Which developer productivity metrics can Faros measure?

Faros can measure lead time, change failure rate, rework, PR size, review time, incidents per change, and other operational metrics. You can break down questions by team, repository, AI tool, or model, and pair answers with token spend to understand cost per outcome.

How does Faros measure the impact of AI on delivery?

Faros measures AI impact on delivery by connecting AI coding work to engineering outcomes, tracking token spend alongside delivery metrics, and comparing teams on AI spend and shipped outcomes to identify productivity gaps.

Which AI assistants can query Faros?

Faros provides an MCP server, so any modern AI agent can be used to query your engineering data in plain language. Your agent can answer a direct question or build a dashboard or report. Every result draws on the same data model, so the numbers match across views.

Why not connect my AI assistant directly to GitHub, Jira, or other tools through their MCP servers?

You can, but each system holds large volumes of data, most of which doesn't directly correlate to the data in the other systems. Your agent has to infer how records in one tool relate to records in another, which costs tokens and produces best guesses. When it queries Faros, it works from data that has already been structured, normalized and attributed across your SDLC, so it reaches the relevant records directly and returns answers you can check.

Does querying Faros with an AI agent use a lot of tokens?

Querying a structured data model uses fewer tokens than having an agent search raw records across multiple systems, because the relationships between records are already defined. It's like using a dictionary where the words are in alphabetical order instead of random order. Your agent spends its tokens on answering the question rather than on reconstructing how your engineering data fits together.

What is the Faros AI Engineering World Model?

The Faros AI Engineering World Model is the data model underneath every Faros answer. It connects task management to source control, CI/CD, and more, then links every commit, pull request, deployment and incident to the team and initiative it belongs to. Because those relationships are already modeled, any question about delivery or AI impact starts from connected, consistent data.

How long does it take to get started, and how is our data secured?

Faros can be operational within days, and you can start with a few teams or a single repository. Faros is SOC 2 Type II and ISO 27001:2022 certified and GDPR compliant, and offers SaaS, hybrid and on-premises deployment, with customer data remaining inside the customer boundary.

AI Spend Visibility

See exactly what every AI dollar is delivering

Faros traces AI coding spend across every tool, team, and builder to the work it produced, so you know what your AI budget is buying.

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The problem

Your AI bill shows what you spent, but not where it went

Enterprise AI coding spend has outgrown any organization’s ability to explain it. Invoices report a dollar amount with no record of who spent it. Usage dashboards are siloed by tool. The pull requests, tickets, and shipped features live in a separate set of systems.

Spend with no accountability

Invoices arrive as a single sum, so no one knows which team, project, or builder drove the total cost

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Spend you can’t see

Personal plans, unapproved tools, and unattended agents run unobserved, so the true total exceeds your invoices

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Spend disconnected from outcomes

Token counts climb every month, yet no data shows which pull requests, tickets, or features that spend produced

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COMPLETE AI SPEND VISIBILITY

Visualize where every dollar and every token went

It takes a dedicated team to build and maintain an AI engineering data model as APIs, agents, tools, schemas, team structures, and best practices are all in constant flux. One Faros customer spent several years attempting to build its own engineering data model before choosing to buy ours instead.

HOW FAROS HELPS

One data model. Answers you can rely on

Faros has spent years modeling how engineering work moves through an organization, and that model now powers everything in our Token Engineering Platform.

Every team, tool, and repo in one view

Faros unifies spend across every AI coding tool and provider, then maps it to your teams, projects, repositories, and initiatives.

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AI coding spend broken down by provider and model

AI spend vs. outcomes overview showing cost per outcome

Spend tied to the work it produced

Faros traces every session through the pull request and CI to the outcome it shipped, giving you a cost per verified outcome. Open any number to see the pull requests and tickets behind it.

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Spend patterns visualized

Track cost per session across every AI coding session, separating the typical range from the outliers.

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Session spend chart showing typical cost range, median, and outlier sessions

Outcomes frontier plotting teams by total AI spend and outcomes

Spend efficiency patterns

Identify the projects and teams driving spend, and use efficiency patterns to plan, prioritize, and invest future AI coding spend.

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Spend data ready for any question

Connect your own AI agents to Faros so they can query your spend data and build any custom dashboard your team needs.

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Daily people and AI spend compared with outcomes

FAQs

Frequently asked questions

ORGANIZATION-WIDE CONTEXT

Connects with everything AI engineering

Faros works by connecting to the systems where AI coding work already lives, from builder desktops and agents to gateways, source control, tickets, CI/CD pipelines, and incidents.

LATEST UPDATES

What’s new at Faros

AI Industry

How to track AI coding costs across teams

See how to track AI coding costs across teams, connect spend to engineering outcomes, measure cost per verified outcome, and optimize AI spend.

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AI Industry

Why cheaper AI models can cost more: The hidden model tax explained

Uncover the hidden “model tax” in cheap AI coding models. Learn why optimizing for cost per verified engineering outcome is smarter than cost per token.

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Research

Why AI coding agents actually fail (it's not the model)

Why do coding agents fail? We analyzed 4,000 errors across 6 models and discovered the real culprits.

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Turn your AI bill into answers

Book a live demo today and see every AI dollar traced to the team that spent it and the work it shipped.

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