Strides Build lets product managers ship a fully autonomous AI feature without filing an engineering ticket. One script tag. Live before end of day. No ML required.
Everything on Your AI Roadmap. Done Today.
No dependency on engineering. No sprint wait. No ML expertise required.
One-Script-Tag Install
Paste one line of code and walk away. Strides auto-discovers every page, route, and API endpoint in your app without any configuration.
Autonomous Actions
The AI doesn't just answer questions — it executes tasks. Draft an invoice, reassign a ticket, send a report — using your real APIs, in your real product.
100% White-Labeled
Your branding. Your product name. Your colors. Strides is permanently invisible. No "powered by" badge, no Strides mention anywhere in the UI.
Strict Tenant Isolation
Built for multi-tenant SaaS from day one. Every user sees only their own data. Zero cross-tenant bleed at the architecture level.
Live User Context
Strides queries your real APIs for each user's actual data in real time. No static snapshots. No generic answers. The AI knows exactly what's in their account right now.
Streaming Responses
Responses stream back via SSE the moment generation starts. Sub-200ms TTFB globally via our edge CDN. No loading spinners. No waiting.
How Product Managers Can Own Their AI Feature — Without Engineering Dependency
AI features have moved from differentiator to baseline expectation in most SaaS categories. Buyers ask about AI capabilities during evaluation. Users compare products partly on the sophistication of their AI. Yet in most product organizations, AI features sit in the engineering backlog for quarters, blocked by the complexity of the underlying infrastructure work.
Product managers have always been in the position of defining what needs to be built and then depending on engineering to build it. Strides Build changes this equation for AI: PMs can install, configure, and launch the AI feature themselves — without the engineering dependency, without the sprint wait, and without needing to understand the underlying ML infrastructure.
The configuration layer is designed for PMs. You define the AI's scope (what topics it handles), its persona (name, tone, level of formality), its action permissions (which operations it can perform on behalf of users), and the rollout scope (all users, beta group, specific segments). All of this is done through a dashboard UI — no code, no configuration files, no engineering involvement required.
The PM ownership model creates better outcomes than the engineering-led model for one key reason: the person who knows your users best is now the person configuring the AI. PMs can iterate on the AI's behavior based on user feedback in real time, without waiting for an engineering sprint. The velocity of improvement is dramatically higher.
The fastest-moving product teams use Strides Build to establish an AI baseline quickly — get something live, measure how users interact with it, and iterate — rather than spending months building a perfect initial implementation that may not match actual user behavior. Ship fast, learn fast, iterate.
Frequently Asked Questions
Do I need to involve engineering at all?
For the basic installation, just one line added to your base HTML template — a 2-minute task for any developer. After that, a PM can configure, iterate, and manage the AI entirely through the dashboard without engineering involvement.
What can I configure without writing code?
Everything: the AI's name and persona, the topics it handles, which API actions it can take, the rollout scope (which users see it), the UI position and styling, and the welcome message. All through a point-and-click dashboard.
How do I measure whether the AI feature is working?
Strides provides a built-in analytics dashboard showing usage volume, user satisfaction signals, the most common queries, action execution rates, and support ticket deflection estimates. You can also export raw data to your existing analytics tools.
Can I A/B test different AI configurations?
Yes. You can run different AI configurations for different user segments simultaneously and compare engagement metrics. This lets you test different personas, scope configurations, or capability sets before rolling out to all users.
How do I handle user feedback about the AI?
The Strides dashboard includes a feedback review interface showing thumbs-up/thumbs-down signals and any explicit user comments. You can review these, identify patterns, and adjust the AI's configuration accordingly — without engineering.
What does the rollout process look like for a PM?
Start with a small beta group (5–10% of users or a specific segment). Review usage and feedback for one week. Iterate on the configuration. Expand to a larger group. Repeat until you're confident in the behavior. Full rollout. The whole process is PM-controlled.