Valence AI is an SDK-first application security platform for teams shipping AI and modern web products fast. It brings scans, runtime visibility, findings, remediation, reporting, and launch workflows into one practical control plane.
Fast-moving teams were choosing between static scanners that dump long issue lists and runtime tools that only explain problems after something risky already happened.
Founders, product engineers, and lean security teams needed clearer signal, better prioritization, and actionable guidance without slowing delivery or handing over persistent repo access.
Scans, runtime events, alerts, compliance work, and release decisions were fragmented across separate tools, making it hard to know what mattered first.
Teams also needed reporting, remediation context, audit-ready surfaces, and a way to understand product-specific risk before shipping AI-enabled workflows.
Valence AI brings together the layers most fast-moving teams usually piece together by hand: developer-side scans, runtime visibility, structured findings, remediation guidance, compliance tracking, and release-focused reporting.
Security checks surface high-signal issues during development and inside delivery pipelines so teams can catch important problems before launch.
The platform extends beyond static scans with runtime monitoring that helps teams spot risky behavior in local and production environments.
Projects, issues, activity, and security posture are pulled into one control plane so teams can triage and act without bouncing between tools.
Findings are paired with clear context, why the issue matters, and what to fix next so the workflow stays grounded in action instead of noise.
Compliance Hub, reporting, and export flows support internal reviews, audit readiness, and trust conversations without turning the product into a compliance-only tool.
Teams can define product-specific checks, expand beyond default rules, and use Valence AI Copilot to understand risk faster with grounded assistance.
Building Valence AI meant turning security signal into a product workflow teams could actually use while shipping quickly.
Challenge: Valence AI needed to read like a serious application-security product for AI and modern web teams, not a narrow developer utility or a generic scanner dashboard.
Solution: I aligned the product story across landing, docs, and admin around one control-plane narrative: scans, runtime visibility, findings, remediation, compliance, and release confidence.
Challenge: Security workflows become hard to use when scans, events, projects, reports, and history are separated into disconnected surfaces with weak prioritization.
Solution: I helped shape the control plane around dashboard visibility, structured security issues, activity history, remediation context, and project-level posture so teams can move from detection to action faster.
Challenge: An SDK-first platform has to feel easy to adopt. Teams need to understand how local scan mode, CI ingestion, and runtime signals fit together without reading the product as heavy infrastructure.
Solution: I clarified the quickstart and product flow around connect, scan, monitor, and launch, making the SDK path feel like a practical developer workflow rather than a security program overhaul.
Challenge: Valence AI needed enough depth for security-minded teams while still being understandable to founders and product engineers who do not have a dedicated AppSec function.
Solution: I kept the UX focused on high-signal findings, guided remediation, clean reporting, and optional advanced controls so the platform scales from startup velocity to stronger governance needs.
Challenge: Default scan rules are rarely enough for AI products, admin-heavy SaaS, fintech workflows, or sensitive runtime actions where the real risk depends on the product context.
Solution: I leaned into custom audits, compliance views, runtime visibility, and release-readiness surfaces so teams can extend security coverage around the parts of their own product that matter most.
Challenge: Teams do not just want issue lists. They need enough structure to answer whether they are ready to ship, what needs review, and how to communicate that clearly to stakeholders.
Solution: I connected findings, reports, compliance progress, and release-readiness workflows into a more decision-oriented product experience that supports both engineering action and stakeholder review.
The product direction is about helping teams move fast without treating security as an afterthought or a separate post-release function.
| Metric | Before | After |
|---|---|---|
| Issue Detection | Noisy scans or ad hoc checks | High-signal findings across local and CI |
| Runtime Coverage | After-the-fact logs | Runtime risk visibility in one workflow |
| Security Triage | Fragmented tools | Unified findings dashboard |
| Release Readiness | Manual judgment | Reports, compliance, and remediation context |
As Founding Engineer, I worked across the product story, control-plane UX, docs, SDK onboarding flow, findings surfaces, compliance and reporting direction, and the broader workflow that connects detection, runtime visibility, and remediation. The focus was making Valence AI feel credible, actionable, and aligned to how modern teams actually ship.