I build the product
behind the product.
B2B SaaS companies do not just ship a product. They ship a support, knowledge, education, AI, feedback, and escalation system around it. I design that Customer Experience Intelligence system so technical teams can reduce mean time to mitigate, increase adoption, improve retention, and scale customer experience without creating operational drift.
I build post-sales infrastructure for technical B2B SaaS teams that need stronger support automation, governed AI-assisted workflows, knowledge systems, customer signal loops, and adoption paths that help customers get value faster and stay engaged longer.
Your customers experience your systems,
not your org chart.
To your company, support, success, product, community, documentation, education, AI, and operations may be separate functions.
To your customers, they are one experience.
When those systems are disconnected, customers repeat context, get different answers in different places, wait for escalation, miss adoption moments, and lose trust in AI because the underlying content and data are unreliable.
That is not just a support problem.
It is an architecture problem.
When CX is not engineered,
Shadow CX takes over.
Shadow CX is what happens when customer experience depends on hidden spreadsheets, tribal knowledge, Slack favors, one-off automations, outdated help content, disconnected CRM fields, and escalation paths only three people understand.
It works — until it does not.
Then support gets noisy, AI gets unreliable, customers lose trust, and teams spend more time routing problems than solving them.
Customer Experience Intelligence replaces Shadow CX with observable, intentional, governed systems that reduce mean time to mitigate, improve pre-escalation resolution, increase adoption, and reduce churn risk.
Better self-service moves the system
closer to intent.
Self-service is not about making customers do more work.
Better self-service moves the system closer to the customer’s intent, so adoption, resolution, and recovery happen earlier in the journey.
That means the right answer, next step, academy path, escalation route, product signal, or feedback loop appears before the customer has to translate their need into your internal process.
See how the method works →The product behind the product.
I design and ship Customer Experience Intelligence systems that reduce mean time to mitigate, improve pre-escalation resolution, increase adoption, and strengthen retention, including:
Support automation
That reduces mean time to mitigate and avoidable escalation.
Knowledge systems
That help customers learn, adopt, and recover without waiting for support.
Community experiences
That capture signal, build trust, and turn peer resolution into reusable knowledge.
Governed AI support workflows
Grounded in trusted content, clean data, human review, and rollback paths.
Customer signal architecture
That makes context usable across support, success, product, and AI.
Escalation paths
That preserve context and trust when customers need expert help.
Feedback loops
That connect customer friction to product, support, education, and retention insight.
Governance and observability models
That keep automation, AI, and support workflows from drifting after launch.
Education and academy paths
That move customers from answer-seeking to product fluency.
Product engineering for the full
post-sales experience.
Product engineers build the core product experience.
Customer Experience Intelligence builds the systems that help customers adopt it, learn it, trust it, request changes to it, recover when they get stuck, and stay engaged over time.
The best post-sales systems work often shapes in-product journeys too, because it is informed by the places customers struggle after launch: support cases, search failures, community questions, academy engagement, documentation gaps, escalation patterns, feature requests, churn signals, and customer feedback.
Sometimes the work is connecting what already exists. Often, it means building the missing CX features companies should have had all along: feature request systems, closed-loop feedback, academy/LMS pathways, self-service journeys, support intake, escalation visibility, AI answer flows, and customer signal loops.
| Product Engineer | Customer Experience Intelligence |
|---|---|
| Builds the core product customers use | Builds the adoption, resolution, learning, and feedback layer customers rely on to succeed |
| Designs in-product workflows and features | Designs customer journeys across product, support, docs, community, academy, AI, and escalation |
| Ships product features | Ships CX features like feature request systems, closed-loop feedback, academy/LMS paths, self-service flows, support intake, escalation visibility, AI answer patterns, and knowledge capture |
| Owns user friction in the product | Owns customer friction wherever it appears across product, support, success, education, community, content, AI, or operations |
| Uses product analytics and user feedback | Uses product analytics plus support data, search behavior, community signals, academy engagement, CRM data, case trends, feedback, and escalation patterns |
| Improves activation, adoption, retention, and usage | Improves adoption, resolution, trust, self-service, feedback quality, escalation quality, and post-sales scalability |
| Turns user feedback into product improvements | Builds closed-loop systems that turn customer signals into product, content, education, support, and operational improvements |
| Connects engineering, design, and product | Connects product, design, engineering, support, success, education, community, content, RevOps, data, and AI |
Selected systems outcomes.
These outcomes came from treating support, knowledge, community, education, AI, Salesforce, and customer signals as one post-sales operating system: built to help customers adopt faster, get unstuck earlier, and stay engaged longer.
226%
growth in problems resolved before reaching support by moving mitigation paths upstream through predictive signals, inline guidance, and community-based routing
$192K
in annual operational efficiency gains, validated by Finance, through digital support improvements that reduced avoidable support demand and helped customers get unstuck earlier
4.96% → 8.8%
growth in community-assisted resolution as a share of incoming support volume after rebuilding a transactional portal into a self-service, routing, and customer signal system
78%
growth in quarterly community logins after improving routing, content visibility, and customer-facing paths for support, learning, and self-service
10%
ticket volume reduction and 25% fewer repeat questions after unifying Zendesk, Discord, and Discourse into an intent-based help architecture for a technical cybersecurity audience
Post-sales systems
designed, built, and shipped.
A focused diagnostic if you need to see where your customer experience infrastructure breaks. A fractional engagement if you need someone to own the systems that reduce mean time to mitigate, improve adoption, increase stickiness, and reduce avoidable escalation.
CX Architecture Audit
A focused 1-hour diagnostic of your post-sales experience infrastructure. I map how customers currently move through support, knowledge, community, education, AI, escalation, feedback, and internal handoffs — then identify the places where systems, ownership, and data drift apart.
The audit is designed to answer one question: where is the system too far from the customer’s intent to help them adopt, resolve, recover, or escalate with context?
I look for the places where customers are forced to translate their own need into your internal categories, search across disconnected surfaces, repeat context, miss learning paths, escalate without clarity, or submit feedback into a loop they cannot see.
Deliverables include:
- Customer intent path map.
- Self-service, adoption, and escalation breakpoints.
- Academy/LMS and learning-path gaps.
- Feedback-loop and feature-request gaps.
- AI, knowledge, and support automation readiness risks.
- Recommendations for moving answers, actions, escalation, learning, and feedback closer to customer intent.
Fully credited toward a Fractional engagement if we continue.
Book a CX Architecture Audit →Fractional Post-Sales Systems Architect
For teams that need senior systems leadership across support automation, knowledge, AI, adoption paths, and customer signal loops without hiring a full-time function. I help design, build, and operationalize Customer Experience Intelligence systems that reduce mean time to mitigate, increase adoption, improve retention, and scale support impact: support workflows, knowledge architecture, community strategy, education paths, AI readiness, data hygiene, observability, and cross-functional governance.
Built for companies that need someone who can move from strategy to system design to implementation before they scope a full-time post-sales systems role.
Explore Fractional Post-Sales Systems →3-month minimum. Rolling monthly after. 30-day cancellation. Limited to 2 concurrent partners at a time.
The bits and the Bee.
NahamSec and I at a Bugcrowd live hacking event in Las Vegas
I'm a technical operator, systems architect, and builder focused on the infrastructure behind customer adoption, support, and retention.
My work sits between support, success, product, community, knowledge, education, data, and AI. I help B2B SaaS teams turn fragmented post-sales operations into Customer Experience Intelligence systems that customers and internal teams can trust.
I believe most customer struggle is not user error. It is system failure. When customers cannot find the right answer, when support cannot see the right context, when AI gives inconsistent responses, when escalation depends on tribal knowledge, when adoption paths are unclear, or when feedback never reaches the product team, the issue is rarely one person or one tool. It is the architecture.
Frequently asked questions
Is this support operations, customer experience, business systems, or retention? +
Yes. I work across support operations, knowledge architecture, community, education, self-service, AI, business systems, and customer signal loops because customers experience those as one path, not separate departments. The downstream outcomes are adoption, retention, trust, and support scale.
Do you only advise, or do you also own execution? +
I own execution. The advisory work is useful because it identifies where the system breaks, but the larger value is building the roadmap, governance, workflows, routing, automation, and measurement model needed to fix it.
Is the audit useful if we don't continue? +
Yes. That's why it's a flat $1,500 and only credits forward if you choose to continue. The deliverable stands on its own.
How is this different from a UX audit? +
A UX audit usually focuses on interface and usability. My work looks at the full post-sales system: support flows, knowledge architecture, community, education, routing, self-service, AI surfaces, escalation, feedback loops, and the operational seams between them. The issue is often not one screen. It’s that the experience strategy and the systems behind it are owned separately, so the customer journey breaks across the boundary.
Can I just hire you full-time? +
Possibly. The fractional model exists for companies that need this function before they're ready to scope or hire it as a full-time role. That role may look like Customer Experience Intelligence, post-sales systems, digital support, support automation, or customer experience infrastructure, depending on the company.
Build the post-sales system behind your product
Tell me where
the experience breaks.
If your post-sales experience depends on hidden workarounds, disconnected tools, or heroic internal effort, it is time to engineer the system behind adoption, mitigation, escalation, feedback, and retention.