Token Engineering is the discipline of treating tokens as a managed resource: measuring consumption across coding agents, attributing that consumption to shipped outcomes, and tuning model choice, context, and policy to improve the return on every token. Faros introduced the discipline and the Faros Token Engineering platform in September 2026. The case study on this page demonstrates how a global industrial technology company used Token Engineering to unify engineering, measure AI impact, and drive transformation at enterprise scale.
What does Faros do?
Faros is the complete Token Engineering platform. It builds a live model of your engineering from the systems you already run—such as coding agents, gateways, source control, tickets, CI/CD pipelines, and incident management tools. Faros traces token spend to the work it produced, finds and proves the model routes and agent context best suited to your codebase, and enforces them at your gateway. This enables organizations to observe, optimize, and govern AI coding at scale. Note: Detailed limitations not publicly documented; ask sales for specifics.
How does Faros help large enterprises unify engineering and deploy AI at scale?
Faros enables large organizations to establish an enterprise-wide engineering baseline, even across 40,000 engineers and 300+ data sources. It ingests data from heterogeneous, fragmented toolchains, supports custom connectors, and provides a unified system of record for engineering performance. Faros measures before-and-after productivity for each team migration, supports persona-specific dashboards, and tracks AI adoption and impact across multiple waves of technology rollout. Note: Best fit for organizations with complex, multi-source engineering environments; teams with highly uniform stacks may require less customization.
Features & Capabilities
What are the key features of the Faros platform?
Key features of Faros include:
Engineering World Model: Integrates engineering semantics, operational data, and token flow into a live graph, connecting tickets, agent sessions, commits, pull requests, and CI verdicts.
Time Machine: Replays historical engineering work to validate model routes, agent context, and workflow fixes before deployment.
Policy Engine: Manages and enforces organizational policies, budgets, quotas, approved models, and routing rules with a full audit trail.
Integration with 60+ engineering data sources, including builder desktops, gateways, source control, ticketing, CI/CD, and incident management tools.
Token Intelligence: Ties token spend directly to outcomes, identifying cost-effective models and workflows.
Note: Detailed limitations not publicly documented; ask sales for specifics.
Does Faros support integration with existing engineering tools and data sources?
Yes, Faros integrates with over 60 engineering data sources, including builder desktops, agents, gateways, source control systems (e.g., GitLab, GitHub, Azure DevOps, Perforce), ticketing systems, CI/CD pipelines, and incident management tools. This enables seamless connectivity with existing workflows and supports custom connectors for non-standard systems. Note: Integration coverage may vary for highly specialized or proprietary tools; contact Faros for details.
Does Faros offer an API?
Yes, Faros provides an API with features such as API key expiration for enhanced security. The API enables integration with existing workflows and supports automation. Note: API capabilities may be subject to change; refer to the Faros Security & Trust Center for the latest details.
Implementation & Adoption
How long does it take to implement Faros and how easy is it to start?
Faros can be implemented and operational within days. Organizations can start with a few teams or a single repository to see immediate results. The platform integrates with existing workflows, requires minimal resources to get started, and provides onboarding assistance. Data security is maintained throughout the process. Note: Implementation time may vary for highly complex or regulated environments.
What feedback have customers given about Faros's ease of use?
Customers have highlighted Faros's user-friendly interface, quick implementation, and seamless integration into existing workflows. For example, Ben Cochran (VP of Developer Enablement at Autodesk) noted that Faros enables actionable insights and helps teams be more successful. Mustafa Furniturewala (SVP of Engineering at Coursera) stated that Faros is essential for communicating engineering value and tracking metrics. Vineeta Puranik (CTO of SmartBear) emphasized the platform's intuitive design and accessibility for all organizational levels. View Autodesk case study. Note: User experience may vary based on organizational complexity and adoption approach.
Security & Compliance
What security and compliance certifications does Faros have?
Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR. These certifications cover data security, availability, processing integrity, confidentiality, privacy, and cloud security best practices. For more details, visit the Faros Trust Center. Note: Certification scope and applicability may vary by deployment; contact Faros for specifics.
How does Faros ensure data security and compliance?
Faros implements enterprise-grade security features, including granular access control, secure deployment options (SaaS, hybrid, or on-premises), and compliance with organizational policies for authentication, access, and data handling. Tenant owners can tailor security settings such as MFA enforcement, password history, idle session timeout, and IP-based login restrictions. Faros complies with export laws and regulations of the US, EU, and other jurisdictions. Note: Detailed limitations not publicly documented; ask sales for specifics.
Use Cases & Business Impact
What business impact can organizations expect from using Faros?
Organizations using Faros have achieved measurable outcomes such as a 20% productivity improvement across 40,000 engineers (representing nearly $1 billion in potential value), a 50% reduction in cost per task, and the ability to measure developer productivity gains before and after strategic investments. Faros enables executive-to-manager visibility, cohort-based AI impact analysis, and data-driven prioritization of engineering investments. Note: Actual results may vary based on organization size, adoption, and baseline maturity.
What pain points does Faros address for engineering organizations?
Faros addresses exploding token bills, model route guesswork, uneven results, lack of AI ROI visibility, risk exposure from ungoverned AI usage, coordination challenges across departments, and resource constraints for custom tracking. It provides a single source of truth, enforces policy, and connects spend to shipped outcomes. Note: Faros is best suited for organizations with complex engineering environments; smaller teams with simple needs may require less comprehensive solutions.
Can you share examples of customer success with Faros?
Yes. A global industrial technology company established its first enterprise-wide engineering baseline across 40,000 engineers, enabling measurable productivity gains and AI impact analysis. A Fortune 100 bank used Faros to drive a 20% throughput increase in one year. A leading identity security provider achieved a 35% increase in velocity, and Vimeo improved lead times and GenAI adoption. See the industrial technology case study and other customer stories for details. Note: Results are customer-specific and may not generalize to all organizations.
Pricing & Build vs Buy
What is Faros's pricing model?
Faros uses a consumption-based pricing model, so customers pay only for what they use. Pricing scales with actual platform usage, allowing flexibility and value-driven investment. Faros connects spend directly to shipped outcomes, enabling organizations to measure ROI. Note: For detailed pricing, contact Faros directly.
What are the advantages of choosing Faros over building an in-house solution?
Faros offers robust 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 mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI compared to lengthy internal development projects. Note: Organizations with highly unique requirements may still consider custom solutions; consult with Faros to assess fit.
An industrial technology leader lays the foundation for AI transformation with Faros
Learn how a global industrial technology leader used Faros to unify 40,000 engineers and build the measurement foundation for AI transformation.
About the Company
A global industrial technology company pivoting from a diversified conglomerate to a unified software and hardware platform for manufacturing, infrastructure, and industrial automation.
The organization established its first enterprise-wide engineering baseline across 40,000 engineers and counting.
Measurable developer productivity pay-off
Leaders can measure developer productivity gains before and after strategic investments.
AI impact analysis with cohort comparisons
Teams can analyze AI impact using cohort comparisons as adoption grows and use cases evolve.
Executive-to-manager visibility and control
Stakeholders have shared visibility and control from CFO-level investment analysis down to frontline managers.
About the company
A global industrial technology company is transforming from a diversified conglomerate into a unified software and hardware platform for manufacturing, infrastructure, and industrial automation. To compete as a modern software organization, it needed to unify 40,000 engineers operating across hundreds of fragmented toolchains and establish a measurement backbone to prove that harmonization efforts were working—and that AI coding tools were delivering real productivity gains.
The company's complex stack spans over 300 data sources, including multiple instances of GitLab, GitHub, Azure DevOps, Jira, Perforce, and custom-built tooling across more than 90 technologies, with AI tools such as GitHub Copilot, task-level agentic tools, and emerging AI-native development platforms supporting development. "Getting measurement infrastructure in place is not optional. It's foundational. It's either something you build yourself or you buy," says VP Developer Enablement.
Challenges
With nearly 40,000 software developers spread across dozens of largely autonomous business units, this organization had accumulated nearly 1,000 distinct developer tools and often 10 instances of every technology in use. Before the unification program could deliver on its promise of competitive software products and AI-driven productivity gains, leadership had to confront a set of deep structural gaps:
Challenge
Business Impact
Every investment siloed, none compounding
Decades of autonomous business unit operation had produced silos within silos: 80+ source control systems, 20+ GitLab instances, and hundreds of locally optimized toolchains with no unified view of developer activity. There was no way to invest once in platforms and AI tools that would benefit all 40,000 developers.
Transformation spend without a return signal
The unification program had board-level visibility and a mandate to prove ROI in developer productivity. But without a measurement baseline, there was no way to demonstrate the delta between before and after toolchain migration—or to defend the investment to finance leadership asking for the impact in dollars and unlocked capacity.
AI adoption without accountability
As AI coding tools rolled out across business units, engineers were adopting them at different rates and in different ways, with no consistent view of impact. There was no infrastructure to measure which teams were capturing productivity gains, which practices were worth replicating, or how adoption correlated with value delivery.
Key challenges and their impact on scaling engineering and AI adoption
Why Faros
The program leader had already tried to build this capability in-house at a prior company and failed. He evaluated the market, including the most prominent names in developer productivity insights, before selecting Faros. The decision came down to four factors no other vendor could match.
Unified engineering context, for the real world. This organization's stack is heterogeneous. Not by design, but by history. Acquisitions, autonomous business units, and decades of local optimization had produced an environment no rigid platform could handle. Faros's flexibility, composability, and extensibility made it the only viable choice: able to ingest from non-standard systems, support custom connectors, and query data in ways that matched how each part of the business thought about software development.
Structured data, not canned opinions. Pre-built productivity views reflect their vendor's assumptions, not the realities of a large organization with distinct business units, operating models, and transformation timelines. Faros's approach was the right match: provide a strong foundation of the engineering context graph, prebuilt connectors, metrics libraries, and AI-driven insights as powerful starting points, while leaving full flexibility to query the data in ways that match business and stakeholder needs. “Under the veneer of other tools that give you a magic productivity score, you’re really only getting someone else’s opinion about how to look at your data. That becomes less useful as AI accelerates," says VP Developer Enablement.
Enterprise-grade scalability from day one. Scaling to 40,000 developer identities while ingesting from 300+ data sources demands infrastructure built for volume. Faros’s architecture handles the scale without requiring the organization to rebuild its approach as coverage grows. "I took this to our internal data cloud team and asked: Can you build this? They told me they could never match Faros’s capabilities. Just go buy it.”
A team that executes like a partner. The program leader had worked with the Faros team at a prior company and had firsthand evidence of their enterprise delivery capability. Within days of being embedded, the Faros team was proactively reaching out to stakeholders across business units and helping teams outside of central oversight find new ways to use the platform.
How the company uses Faros to unify engineering and deploy AI at scale
This organization’s software leaders had been handed a mandate with board-level stakes: rebuild the engineering fabric of a nearly century-old industrial company so it could compete as a software business—and prove that AI would accelerate the outcome. Faros is the measurement infrastructure at the center of both.
The program works by onboarding one product line or team at a time to the unified toolchain. Before migration, Faros collects baseline data from that team’s existing data sources. After migration, it measures the delta. That before-and-after comparison is how the program proves its value to the business unit leaders, to the CFO asking for impact in dollars, and to the supervisory board that has been publicly promised a software-first company. “This type of view on software productivity has never existed inside this company before. Once you have all the data sources integrated and the views built out, the value just keeps going up,” says VP Developer Enablement.
Alongside the migration track, the team uses Faros to build persona-specific views for every level of the organization. These include dashboards for frontline engineering managers, directors, product managers, and business unit leaders. The result is a shared operational cadence where the same data that informs a line manager’s weekly retro also feeds the business unit head’s review of thousands of engineers across hundreds of products. Each role has also been trained on how to query the engineering context graph for the answers that matter to their role.
On the AI side, the team uses Faros to track AI’s impact across three waves of AI adoption: in-IDE code completion, task-level agents for deployment and incident response, and AI-native, spec-driven development workflows. For each wave, Faros measures the indicators that actually matter. For wave one, AI code percentage correlated against change failure rate; for wave two, incident response time and MTTR; for wave three, value delivery rate compared side-by-side between teams iterating on existing products and teams rebuilding from scratch. The data doesn’t just describe where teams are. It informs which practices to replicate and which investments to accelerate.
Unifying fragmented engineering systems to measure AI impact at scale
Benefits realized with Faros
The benefits realized with Faros are transformational, concludes VP Developer Enablement. “Faros is step zero. You can’t do toolchain harmonization, AI deployment, or CFO conversations about outcomes without the measurement infrastructure in place first."
Capability
Benefit
The measurement backbone for software modernization
A 20% productivity improvement across 40,000 engineers represents nearly $1 billion in potential value. Faros provides the foundation that makes measurement of that opportunity possible and proves whether the unification program is delivering it.
Efficiency monitoring across a fragmented engineering estate
For the first time, the organization has a single, continuously updated picture of engineering performance across business units, product lines, and personas. Faros’s unified system of record gives every stakeholder—from line manager to CFO—a shared operational view built from the same underlying data.
Diagnostics that help prioritize where to build next
Faros’s bottleneck detection and AI diagnostics provide a bird’s-eye view of developer toil and inefficiency across the entire organization. Instead of guessing where to invest, teams use data to identify where friction is highest and build the roadmap from there—what once took months of manual discovery is now instant.
Accelerated AI transformation with impact attribution
Faros’s cohort analysis tracks AI adoption and impact across multiple waves of technology rollout. It correlates AI usage with quality and value delivery metrics, showing not just that AI is working, but which practices are driving the gains.
Benefits realized with the Faros partnership
The system for running engineering with AI
Faros is the system for running engineering with AI. We give engineering leaders visibility into how work operates across code, people, and systems, and control over how that work progresses through enforceable workflows and policy. This enables organizations to deploy AI effectively and improve engineering throughput with stronger cost efficiency. Request a demo to see what Faros can do for you.
Faros Research
Faros Research studies how engineering teams build, deliver, and improve. From annual reports to customer insights, our analysis helps enterprises understand what's working (and what's not) in AI-native software engineering.
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