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Rivac AI Labs
Project & Program Management

Experiment Tracker

Running experiments without a system means tests overlap, results get interpreted loosely, and "ship it, the data looked good" can mean almost anything depending on who's reading the dashboard. This service builds the infrastructure and discipline behind your experimentation program, a hypothesis backlog, a consistent significance threshold, and a tracker that shows exactly what's running, what's queued, and what's been decided and why. It's for teams already running A/B tests who can't yet say with confidence which past experiments actually moved the numbers they claimed to. What you get is an experimentation practice where every test has a clear hypothesis going in and an honest verdict coming out.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

  1. 1

    Audit past and current experiments for how hypotheses, significance thresholds, and decisions actually get recorded today.

  2. 2

    Pilot a standardized experiment brief and tracker on two or three live tests to validate the format.

  3. 3

    Review pilot results with stakeholders to confirm the significance and decision criteria hold up under real scrutiny.

  4. 4

    Stand up the full experiment tracker and train the team to log and close out tests independently.

What You Get

Deliverables from this engagement

  • A standardized experiment brief template with hypothesis and success criteria
  • A live experiment tracker showing status, results, and decisions
  • A documented statistical-significance and decision framework
  • A historical log of past experiments and their verified outcomes

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Hypothesis Backlog

  • Idea Submitted by Team
  • Spreadsheet Hypothesis Backlog
  • Prioritized by Expected Impact

Test Setup

  • Manual AB Split via Feature Toggle
  • Sample Size Estimated by Hand
  • Test Launched

Manual Analysis

  • Results Pulled Into Spreadsheet
  • Significance Calculated Manually
  • Result Interpreted

Decision and Record

  • Ship or Kill Decision Made
  • Outcome Logged in Tracker
  • Slack Update to Team

Cloud-Native / Managed Services Architecture

Managed Experimentation Platform

  • Feature Flag Service
  • Managed AB Testing Platform
  • Experiment Configuration API

Cloud Data Pipeline

  • Event Stream Collector
  • Cloud Data Warehouse
  • Automated Significance Job

Results Dashboard

  • Live Experiment Dashboard
  • Historical Experiment Log
  • Analyst Review of Results

Standards

  • Standardized Experiment Brief Template
  • Significance Threshold Policy
  • Decision Recorded and Archived

Event-Driven & Automation-First Architecture

Triggers

  • Experiment Start Event
  • Sample Size Reached Webhook
  • Time Window Elapsed

Automated Pipeline

  • Automation Workflow Engine
  • Significance Check Job
  • Anomaly Detection Rule

Automated Alerts

  • Slack Alert on Significant Result
  • Slack Alert on Anomaly
  • Auto-Generated Result Summary

Closeout

  • Analyst Reviews Auto-Summary
  • Decision Logged Automatically
  • Feature Flag Auto-Updated

Enterprise-Grade & Compliance-Ready Architecture

Experiment Governance

  • Experimentation Review Committee
  • High-Risk Test Approval Gate
  • Statistical Standards Policy

Data Integrity

  • Redundant Data Pipeline
  • Data Quality Checks
  • Immutable Results Store

Compliance Controls

  • Customer Impact Review
  • Legal Sign-Off for Sensitive Tests
  • Access Control on Raw Data

Audit Archive

  • Full Audit Trail of Decisions
  • Historical Experiment Archive
  • Annual Compliance Report

AI-Augmented, Human-in-the-Loop Architecture

Live Experiment Data

  • Real-Time Event Stream
  • Historical Baseline Data
  • Segment and Cohort Data

AI Analysis Engine

  • AI Monitors Significance in Real Time
  • AI Flags Early Winner or Loser
  • Draft Recommendation With Confidence

Human Approval Checkpoint

  • Analyst Reviews AI Recommendation
  • Approve Ship Decision
  • Analyst Requests More Data

Ship and Learn

  • Feature Shipped to All Users
  • Outcome Logged in Tracker
  • Model Calibration Updated

Integration-Heavy / Ecosystem-Connected Architecture

Client Analytics Stack

  • Amplitude or Mixpanel
  • Feature Flag System
  • Data Warehouse
  • Marketing Automation Tool

Integration Connectors

  • Event Data Connector
  • Flag State Connector
  • Warehouse Sync Connector

Central Experiment Tracker

  • Unified Experiment Registry
  • Cross-Tool Result Aggregator
  • Significance and Decision Engine

Sync Back to Ecosystem

  • Winning Variant Pushed to Flag System
  • Results Pushed to Data Warehouse
  • Insights Pushed to Marketing Tool

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Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about Experiment Tracker

Most engagements like this start as a $500–$2,500 pilot, see full pricing.

Questions? Book a free call