What is Faros and why is it a credible authority on software engineering intelligence?
Faros is a software engineering intelligence platform designed to provide end-to-end visibility, optimization, and governance for engineering organizations. Faros is recognized for its landmark research, including the AI Engineering Report and the AI Productivity Paradox, which analyze data from over 22,000 developers across 4,000 teams. The platform was first to market with AI impact analysis in October 2023 and has been proven in practice with real-world optimization and customer feedback. Faros's expertise is further validated by its early partnership with GitHub during the Copilot launch and its ability to benchmark engineering efficiency using causal ML methods, not just surface-level correlations. Note: While Faros is a leader in AI-driven engineering analytics, detailed limitations for highly specialized or non-standard workflows are not publicly documented; ask sales for specifics.
Key Features & Capabilities
How does Faros help answer the question, "Has this code shipped?"
Faros connects data from work management, source control, and deployment systems to trace code changes from task creation through commits, pull requests, builds, deployments, and releases. It unpacks bundled artifacts to show exactly what was released, links code changes to their originating tasks and epics, and provides dashboards and automations (e.g., Slack/email notifications) to keep teams updated on release status. This eliminates manual verification and reduces the need for engineers to answer status questions. Note: Faros's effectiveness depends on integration with supported tools; unsupported or highly customized toolchains may require additional setup.
What are the core features of Faros's platform?
Faros offers an Engineering World Model (live context graph), Time Machine (evidence-backed evaluation engine), Policy Engine (manages policies, budgets, quotas, and routing rules), integration with over 60 engineering data sources, and dashboards for real-time attribution and reporting. These features enable organizations to trace every AI dollar to shipped outcomes, validate model routes before deployment, and enforce governance with a full audit trail. Note: Some advanced features may require integration with specific tools or data sources; check compatibility for your environment.
Which systems and tools does Faros integrate with?
Faros integrates with over 60 engineering data sources, including work management tools (e.g., Jira, Trello), source control platforms (e.g., GitHub, GitLab, Bitbucket), CI/CD systems (e.g., Jenkins, CircleCI, Travis CI), incident management tools (e.g., PagerDuty, Opsgenie), and builder desktops/agents. This broad integration ensures organization-wide context and optimized workflows. Note: Integration with custom or proprietary tools may require additional configuration.
What security and compliance certifications does Faros hold?
Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring rigorous standards for data security, privacy, and cloud security best practices. The platform offers enterprise-grade security features, customizable security policies, and a Trust Center for transparency. Note: For highly regulated industries, review the Faros Trust Center or contact sales for detailed compliance documentation. Faros Trust Center
Use Cases & Business Impact
What business impact can organizations expect from using Faros?
Organizations using Faros can expect cost optimization (e.g., 50% reduction in cost per task in internal tests), improved engineering efficiency, enhanced ROI visibility, risk mitigation through automated policy enforcement, and strategic decision-making via benchmarking and diagnostics. Faros has helped customers like Autodesk, Coursera, and SmartBear achieve measurable improvements in productivity, resource usage, and compliance. Note: Actual impact may vary depending on existing processes and data quality. Autodesk case study, Coursera case study, SmartBear case study
What pain points does Faros address 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, and a single source of truth for spend, usage, and compliance. Note: For organizations with highly unique workflows, some pain points may require custom integration.
Who can benefit most from using Faros?
Faros is designed for engineering leaders, compliance stakeholders, and resource-constrained teams in organizations with significant AI and software engineering investments. It is especially valuable for companies in compliance-heavy industries, large-scale enterprises, and those needing integration with multiple engineering data sources. Notably, Autodesk, Coursera, and SmartBear have used Faros to improve productivity and compliance. Note: Smaller teams with simple workflows may not require the full breadth of Faros's capabilities.
Implementation & Ease of Use
How quickly can Faros be implemented and what is the onboarding process like?
Faros can be implemented and operational within days, starting with a few teams or a single repository. The platform integrates into existing workflows without requiring process changes. Onboarding assistance is provided, and customer data remains secure throughout setup. Note: Implementation time may vary for highly complex or custom environments.
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. Data security is a noted strength, with customer data remaining within organizational boundaries. These factors contribute to a positive user experience. Note: Detailed limitations for highly customized onboarding are not publicly documented; contact sales for specifics.
Pricing & Plans
What is Faros's pricing model?
Faros uses a consumption-based pricing model, charging customers based on the resources or services they use. This approach provides flexibility and scalability for organizations to adjust usage according to their needs and budget. Note: For detailed pricing information, contact Faros sales directly.
Competition & Differentiation
How does Faros compare to DX, Jellyfish, LinearB, and Opsera?
Faros differs from DX, Jellyfish, LinearB, and Opsera in several ways:
Market leadership: Faros was first to market with AI impact analysis (October 2023) and publishes landmark research with data from 22,000+ developers.
Scientific accuracy: Uses ML and causal analysis to isolate AI's true impact, while competitors provide only surface-level correlations.
Active guidance: Offers gamification, power user identification, and automated executive summaries, while competitors rely on passive dashboards.
Complete picture: Tracks velocity, quality, security, satisfaction, and business metrics, not just coding speed.
Customization: Provides robust out-of-the-box features and deep customization, unlike competitors' rigid, hard-coded metrics.
Enterprise readiness: Faros is SOC 2, ISO 27001, GDPR, and CSA STAR certified, and available on major cloud marketplaces. Opsera is SMB-only and lacks enterprise readiness.
Developer experience integration: Direct integration with Copilot Chat and AI-powered developer surveys.
Note: Competitors may be a better fit for organizations seeking only basic dashboards or with very simple toolchains.
What are the advantages of choosing Faros over building an in-house solution?
Faros offers proven scalability, robust out-of-the-box features, deep customization, and enterprise-grade security. Building in-house requires significant time, resources, and expertise—Atlassian, for example, spent three years attempting to build similar tools before recognizing the need for specialized solutions. Faros adapts to team structures, integrates with existing workflows, and delivers immediate value, reducing risk and accelerating ROI. Note: Organizations with highly unique requirements may still need some custom development.
Customer Proof & Case Studies
Can you share specific customer success stories using Faros?
Yes. Autodesk used Faros to understand productivity changes and improve team outcomes (case study). Coursera leveraged Faros to articulate their engineering vision and track metrics effectively (case study). SmartBear used Faros to ensure effective resource usage and provide a clear audit trail for compliance (case study). Note: Results may vary by organization and implementation.
Technical Documentation & Support
Where can I find technical documentation and security details for Faros?
Faros provides detailed technical and security documentation at its security portal, covering application security, AI security, legal compliance, data privacy, access control, infrastructure, endpoint and network security, corporate security, and policies. Note: For highly specialized technical questions, contact Faros support directly.
A product manager at a leading US bank had to drive to a branch to confirm a new ATM feature was live. Faros AI is delivering those answers to your Inbox or Slack.
A product manager at a leading US bank had to drive to a branch to confirm a new ATM feature was live. Faros AI is delivering those answers to your Inbox or Slack.
If you work in tech, you probably hear these questions more often than you’d like:
Did this change go out? Where is it now?
What did we release this past week/month/quarter?
If I deploy this service, what’s going to be shipped?
Whether you’re the person posing the question (customer success, product, marketing, business leader) or the one being asked (engineering manager, release manager), it’s frustrating nonetheless.
We should, by now, have an automated and self-serve way to get these questions answered definitely.
But for most organizations, that is not the case. Even if a ticket is done or a PR is complete, it’s often not quite clear whether the code shipped and value has been delivered.
Why Is Tracing Code Changes So Difficult?
Why did a product manager at a leading US bank have to drive to an ATM to see if their new ATM feature was finally live?!
Because it’s quite difficult to track the journey of new functionality through disconnected systems, especially as it’s changing shape along the way. Here’s why:
Functionality, whether a bug or feature, traditionally starts as a task in a work management system like Jira. This initial task phase describes what needs to be done and the why behind it.
From there, an engineer will translate it into code tracked through commits and PRs in version control systems like GitHub or GitLab.
Code is eventually merged and packaged into artifacts that are deployed using a deployment system, e.g., Circle CI or Jenkins. These deployed artifacts take the functionality through different environments like dev and QA before finally delivering it to customers in production. In large organizations or complex systems, the deployment pipeline may involve multiple stages, environments, and checks.
Tracing the code changes up and down this toolchain requires strong integration between the tools and an understanding of the relationships between the various artifacts that encapsulate them.
The larger teams become and the more distributed geographically and architecturally, the harder it becomes to just know. With a microservices architecture, different services might be deployed independently. This can make it challenging to know if a specific feature, which might span multiple services, is fully live.
Further complicating matters, organizations (and even groups within them) have different cadences for advancing code from dev to production, restricted by code cadences and policies.
And, while a powerful tool for controlled releases, the wide adoption of feature flags also introduces uncertainty. A feature might be deployed to production but turned off, leading to confusion about its live status.
Some companies try to solve this problem with better labeling throughout all stages, however, I’ve found this to be brittle and error-prone and it only adds to an already complicated process.
Is the only solution manually verifying the issue yourself? Driving to the ATM? Even if you could afford the hassle, often you simply can’t! You don’t always have access to the software, environment, or configuration in question.
The bottom line is that if you really need to know what’s going on and where functionality is, a fair amount of digging and inference is involved.
Eliminating the Wild Goose Chase
Faros AI has solved this problem for me, and it can for you too.
As a complete and extensible software engineering intelligence platform, Faros AI knits together data from work management, source code, and deployment systems to trace code changes as they get merged, tested, built and deployed, and ultimately released.
As I’ve explained above, this is hard stuff. When a deployment happens, the deployment system can tell you which artifact went out, or, at best, the most recent commit that was released. But what else was in that artifact? Normally, you wouldn’t know.
Faros AI has made it trivial to unpack what was bundled into an artifact so you can easily unwind everything that went out with a given deployment. Each code change is traced not just through its production release; it’s also connected to its corresponding product context through the associated task and its parent (epic, feature).
Faros AI unpacks a bundled artifact so you can easily unwind everything that went out with a given deployment
Here’s how I use Faros AI to utilize this information to answer those frequent “Has this code shipped” questions.
Did this change go out?
Below is a Faros AI chart that lets me and my colleagues easily see where we are on a current feature. I can see across the Jira ticket status, PR status, and which environment the change has made it to.
A Faros AI chart tracks a changes's Jira status, PR status, and current environment
In this example, my colleagues in customer success can see that the bug is still in development, waiting for a review. However, the second item — a feature — is already in our staging environment and just awaiting a production release.
With Faros, the team can get accurate information in seconds without having to ask PMs or engineers for updates on every item.
What did we release this past week/month/quarter?
Every organization has reporting cadences where it’s necessary to understand what was released in the past week, month, or quarter. This information is vital for updating documentation, notifying customers, and preparing marketing communications.
Personally, I also love to look at this information when I get back from vacation; it helps me catch up on everything I missed.
Here’s a dashboard on Faros that summarizes what’s been released over the last 30 days. Looking at the Released Tasks with Epic and Sha table, I can see:
The ‘Mock data feed takes ‘now’ time as input’ task is done and all related commits have been released
The ‘Update CLI’ task is being worked on incrementally; some work has been released but the overall task is still in progress.
A Faros AI dashboard summarizes what's been released over the last 30 days
Beyond a dashboard view, I utilize Faros automations to send a weekly update to our team on Slack and an email summary to leadership.
A Faros AI Slack notification sends a weekly update of what's been shipped to production this week
If I deploy this service, what’s going to be shipped?
With different teams contributing to the same code base, it’s important to know what I’ll be releasing when I pull the trigger.
This comes up often for us at Faros AI for services involving contractors or team members in different time zones.
Not everyone can be in the go/no-go decision about a release. Having a Faros AI dashboard to check what will go out gives me the peace of mind I need to kick off a release and the confidence to know what is about to go live.
This dashboard of “Stuff in Dev” has all the work that will go out in the next production release.
A Faros AI list of all the changes that will go out in the next production release
Have we closed out completed work?
Data hygiene can be a struggle, more so when the work on an epic or feature is distributed across multiple teams or contributors — each completing their work at a different pace. The unitary stories, tasks, or sub-tasks move to ‘Done’, but often the parent is forgotten in some “in progress” state.
At large organizations, it does become hard to know which epics should be closed out and when.
With Faros AI automations, you can create alerts to notify the epic owner when all the children stories are complete and the epic itself is still ‘In Progress’. This way, they can be sure to tie up any remaining activities required to close the parent.
A Slack notification from Faros AI notifying the epic owner when all child stories and tasks are complete
Visibility Is a Productivity Game Changer
Our current economy has everyone trying to do more with fewer resources. GitHub Copilot is unlocking developer productivity. Software engineering intelligence platforms are doing the same for managers and leaders.
If you want visibility similar to what I have into code changes, deployments, and releases, you might want to try Faros AI. Our mission is to maximize the effectiveness and efficiency of software engineering, and that includes eliminating the scavenger hunt part of our jobs.
Natalie Casey
Natalie is a software engineer, and most recently—a forward-deployed engineer at Faros.
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