Jordan Heasman
Documentation for complex B2B data and security platforms, where correctness, governance, and scale matter.
I design and own documentation systems for enterprise platforms where mistakes have real consequences: misconfigured data models, broken governance rules, irreversible lifecycle decisions, and failed adoption of complex features.
I’m a senior technical writer with experience operating inside large, product-led SaaS organizations, working directly with platform and engineering leadership to ensure documentation functions as a control surface—not just an explanation layer.
My work centers on making high-stakes documentation decisions under constraint, where full correctness is impossible, review capacity is limited, and the cost of being wrong is high.
What I specialize in
I do my best work when documentation decisions determine platform correctness, governance, or adoption—not when polishing surface-level UX.
- Platform-level documentation for data and security systems (schemas, identities, policies, lifecycle rules, APIs)
- Governance-critical content where accuracy, consistency, and structure are non-negotiable
- Documentation systems at scale, including versioning, metadata, and machine-consumed content
- Automation-assisted documentation workflows focused on enforcement, consistency, and drift detection—not content generation
I intentionally do not focus on routine feature documentation or tutorials—those execution skills are assumed at this level.
How I operate
My role goes beyond writing pages.
- Prevent incomprehensible engineering output from reaching customers
- Translate complex platform behavior into durable documentation systems
- Design documentation that scales across teams, surfaces, and releases
- Reduce risk in areas where documentation decisions are effectively irreversible
I work closely with engineers, platform leaders, and product teams to make judgment calls about structure, scope, and long-term maintainability—not just wording.
About my approach to AI
Where automation is appropriate, I use AI strictly as infrastructure to support governance and review—not as a creative shortcut.
- Detect inconsistency and standards drift at scale
- Enforce documentation rules across large content sets
- Accelerate review, validation, and maintenance workflows
AI never replaces human judgment where documentation decisions affect customer data, governance, or compliance.
Selected work
A small, opinionated set of documentation initiatives where I owned system-level decisions under real constraints—platform scale, governance requirements, and cross-team complexity.
If you’re building a complex B2B data or security platform and documentation has become a bottleneck to adoption, governance, or scale, I’m happy to talk.
Work
System-level documentation initiatives focused on governance, consistency, and decision enablement at scale. Each case study shows how documentation problems were framed, constrained, and solved in large platform environments—prioritizing judgment, tradeoffs, and human-in-the-loop design over tooling or output volume.
Browse by tag:
Documentation Governance at Scale
- Business Problem: Platform documentation spanned multiple editions and teams, creating inconsistent metadata and discoverability gaps that could not be reliably detected or reviewed at scale.
- Assumptions: Documentation correctness is a governance responsibility, human review time is constrained, and automated systems must surface high-confidence signals without taking action.
- Decision: Determine which documents require human review for edition correctness—and which do not—without reviewing every page.
- Intervention: Implemented a contract-driven review system that evaluates documentation metadata in bulk and surfaces only review-worthy violations while remaining silent on compliant content.
- Results: Enabled targeted, repeatable governance of edition-specific documentation, improving review efficiency and confidence without changing authoring workflows or increasing coordination cost.
Contract-Driven Standards Enforcement at Scale
- Business Problem: Platform documentation standards drifted as content scaled, making user-centric quality inconsistent and costly to review manually.
- Assumptions: Editorial standards reflect organizational intent, human review does not scale, and automation must surface risk without rewriting content.
- Decision: Determine whether documentation meets the user-centric quality bar—or requires targeted review—without increasing editorial workload.
- Intervention: Introduced a contract-driven review system that evaluates content against editorial standards and surfaces only high-confidence quality risks.
- Results: Enabled stable, repeatable standards enforcement at scale, focusing reviewer effort where it mattered most while preserving publishing velocity.
Approach
I treat documentation as a system, not a collection of pages. My approach focuses on enabling correct decisions at scale—by making tradeoffs explicit, defining clear boundaries, and preserving human judgment where it matters most.
I optimize for durability, governance, and reviewer leverage, not short-term output or tooling novelty.
I establish documentation boundaries early by clarifying:
- What decisions the documentation must support
- Which risks matter at platform scale
- Where precision is required versus where silence is acceptable
This prevents scope creep and ensures documentation effort aligns with product intent and user workflows, rather than feature inventory.
I treat information architecture as a systems design problem.
Structure, navigation, and taxonomy choices are evaluated based on how well they:
- Support user goals and decision paths
- Scale across large, evolving content sets
- Remain stable as contributors and features change
I prioritize patterns that reduce cognitive load and avoid localized optimizations that degrade the system over time.
I approach standards as contracts, not guidelines.
Where possible, standards are encoded in machine-readable form to:
- Detect risk early
- Reduce subjective, taste-based review
- Preserve publishing velocity without lowering quality
Automation is used to surface high-confidence signals—not to rewrite content or replace editorial judgment.
I design documentation systems for long-term maintainability.
This includes:
- Clear ownership models
- Explicit deprecation paths
- Metadata and structure that support downstream consumption and reuse
The goal is not to eliminate human involvement, but to ensure human effort is applied where it has the greatest impact as the platform and organization grow.
Background
My career did not start in documentation teams or platform organizations. It started in high-consequence operational environments, where correctness, clarity, and judgment under pressure were non-negotiable.
Across roles in emergency dispatch, customer support, and enterprise software, the common thread has been the same: making complex systems legible to humans when mistakes are costly. That through-line is what eventually led me to platform-scale documentation governance.
At Adobe (via Ensemble Systems), my scope expanded from individual documents to documentation systems.
I worked on external developer documentation for large, cloud-native, distributed platforms—covering APIs, data governance, privacy orchestration, identity, and security features. As the documentation corpus grew across editions, teams, and products, so did the risk surface.
Rather than treating inconsistency as a writing problem, I approached it as a governance and detection problem.
That led to work focused on:
- Contract-driven documentation standards
- Metadata correctness and discoverability boundaries
- Confidence-gated review systems
- Human-in-the-loop enforcement at scale
This shift—from authoring content to designing systems that decide where human attention is required—defines my current focus.
I operate at the intersection of documentation systems, editorial judgment, and platform-scale governance, designing mechanisms that:
- Surface risk without rewriting content
- Preserve author autonomy
- Allow documentation to scale without collapsing under review debt