Indeed's Enterprise Data and Analytics team needed to expand its machine learning framework with predictive models for client retention risk and uplift opportunities. The team required a Gen AI solution that could support a large sales organization. They needed the solution to operate across multiple client-facing portals. Data scientists and ML engineers were supplied to help scale the existing ML framework. The implementation added predictive models focused on retention risk and uplift opportunities. The solution was built to serve sales representatives through multiple client-facing portals. The expanded framework supported thousands of sales representatives. It delivered business insights to hundreds of employees each day. It enabled broader access to predictions for client retention risk and uplift opportunities across the organization.

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KindHealth, a consumer health plan cost-saving solution, struggled to scale in a complex regulatory environment. The team had to operate amid diverse data reporting standards. These constraints made it harder to expand while staying aligned with compliance expectations. KindHealth brought in four senior engineers with insurance experience to match the specialized skills needed for its initiatives. The team revamped authentication through a standalone SSO that integrated across projects. They also developed an automated data entry solution to support accurate, consistent data reporting. The work ensured accurate and consistent data. It boosted development velocity by bringing in engineers familiar with the industry compliance landscape. It also established a single source of truth for data reporting across the organization.

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Headspace Health faced a rapidly scaling user base and anticipated a spike in customer support requests. It needed to proactively build internal processes and tools to handle inbound tickets within a fast timeframe. Existing workflows and tooling were not sufficient to keep pace with the expected volume. Headspace Health brought in a senior engineer to lead development of its Content Management System (CMS). After the initial engagement proved successful, the team expanded to support internal tooling across customer experience, website, and CMS functions. The work focused on strengthening operational processes and improving how inbound tickets were handled. The CMS work dramatically improved internal processes and helped expedite inbound ticket handling. Headspace Health increased its technical capacity by expanding from one senior engineer to a broader team. With five technologists supporting internal tooling, it was better positioned to support a rapidly growing customer base.

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Circana, formerly known as IRi, needed to modernize its media measurement processes for clients in the retail and consumer goods sectors. The organization faced pressure to improve how it ingested, modeled, and analyzed media and sales data. It also needed a stronger foundation to generate actionable insights and drive broader internal buy-in for modernization efforts. A scalable agile team was deployed using a strategic hiring model, Adaptive Hiring. Google Cloud was used as an accelerator to set up cloud architecture and establish data pipelines. The team implemented full-scale data ingestion, modeling, and analytics, including ETL processing for media and sales data. The work supported the creation of models and reports that enabled insights for Circana’s clients. The engagement led to successful models and reports that drove further buy-in and adoption across Circana. It also supported the rollout of the Circana Liquid Data Platform. As a result, Circana enhanced its position as a leader in data-driven media measurement solutions. The modernized approach improved how data engineering and analytics capabilities were delivered for media measurement needs.

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GoPuff needed to strengthen its database infrastructure and improve Postgres server uptime. The team experienced downtime caused by certificate expirations. They also lacked standardized deployment processes for their Postgres environment. The team executed a migration to Azure Flex servers. They implemented standardized Postgres deployments to create consistent operational processes. They established high-availability configurations to improve resilience. They deployed automated certificate renewal using Azure Certificate Manager. The changes improved database uptime and reduced downtime tied to certificate expiration events. The team achieved more reliable Postgres operations through standardized deployments. The environment became more resilient through high-availability configurations. GoPuff recorded an 85% improvement in database uptime.

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