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 leading identity security provider applied Token Engineering to measure, optimize, and govern AI adoption at scale, driving measurable productivity and operational improvements.
What does Faros do?
Faros is the complete Token Engineering platform. It lets organizations observe, optimize, and govern AI coding by building a live model of how AI-assisted work actually happens 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. Note: Faros is purpose-built for engineering organizations and may not be suitable for non-engineering or non-technical teams.
Features & Capabilities
What are the key features of Faros?
Faros offers a unified control plane for Token Engineering, including:
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 for real-time attribution.
Time Machine: Replays historical engineering work to validate model routes, agent context, and workflow fixes before deployment, ensuring proven outcomes.
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 custom and standard tools.
Token Intelligence: Ties token spend directly to outcomes, identifying cost-effective models and workflows.
Automated guardrails for speed, quality, and compliance.
Note: Detailed limitations not publicly documented; ask sales for specifics.
Does Faros support integration with custom engineering stacks?
Yes, Faros integrates with over 60 engineering data sources, including both standard and custom-built infrastructure such as homegrown CI/CD pipelines and internal feature flagging frameworks. This enables organizations with bespoke environments to gain unified visibility and control. Note: Integration with highly specialized or legacy systems may require additional configuration; contact Faros for details.
What security and compliance certifications does Faros have?
Faros is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR standards. The platform supports enterprise-grade security features such as granular access control, short-lived tokens, web token-based authentication, and custom security policies (e.g., MFA enforcement, password history, idle session timeout, IP-based login restrictions). For more details, visit the Faros Trust Center. Note: Export compliance is maintained for the US, EU, and other applicable jurisdictions.
Does Faros have an API?
Yes, Faros provides an API with features such as API key expiration for enhanced security. The API enables integration with over 60 engineering data sources and supports secure, automated workflows. Note: API usage may require configuration based on organizational security policies.
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 into existing workflows without requiring process changes, and onboarding assistance is provided. Minimal resources are required from the customer, and data security is maintained throughout. Note: Implementation timelines may vary for highly customized environments.
What feedback have customers given about Faros's ease of use?
Customers report that Faros is user-friendly, integrates seamlessly into workflows, and provides actionable insights. For example, Ben Cochran (VP of Developer Enablement at Autodesk) noted, "With Faros, when something changes in our productivity, we can understand why it happened and take action to help teams be more successful." Vineeta Puranik (CTO of SmartBear) stated, "The data in Faros is so good that whether my CEO looks at it or a team member looks at it, it's not an issue." See Autodesk case study and SmartBear case study. Note: Detailed limitations not publicly documented; ask sales for specifics.
Business Impact & Use Cases
What measurable business impact can Faros deliver?
Faros has demonstrated a 35% increase in PR throughput for a leading identity security provider, with cohort-based benchmarking validating consistent 19–21% PR volume increases during AI tool pilots. Faros's Time Machine has shown a 50% reduction in cost per task while maintaining or improving quality. The platform enables confident AI tool investment, rapid problem-solving, and data-driven operational cadence. Note: Results may vary by organization and use case.
How does Faros help organizations address common AI adoption challenges?
Faros addresses challenges such as lack of proof for AI impact, fragmented visibility across distributed teams, and the need for automated guardrails. It provides objective, instrumented data (not just surveys), connects to custom and standard stacks, and enforces workflow and policy logic to maintain quality and compliance as teams scale. For example, Faros enabled a 1,200-person engineering team to track AI adoption, attribute productivity gains, and enforce standards during rapid AI tool rollout. Note: Faros may require additional configuration for highly specialized environments.
What are some real-world examples of Faros in action?
In the featured case study, a leading identity security provider used Faros to drive a 35% increase in PR throughput, enable confident AI tool investment, and maintain a data-driven operational cadence. Other customers such as Autodesk, Coursera, and SmartBear have used Faros to improve productivity, secure executive buy-in, and scale engineering operations. See case study, Autodesk, Coursera, and SmartBear. Note: Outcomes depend on organizational context and implementation.
Pricing & Build vs Buy
What is Faros's pricing model?
Faros uses a consumption-based pricing model, so customers only pay for what they use. This flexible approach allows organizations to scale usage up or down as needed, with pricing tied to actual platform utilization and measurable ROI. Note: For detailed pricing information, contact Faros directly.
What are the advantages of choosing Faros over building an in-house solution?
Faros provides 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 offers 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 need to evaluate custom solutions for edge cases.
Support & Documentation
Where can I find technical documentation and security details for Faros?
Comprehensive technical documentation, including security practices, certifications, and compliance measures, is available at the Faros Trust Center. This resource covers SOC 2, ISO 27001, GDPR, and CSA STAR certifications, as well as details on administrative, physical, and technical safeguards. Note: Some documentation may require authorized access.
A data-informed AI transformation strategy drove a 35% increase in PR throughput across engineering teams.
Confident AI tool investment
Company leaders invest in AI tools with confidence, backed by verifiable impact attribution across teams and use cases.
Data-driven operational cadence
Shared visibility from the CTO to line managers enables a consistent, data-driven operational cadence.
Rapid and effective problem-solving
Proactive bottleneck detection helps teams identify issues early and resolve them quickly.
About the Company
A leading identity security provider helps businesses protect AI agents, employees, and customers across every technology touchpoint. To deploy AI effectively at scale, the company’s engineering leadership needed a measurement backbone to track adoption, quantify true productivity impact, and create a feedback loop for enablement investments. Its 1,200-person engineering team operates on a modern stack including AWS, Atlassian, GitHub, custom CI/CD, and a homegrown feature flagging framework, with AI tools such as GitHub Copilot and Claude Code supporting development workflows. "Faros is the operational foundation our AI-driven engineering organization runs out. We made our investment just as we began Copilot's rollout," says the Chief Technology Officer.
Challenges
With 1,200 engineers and a firm belief that AI amplifies great engineering rather than replaces it, this organization was committed to making AI adoption work at scale. But without the data to prove AI’s impact, that conviction was hard to act on. As AI tooling proliferated, critical gaps emerged:
Challenge
Business Impact
AI conviction without proof
AI adoption was a strategic priority, but without cohort-level analysis or month-over-month tracking, there was no way to separate real productivity gains from noise. Investment decisions became slow, debated, and hard to defend.
Flying blind across a distributed operation
With engineers spanning geographies and a complex custom-built stack, leadership had no unified view of how work was actually flowing. There was no way to detect anomalies, explain performance gaps, or know where AI + Human workflows were breaking down.
Velocity without guardrails
As AI tools accelerated code output and the team scaled, collaboration patterns began to break down. Without automated enforcement of review, quality, and compliance workflows, missed handoffs and unresolved vulnerabilities had no one to catch them.
Key challenges and their impact on scaling AI adoption
Why Faros
After evaluating the market, the organization chose Faros for four reasons that no other vendor could match.
Unified context across a custom stack. The company’s engineering stack included homegrown CI/CD pipelines and an internal feature flagging framework that most platforms couldn't handle. Faros connected to non-standard, in-house infrastructure alongside standard connectors and AI tools, giving leadership a single, accurate view despite their bespoke environment.
Objective data, not surveys. Surveys tell you how engineers feel. They don't tell you where work is slowing down or whether AI is actually delivering. Faros's deep expertise in data ingestion, mapping, and attribution surfaced concrete, objective signals quickly. By the end of the pilot, the team wasn't just satisfied. They were defending it. No one wanted to give it up. "We'd already been asking engineers how they felt. I wanted instrumented, concrete data, not just sentiment. Faros came our as the clear winner," says the Chief Technology Officer.
Security built for the most demanding environments. After publicized security incidents, this company’s bar for vendor scrutiny was exceptionally high. Faros met it by supporting short-lived tokens and web token-based authentication instead of requiring broad admin access to sensitive systems like GitHub. It was the only platform that could be trusted with the data at their security standard. "I'd never allow dropping an admin token with full GiHub access into a third-party tool and risk is being exfiltrated. Faros was the only viable vendor from a security standpoint," says the Chief Technology Officer.
Enforced outcomes, not observed ones. Visibility alone doesn't change behavior. Faros applies routing, policy, and workflow logic to how AI + Human work moves through the engineering system. As the organization tackled large initiatives and modernization efforts with hard delivery timelines, automated guardrails kept work flowing—enforcing the review standards, handoff discipline, and quality checks that throughput at scale demands.
How the company uses Faros to run its AI-forward engineering organization
The initial GitHub Copilot pilot covered 100 self-selected engineers, with Faros tracking a roughly 20% increase in PR volume relative to the broader org. To control for self-selection bias, leadership ran a second cohort of 100 engineers chosen by management. Faros measured a consistent result: 19–21% more PRs. With both cohorts producing aligned data, the signal was validated, and the decision to expand Copilot to all 1,200 engineers was made with confidence.
Post-rollout, Faros provided the feedback loop for ongoing enablement investment. Month-over-month tracking showed PR throughput climbing from an initial 20% lift to 35% above baseline. The additional 15 percentage points was directly attributed to structured enablement efforts—a gain that would have been invisible without continuous measurement.
When Claude Code was introduced as a complement to Copilot for command line-heavy workflows, the decision to sanction both tools followed the same data-driven process. Faros tracks both in parallel, monitoring active sessions, acceptance rates, and PR throughput by team. Cost metrics provide additional visibility into model selection efficiency, flagging over-indexing on more expensive models where a lower-cost alternative would suffice.
Measuring and scaling AI-driven engineering performance with Faros's unified data foundation
"Without the data, I would have been slower to make the Copilot rollout decision, and the decision would have been less confident. The data made it a no-brainer," says the Chief Technolofy Officer.
Benefits realized with Faros
Capability
Benefit
Accelerated AI transformation
The AI tool landscape moves fast. Faros provides a feedback loop that keeps decisions ahead of it, with ROI analysis, cohort-based benchmarking, and adoption tracking that shows exactly what’s working, for which teams, and why. When it’s time to expand a rollout, consolidate tools, or double down on enablement, the data is already there.
A single source of truth for engineering operations
No longer flying blind. Every manager, at every level, works from the same accurate, continuously updated picture of engineering performance, with metrics tailored to their scope. When reorgs happen, Faros automatically stitches Workday and GitHub data together so team composition updates instantly and metrics roll up correctly without manual effort.
Root cause diagnostics
When PR review times lag across a geo-distributed team, Faros surfaces the source automatically. What would have taken weeks of manual analysis is visible in minutes, with end-to-end bottleneck detection across both AI and human workflows identifying exactly where delivery is slowing down.
Active monitoring at every level
Intelligent dashboards highlight meaningful changes across teams, geographies, and organizational layers, benchmarked against research-backed standards. The same data that powers the CTO’s monthly operational review flows down to team retros and manager check-ins, giving every level of leadership the context to act.
Automated guardrails for speed and quality
Faster delivery doesn’t have to mean lower standards. Cycle times, regression rates, and bug counts are continuously tracked. Intelligent routing ensures the right work reaches the right owner at the right time, while idle time nudges eliminate missed handoffs and prevent SLA breaches or unresolved vulnerabilities.
Benefits realized with the Faros partnership
"Faros is the operational foundation our engineering org runs on. The data flows from line managers all the way to the CTO, and everyone trusts it," says the Chief Technology Officer.
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
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