Wizehire is a talent intelligence platform that began life as an ATS and now serves main street through mid-market teams. Their mission is to help non-expert hiring managers make great first (and next) hires by combining broad applicant reach with practical intelligence. That philosophy has always been data-first: instrument everything, learn from real user behavior, and keep the experience simple.

Before Statsig: data-driven but gated by process

Wizehire’s product development was rigorous and document-driven: long-form Business Requirements Doucuments (BRDs) from product, implementation with engineering, push to staging for verification, then a single promotion to production. Analytics lived in separate tools (Amplitude for product analytics plus a BI layer for database views). Despite the discipline, two problems kept surfacing:

• Non-representative staging: Real hiring volume only existed in production enviornments, making it costly or impossible to replicate meaningful test conditions pre-launch.
• Velocity and cost friction: Running experiments and rollouts through existing tools felt heavy and expensive for a lean team—and not especially self-serve.

The result? a culture that valued data and shipped quality but lacked a fast, safe way to test in production and iterate based on what real users were doing

Choosing Statsig: a single platform for speed, control, and clarity

Wizehire compared several platforms with a simple goal. They wanted one workflow to control rollouts, run experiments, and see results in context. Statsig felt complete yet simple for a lean team, and it matched how they prefer to build for the following reasons:

• Feature management + experimentation + analytics together: One platform to gate features, run tests, measure results, and visualize outcomes without switching tools.
• Self serve from day one: Quick setup, fast instrumentation, and an interface that builders and stakeholders can use with confidence.
• Cost-effective: Right sized pricing for a smaller, fast moving team, with all three tools in one platform at a lower cost than Amplitude alone for analytics. Choosing Statsig meant fewer handoffs and clearer decisions. The team could ship behind flags, validate in production, and read impact in the same place, all while keeping costs predictable.

Choosing Statsig meant fewer handoffs and clearer decisions. The team could ship behind flags, validate in production, and read impact in the same place, all while keeping costs predictable.

“Statsig is the easiest platform I’ve used to instrument events and verify they’re working.”

Alex Preston

Product Manager, Wizehire

Adopting Statsig across Wizehire

Wizehire adopted Statsig as a unified product engine. The team relies on Feature Flags, Experiments, and Product Analytics to gate features, run tests, and understand user behavior in context. Statsig is widely used across the organization. The primary teams using Statsig are as follows:

• Product Management: Defines flags and experiments for each feature; builds dashboards and funnels for every product area; uses cohorts to compare industries and ICPs; reviews User Journeys to reduce friction.
• Engineering: Ships with feature flags first; runs targeted rollouts by company, user, or job post; relies on instant kill switches to protect stability.
• UAT and QA: Tests in production with scoped flags on test entities; verifies behavior under real traffic before wider exposure.
• Tech Support and Customer Success: Investigates user sessions and trends; validates soft signals from support with behavioral data to speed root cause analysis.
• Leadership and stakeholders: Monitors adoption, click rates, and product health through standardized dashboards.

Feature Flags as the default safety net

Feature flags are now required in the software development lifecycle (SDLC). Every meaningful change ships behind a flag. UAT turns flags on for controlled test entities in production to validate with real data. Rollouts expand by cohort such as company created date or last login so legacy customers remain stable while newer cohorts try the update. If something looks off, the team disables the feature in seconds without a code rollback.

“Testing in production is essential for us. Production data is the truth. With Statsig flags we validate safely on real traffic and then expand exposure in controlled steps.”

Alex Preston

Product Manager, Wizehire

Experiments that move the funnel

Wizehire runs controlled experiments on high leverage flows such as self serve signup and apply. A prior signup test delivered a statistically significant conversion lift, which is meaningful for the business because organic signups power much of the company’s growth. More conversion tests are scheduled for the end of the year using the same pipelines and scorecards to measure lift with confidence.

Product Analytics that guide design

Each product area has a dashboard for adoption, click rates, and long term health. Cohorts make cross segment comparisons simple by ICP or industry. User Journeys reveal extra clicks and back and forth navigation that slow people down, which leads to concrete design changes that remove steps and speed time to value.

Faster shipping, safer rollouts, measurable gains

Statsig helps Wizehire ship faster, read real behavior in context, and manage risk with more control and less overhead.

Shipping velocity has increased by roughly four times year over year, with the second half of 2025 already ahead of the first. A flags first SDLC and UAT in production shorten QA loops and remove the need to mirror production conditions in staging.

A prior signup experiment produced a statistically significant lift in conversion. That matters because organic self-serve signups drive a large share of growth, and more conversion tests are queued for later this year.

Risk on rollouts has dropped. Exposure is scoped by cohort, legacy users stay stable, and if something looks off the team disables the feature in seconds without a rollback. Only data migrations sit outside this safety net.

Statsig also consolidates work that once lived in separate tools. Gating, testing, and measurement now happen in one place, which lowers cost and reduces cognitive load for a lean team.

“We’re shipping more than ever, and our functional risk on feature rollouts is effectively near zero. If something looks off, we flip a switch. It’s changed our pace and our confidence.”

Alex Preston

Product Manager, Wizehire

What’s next: AI-accelerated building, monitored by intelligence

In 2025, Wizehire shifted from an ATS to a broader talent intelligence platform with AI at the center. The next phase focuses on faster instrumentation, earlier product signals, and tighter eval to experiment loops. The team is exploring Statsig’s MCP server to propose event instrumentation that matches internal naming so engineers can spend more time on product while analytics stay consistent.

They also want experience level health signals. User level anomaly detection, like a sudden drop in clicks on a key step, would complement server monitoring and surface issues sooner. Wizehire already runs prompt and model evaluations with Langfuse and is open to head to testing Statsig’s new AI evals product suite.

About Wizehire

Wizehire is a talent intelligence platform that helps Main Street and mid-market companies find, evaluate, and hire great candidates. By combining broad applicant reach with insights that simplify every step of hiring, Wizehire empowers non-expert hiring managers to make confident, high-quality decisions fast.