Claritev Adopts CodeTogether’s AITrax to Improve Measurement and Accountability in AI-Assisted Software Development
Claritev Corporation, a healthcare technology, data, and insights company focused on improving affordability, transparency, and quality across the healthcare system, has announced plans to use CodeTogether’s AITrax platform to gain greater visibility into the use, cost, performance, and impact of artificial intelligence-assisted software development across its engineering organization.
The adoption comes as enterprises increasingly integrate AI-powered coding assistants and agentic development tools into their technology operations. While these tools are becoming more common, many organizations continue to face challenges in measuring whether AI-assisted development is delivering meaningful business value.
Technology leaders are increasingly seeking objective data that can help them understand how AI tools are being used, how much they cost, whether they improve developer productivity, and how they affect software quality.
Through its adoption of AITrax, Claritev aims to bring greater measurement and accountability to its AI-assisted development strategy.
AI Adoption Creates New Measurement Challenges
Artificial intelligence is rapidly changing the way software is designed, developed, tested, and maintained.
Developers now have access to AI tools that can generate code, suggest solutions, analyze errors, write tests, automate repetitive tasks, and support complex software development workflows.
More advanced agentic systems can perform multiple development activities with a higher degree of autonomy, potentially allowing teams to complete certain tasks faster and with fewer manual interventions.
However, the rapid adoption of these technologies has also created new challenges for technology leaders.
Companies need to understand whether AI-assisted development is producing measurable improvements or simply increasing technology spending. They also need to determine whether the use of AI tools is improving productivity without creating new risks related to code quality, security, governance, or software maintenance.
The lack of reliable measurement can make it difficult for organizations to assess the actual return on investment associated with AI-assisted development.
Claritev’s adoption of AITrax is intended to address this challenge by providing greater visibility into how AI tools are being used throughout the engineering organization.
Building on an Existing Engineering Intelligence Partnership
Claritev’s relationship with CodeTogether began in 2022, when the company deployed CodeTogether’s engineering intelligence platform across more than 1,000 technology associates.
Based on Claritev’s internal measurement of that deployment, the implementation was associated with a 59% increase in issue throughput and a 48% increase in active development time.
The results provided Claritev with a framework for evaluating technology operations using objective data rather than relying solely on employee surveys or subjective assessments.
The companies are now applying a similar measurement-focused approach to AI-assisted and agentic software development.
The goal is to help Claritev understand how developers and AI agents are interacting with software development environments and how those interactions may contribute to productivity, efficiency, quality, and cost.
AITrax Designed to Provide AI Development Intelligence
AITrax is the AI-specific measurement layer of CodeTogether’s broader engineering intelligence platform.
The technology is designed to capture developer- and agent-level AI usage across development environments, including integrated development environments and command-line workflows.
The platform transforms this activity into data and insights intended for technology leaders and executives.
By providing visibility into AI usage, AITrax is designed to help organizations understand how different teams are using AI-assisted development tools and identify patterns across workflows.
This information may help leaders determine where AI is being adopted successfully, where additional training may be needed, and where governance policies may need to be strengthened.
The platform also aims to help organizations assess the relationship between AI usage and business outcomes.
Rather than simply measuring the number of AI interactions or the volume of code generated, the system is designed to provide insights into the broader value of AI-assisted development.
Monitoring Cost and AI Spending
One of the key challenges associated with AI-assisted development is cost management.
AI tools may involve different pricing models, including usage-based costs tied to token consumption, agent sessions, or other forms of computational activity.
As organizations expand their use of AI across engineering teams, costs can grow quickly.
Technology leaders may therefore need greater visibility into how AI spending is distributed across teams, tools, projects, and workflows.
AITrax is designed to provide visibility into token usage, agent sessions, abandoned sessions, and other aspects of AI-assisted development activity.
This information can help organizations understand the actual costs associated with different work models.
For example, technology leaders may be able to compare the cost and value of agentic development with AI-assisted development and traditional software development processes.
The goal is to help organizations identify where AI is producing meaningful returns and where spending may not be generating sufficient value.
Evaluating Productivity and Return on Investment
Another important component of the platform is the ability to evaluate AI-assisted development based on productivity and return on investment.
Organizations are investing heavily in AI tools, but the value generated by those tools can vary significantly.
Some teams may use AI to automate repetitive tasks, while others may rely on AI for more complex development activities.
Different tools and workflows may also produce different results.
AITrax is designed to help technology leaders compare the value generated across different work models.
The platform’s approach includes evaluating value per dollar across agentic, AI-assisted, and traditional development.
This type of comparison may help organizations determine which approaches are most effective for specific tasks or teams.
For Claritev, this information could support decisions about how to scale AI tools across its engineering organization.
Supporting AI Governance and Responsible Adoption
As AI becomes more integrated into software development, organizations are also facing new governance requirements.
AI-generated code may need to undergo appropriate review and testing. Developers may require training on how to use AI tools responsibly. Organizations may also need policies governing data security, intellectual property, code ownership, and compliance.
The ability to monitor AI usage can provide technology leaders with greater insight into how tools are being used and where risks may emerge.
AITrax is designed to help organizations identify AI-readiness gaps and provide recommendations for improvement.
The platform is intended to function as more than a reporting tool. It also provides coaching and recommendations designed to help organizations improve AI adoption.
This could include identifying teams that may need additional training, highlighting inefficient workflows, or helping leaders establish more effective governance processes.
Measuring Quality as AI-Generated Code Increases
Software quality is another important consideration as AI-assisted development becomes more common.
While AI tools can help developers generate code more quickly, organizations must also ensure that the resulting software meets quality, security, and performance standards.
Technology leaders may need to evaluate how AI-assisted development affects code reviews, issue resolution, testing, and software maintenance.
AITrax is designed to provide insight into AI usage and development workflows, helping organizations better understand how AI-generated work moves through the software development process.
This visibility may help companies identify where additional review or quality controls are needed.
For Claritev, the ability to evaluate AI-assisted development alongside existing engineering intelligence data could provide a more complete view of how technology teams are operating.
Claritev’s Focus on Responsible AI Adoption
Michael Kim, Executive Vice President and Chief Digital Officer at Claritev, said AI is an important part of the company’s technology strategy.
However, the company is also focused on adopting AI in a responsible and measurable manner.
Claritev’s healthcare technology and data operations support clients and stakeholders across the healthcare system. As a result, the company must consider the impact of technology decisions on operational performance, data management, security, and quality.
Kim said CodeTogether’s platform gives Claritev greater visibility into how AI-assisted development is being used and where it is creating value.
The company also intends to use this information to help scale AI tools in a disciplined way.
This approach reflects a broader trend among large enterprises. Organizations are increasingly moving beyond experimentation with AI and focusing on how the technology can be integrated into long-term operating models.
Moving From AI Adoption to AI Accountability
Many organizations have already adopted AI-assisted development tools. However, the next stage of enterprise AI adoption may focus increasingly on accountability.
Technology leaders need to answer several important questions.
How frequently are AI tools being used? Which teams are using them most effectively? How much does AI-assisted development cost? Are developers receiving adequate training? Is AI improving productivity? Does AI-generated code meet quality standards? Are AI investments producing measurable returns?
AITrax is designed to help address these questions through continuous telemetry and objective measurement.
Unlike survey-based approaches, which rely on self-reported information, the platform is designed to capture usage data directly from development environments.
This can provide organizations with a more consistent view of how AI-assisted development is actually taking place.
The data may also help leaders identify differences between perceived and actual AI adoption.
Supporting the Future of Enterprise Software Development
The adoption of AITrax positions Claritev to continue developing its approach to AI-assisted software development as the technology evolves.
As AI tools become more capable, organizations will likely need new approaches to measuring productivity and value.
Traditional engineering metrics may not fully capture the contribution of AI agents or AI-assisted workflows.
A developer working with an AI agent, for example, may produce results through a different process than a developer working entirely manually.
Organizations may therefore need new measurement frameworks that account for collaboration between humans and AI systems.
AITrax is designed to support this transition by providing visibility into both developer and agent-level activity.
CodeTogether’s Perspective on Enterprise AI
Tim Webb, Co-CEO of CodeTogether, said many enterprises are investing heavily in AI-assisted development without having reliable methods to determine whether those investments are delivering meaningful results.
According to Webb, AITrax is designed to address this gap by providing an accountability and coaching layer focused on cost, risk, and return.
The company’s continued work with Claritev reflects its broader strategy of helping organizations measure and manage the impact of AI across software engineering.
Claritev’s adoption of CodeTogether’s AITrax highlights the growing importance of measurement as artificial intelligence becomes increasingly integrated into enterprise software development.
AI-assisted coding tools and agentic development systems have the potential to transform how software is built. However, organizations must also understand the costs, risks, productivity effects, and measurable benefits associated with these technologies.
By expanding its use of CodeTogether’s engineering intelligence capabilities, Claritev is seeking to establish greater visibility into its AI-assisted development environment.
The company’s strategy focuses on understanding how AI tools are being used, measuring their impact, monitoring costs, identifying opportunities for improvement, and supporting responsible adoption.
The initiative also builds on Claritev’s previous experience using engineering intelligence data to improve technology operations.
As AI continues to become a larger part of enterprise technology strategies, the ability to measure actual performance may become increasingly important.
For Claritev, the adoption of AITrax represents an effort to move beyond simply deploying AI tools and toward building a more disciplined approach to AI governance, productivity, and accountability.
As companies across industries continue to invest in AI-assisted development, technology leaders will increasingly be expected to demonstrate whether these tools are delivering measurable value.
Claritev’s partnership with CodeTogether reflects this shift, emphasizing the importance of objective data and continuous measurement in determining whether AI is truly improving the way organizations develop software.
Source link: https://www.claritev.com/
