocity and engineering efficiency.
Technical Buyers
CTOs, VPs of Engineering, and Heads of Platform, the decision-makers who own the architecture, the roadmap, and the budget.
Owns the decisionDeveloper End-Users
Engineers, data and ML practitioners, platform teams. The daily users whose velocity is the metric every product is judged against.
Ships the workEngineering velocity
Outcome Stakeholder
How it serves them
Four models, distinguished by proximity to the end customer and how much of the stack they own.
01
02
03
04
01
SaaS platforms
Horizontal or vertical software delivered as a service. Recurring revenue, customer-success-driven, retention as the operating metric.
HorizontalVerticalRecurring revenueCustomer success
02
ISVs
Independent software vendors shipping packaged, often industry-vertical software, increasingly migrating from on-prem deployments to cloud delivery.
Packaged softwareIndustry-verticalCloud migration
03
Infrastructure & developer tools
The platforms, SDKs, APIs, and cloud services that other software is built on, the substrate beneath the substrate.
PlatformsSDKsAPIsCloud services
04
Data & AI platforms
The tools other companies use to build their own data and AI capabilities, datasets, model orchestration, vector stores, evaluation harnesses.
Data platformsModel platformsVector / retrievalEval & ops
Code is the product. The lag is the constraint.
Software and APIs. The product is code, the distribution is digital, and the feedback loop between a customer signal and a product change is, in principle, immediate. In practice it is weeks or months, slowed by engineering backlog, review cycles, and deployment friction.
What we're optimizing
on the platform.
Five targets across the engineering org. The dials don't all move in the same direction. Velocity pulls against reliability, efficiency pulls against developer experience, adoption pulls against expansion. The builder decides the trade.
#StakeholderOutcomeDirection · Current → TargetPriority
00Engineering velocityThrough-lineBetter engineering outcomes.current → ship faster, break less, retain moreTiebreaker
01ProductVelocityHigher product velocity.idea → production → days, not quartersHigh
02EngineeringEfficiency · DXHigher engineering efficiency.$/story point · DX → betterHigh
03PlatformReliabilityHigher platform reliability.uptime · latency · error → SLO-cleanHigh
04CustomerAdoption · NRRHigher adoption, retention, and expansion.NRR % → >100%High
05Velocity ↔ ReliabilityConstrainedShip faster without breaking the platform.velocity ↔ reliabilityConstrained dial
The throughline
Engineering capacity is the constraint on everything.
The roadmap is longer than the team can execute, the technical debt is deeper than the budget can address, and the AI capabilities the market expects have to be built on top of infrastructure that wasn't designed for them. zeb is the most natural partner because we built for ourselves exactly what you are building for yourselves.
01 · What's known
The data fabric
- Product telemetryBehavioral
- Engineering work itemsBacklog
- Customer support signalVoice of customer
- The codebaseSource of truth
zeb + you
We build the resolving layer
03 · What gets decided
The decision
- Substrate · First responderPer work item
- Foundry · Spec → shippedCompresses cycle time
- Forge · Pre-roadmapExtends what is possible
- Product-AI featuresShipped to customers
- Engineering capacityCompounding
Where zeb connects.
The Engagement
= Reshaping toward this orbitNot configuration
The work itself
= Reshaping toward this orbitNot configuration
The work itself
01 · Source systems
where the engineering and product record originates
Jira · Linear · GitHub · GitLab · CI/CD · Datadog · Honeycomb · Amplitude · Mixpanel · Support02 · Data fabric & ontology
the unifying schema across code, work, and customer
Codebase graph · Work-item taxonomy · Product events · Customer identity · Incident taxonomy03 · Workflows
spec to iteration, bug to release, feedback to delivery
Spec → Design → Engineering → Review → Deployment → Monitoring → Iteration04 · Value chain handoffs
where signal degrades and velocity is lost
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Run the base loop
on your platform.
Substrate-native delivery for AI-native companies.