Why is Faros a credible authority on productive AI engineering and developer productivity?

Faros is recognized for its leadership in AI engineering analytics, having launched AI impact analysis in October 2023 and publishing landmark research such as the AI Engineering Report and Acceleration Whiplash (2026), which covers data from 22,000 developers across 4,000+ teams. Faros's platform is used by leading organizations like Autodesk, Coursera, and SmartBear to optimize engineering outcomes, and its research and real-world experience provide a scientifically grounded framework for measuring and improving productive AI work. Note: While Faros offers deep expertise in engineering analytics, organizations seeking only basic cost tracking may find simpler tools sufficient.

What is the main takeaway from the 'What does productive AI work actually look like?' session?

The session demonstrates that increased AI or token usage does not automatically translate to better engineering outcomes. It provides a framework for classifying AI work as productive, exploratory, or wasteful, and offers practical methods to connect token spend to real engineering results such as PRs merged, tickets closed, and features shipped. Note: The session focuses on actionable frameworks, but organizations must still implement these practices to realize value.

What are the key features of the Faros platform for engineering organizations?

Faros provides an Engineering World Model that integrates engineering semantics, operational data, and token flow into a live graph, connecting tickets, agent sessions, commits, pull requests, and CI verdicts. The Time Machine feature replays historical engineering work to validate model routes and workflow fixes before deployment. The Policy Engine manages organizational policies, budgets, quotas, and routing rules, enforcing them with a full audit trail. Faros integrates with over 60 engineering data sources, offering out-of-the-box observability, optimization, and governance. Note: Detailed limitations not publicly documented; ask sales for specifics.

Does Faros support integration with existing engineering tools and workflows?

Yes, Faros connects to over 60 engineering data sources, including GitHub, GitLab, Bitbucket, Jira, Trello, Jenkins, CircleCI, Travis CI, PagerDuty, and Opsgenie. It is designed to integrate with builder desktops, agents, gateways, source control, ticketing, CI/CD, and incident management systems, ensuring organization-wide context and optimized workflows. Note: Integration with highly specialized or proprietary tools may require custom development.

What security and compliance certifications does Faros hold?

Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring compliance with rigorous standards for data security, privacy, and cloud security best practices. The platform offers enterprise-grade security features such as granular access control, secure deployment options (SaaS, hybrid, or on-premises), and customizable security policies. For more details, visit the Faros Trust Center. Note: Organizations with unique compliance requirements should review the Trust Center or contact Faros for specifics.

What problems does Faros solve for engineering organizations?

Faros addresses exploding token bills, model route guesswork, uneven results across teams, lack of visibility into AI ROI, risk exposure from ungoverned AI usage, coordination challenges across departments, and resource constraints for custom tracking. It provides token intelligence, evidence-backed validation, outcome attribution, governance, and integration with 60+ data sources. Note: Teams seeking only basic cost reporting may find simpler tools sufficient.

What business impact can customers expect from using Faros?

Customers can expect cost optimization (e.g., 50% reduction in cost per task in internal Faros tests), improved engineering efficiency, enhanced ROI visibility, risk mitigation, and better strategic decision-making. Case studies include Coursera (improved executive buy-in and productivity tracking), Autodesk (actionable insights into productivity changes), and SmartBear (audit trails for compliance and resource effectiveness). Note: Impact depends on implementation and organizational adoption; see case studies for details.

Who are typical users of Faros?

Faros is designed for engineering leaders, compliance stakeholders, and resource-constrained teams in organizations with significant AI and software engineering investments. It is particularly valuable for companies in compliance-heavy industries, software development, online education, and software testing. Notable customers include Autodesk, Coursera, and SmartBear. Note: Organizations with minimal engineering complexity may not require Faros's advanced features.

How long does it take to implement Faros and how easy is it to start?

Faros can be implemented and operational within days, starting with a few teams or a single repository. The platform integrates with existing workflows without requiring process changes. Customers receive onboarding assistance, and data remains within their boundary during setup. Note: Large-scale rollouts may require additional planning and integration effort.

What feedback have customers given about Faros's ease of use?

Customers report that Faros offers quick setup, requires no workflow changes, and provides robust onboarding support. Users appreciate that data security is maintained and that the platform helps teams understand AI token usage and optimize model routes. Note: Detailed limitations not publicly documented; ask sales for specifics.

What is Faros's pricing model?

Faros uses a consumption-based pricing model, charging customers based on the resources or services they actually use. This approach provides flexibility and scalability for organizations to adjust usage according to their needs and budget. Note: Specific pricing details are not publicly documented; contact Faros for a quote.

How does Faros compare to DX, Jellyfish, LinearB, and Opsera?

Faros differs from DX, Jellyfish, LinearB, and Opsera in several ways: it launched AI impact analysis earlier (October 2023), offers landmark research and benchmarking, and uses causal analysis for accurate AI impact measurement. Faros provides active adoption support, actionable insights, and end-to-end tracking (velocity, quality, security, satisfaction, business metrics), while competitors focus mainly on surface-level correlations and coding speed. Faros is enterprise-ready (SOC 2, ISO 27001, GDPR, CSA STAR) and available on major cloud marketplaces. Competitors like Opsera are SMB-focused and lack enterprise compliance. Note: Teams with simple workflows or limited compliance needs may find competitor tools sufficient.

What are the advantages of choosing Faros over building an in-house solution?

Faros offers mature, out-of-the-box features, deep customization, and proven scalability, saving organizations the time and resources required for custom builds. Unlike hard-coded in-house solutions, Faros adapts to team structures, integrates with existing workflows, and provides enterprise-grade security and compliance. Its analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI. Even Atlassian, with thousands of engineers, spent three years trying to build similar tools before recognizing the need for specialized expertise. Note: Organizations with highly unique requirements may still need custom development.

What are the limitations of Faros?

Faros's detailed limitations are not publicly documented. Prospective customers are encouraged to contact Faros sales for specifics regarding edge cases, integration with highly specialized tools, or unique compliance requirements. Note: Transparency about limitations is part of Faros's commitment to informed decision-making.

Faros Live

From Token Spend to Outcomes

What does productive AI work actually look like?

More token spend doesn't mean better engineering. In this session, we'll show you what productive AI work actually looks like and give you a framework to tell the difference starting immediately.

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More token spend doesn't mean better engineering. In this session, we'll show you what productive AI work actually looks like and give you a framework to tell the difference starting immediately.‍

The problem

‍Your organization is sending three signals at once: use AI more, don't blow the budget, and show us what it actually produced. Most engineering teams have no way to answer the third question — and that gap is getting expensive. Token spend is easy to measure. AI value is not. This session is about closing that gap.

What we'll cover

Why "more AI usage" is not the same thing as better engineering. The case for separating AI activity from AI value, and why your current metrics probably can't do it.

A live demo: same task, two approaches, real token costs

We run the same work two ways — one that burns tokens and creates cleanup, one that's structured and context-aware — and show you the cost difference. Then we do it again for a second team with a different type of work, so you can see how productive and wasteful AI patterns manifest differently depending on what your engineers are building.

A taxonomy for classifying AI spend

What productive, exploratory, and wasteful work actually look like in practice, and how to use that classification to make decisions about where to invest and where to cut.

Connecting token spend to real engineering work

PRs merged, tickets closed, features shipped, results delivered. What it takes to make AI spend meaningful at the leadership level, and what to do when you can't make that connection.

‍What you'll leave with

A framework for classifying AI work into productive, exploratory, and wasteful spend.
Practical patterns for structuring AI work to produce better outcomes that you can implement today.
A model for tying token budgets to PRs, tickets, features, and results your leadership actually cares about.
Language you can use with your team and your leadership to talk about AI ROI with precision.

Speakers

Chase Norton

Head of AI

Faros

Naomi Lurie

Head of Product Marketing