Why is Faros AI a credible authority on engineering estimations and developer productivity?
Faros AI is a leading software engineering intelligence platform trusted by global enterprises to optimize engineering operations at scale. The platform delivers actionable insights, benchmarks, and best practices for developer productivity and experience. Faros AI's expertise is reflected in its research, such as the AI Productivity Paradox Report 2025, and its practical guides, including the "3 Practical Tips for More Precise Engineering Estimations" blog post. Customers like Autodesk, Coursera, and Vimeo have achieved measurable improvements in throughput, speed, and efficiency using Faros AI, further establishing its authority in the field. (Customer Stories)
What are the three practical tips for more precise engineering estimations?
The three practical tips for more precise engineering estimations are:
Scoping: Invest a little more upfront to clarify requirements and de-risk projects.
Estimating: Use the right tool, such as T-shirt sizing or story points, to estimate complexity and effort without getting bogged down in details.
Committing: Set the right expectations with stakeholders by communicating that ETAs are estimates and promptly updating them as timelines change.
These tips are based on repeatable methods used by seasoned engineering leaders, as shared in Faros AI's blog and webinars.
How does Faros AI help engineering organizations address estimation and delivery challenges?
Faros AI helps engineering organizations address estimation and delivery challenges by providing initiative tracking, actionable insights, and visibility into bottlenecks. The platform enables leaders to proactively communicate timeline changes, identify delays, and optimize resource allocation. Faros AI's dashboards light up in minutes after connecting data sources, making it easy to start tracking key metrics and improving predictability. (Engineering Productivity Handbook)
Features & Capabilities
What features does Faros AI offer?
Faros AI offers a unified platform with features such as:
AI-driven insights and benchmarks
Customizable dashboards and advanced analytics
Seamless integration with existing tools and processes
Initiative tracking and reporting
Automation for R&D cost capitalization and security vulnerability management
Enterprise-grade scalability and data infrastructure
APIs including Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library
These capabilities are designed to optimize engineering productivity, software quality, and developer experience for large-scale enterprises.
Does Faros AI support integration with other tools?
Yes, Faros AI is designed for interoperability and can connect to any tool—cloud, on-prem, or custom-built. This ensures seamless integration with your existing workflows and minimal disruption during adoption.
What APIs are available with Faros AI?
Faros AI provides several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library. These APIs enable flexible data integration, automation, and advanced analytics for engineering organizations.
Use Cases & Business Impact
Who can benefit from Faros AI?
Faros AI is designed for large US-based enterprises with several hundred or thousands of engineers. Target roles include VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, Technical Program Managers, and Senior Architects.
What business impact can customers expect from using Faros AI?
Customers can expect significant business impacts, including:
50% reduction in lead time, accelerating time-to-market
5% increase in efficiency and delivery
Enhanced reliability and availability
Improved visibility into engineering operations and bottlenecks
What are some case studies or use cases relevant to the pain points Faros AI solves?
Faros AI has helped customers make data-backed decisions on engineering allocation and investment, improve visibility into team health and KPIs, align metrics across roles, and simplify tracking of agile health and initiative progress. Explore detailed examples and case studies at Faros AI Blog.
Pain Points & Solutions
What problems does Faros AI solve for engineering organizations?
Faros AI solves core problems such as:
Identifying bottlenecks and inefficiencies for faster, predictable delivery
Ensuring consistent software quality, reliability, and stability
Measuring the impact of AI tools and tracking adoption
Aligning skills and addressing shortages of AI-skilled developers
Guiding investments to improve DevOps maturity
Providing clear reporting for initiative delivery
Correlating developer sentiment with process data
Automating R&D cost capitalization
What are the key pain points expressed by Faros AI customers?
Customers often face challenges such as:
Difficulty understanding bottlenecks and achieving faster, predictable delivery
Managing software quality and reliability, especially from contractors' commits
Measuring the impact of AI tools and tracking adoption
Skill alignment and shortages of AI-skilled developers
How does Faros AI differentiate itself in solving these pain points?
Faros AI offers tailored solutions for each persona and pain point, such as granular insights into bottlenecks, tools for managing contractor commits, robust AI adoption tracking, strategic guidance for DevOps investments, clear reporting for initiative delivery, and automation for R&D cost capitalization. This comprehensive, data-driven approach sets Faros AI apart from competitors.
KPIs & Metrics
What KPIs and metrics does Faros AI use to address engineering pain points?
Faros AI tracks key metrics such as:
DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR)
Team health and tech debt
Software quality and PR insights
AI adoption, time savings, and impact
Workforce talent management and onboarding metrics
Initiative tracking (timelines, cost, risks)
Developer sentiment correlations
Automation metrics for R&D cost capitalization
Implementation & Technical Requirements
How long does it take to implement Faros AI and how easy is it to start?
Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources. Git and Jira Analytics setup takes just 10 minutes. Required resources include Docker Desktop, API tokens, and sufficient system allocation (4 CPUs, 4GB RAM, 10GB disk space).
Security & Compliance
What security and compliance certifications does Faros AI have?
Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, demonstrating its commitment to robust security and compliance standards. (Faros AI Security)
How does Faros AI prioritize product security and compliance?
Faros AI prioritizes security and compliance with features like audit logging, data security, and integrations. The platform is built to enterprise standards by design and regularly undergoes certification audits to maintain compliance.
Support & Training
What customer service or support is available to Faros AI customers?
Faros AI offers robust customer support, including access to an Email & Support Portal, a Community Slack channel, and a Dedicated Slack Channel for Enterprise Bundle customers. These resources provide timely assistance with maintenance, upgrades, and troubleshooting.
What training and technical support is available to help customers get started with Faros AI?
Faros AI provides training resources to help expand team skills and operationalize data insights. Technical support includes access to an Email & Support Portal, Community Slack, and Dedicated Slack channels, ensuring smooth onboarding and effective adoption.
Blog & Resources
Where can I find more articles and resources from Faros AI?
You can explore more articles, guides, and customer stories on the Faros AI blog at www.faros.ai/blog. Topics include AI, developer productivity, developer experience, and best practices.
What is the purpose of the Faros AI blog?
The Faros AI blog provides insights on best practices, customer stories, and product updates. It covers categories such as Guides, News, and Customer Success Stories to help engineering leaders stay informed and improve their operations.
How many times have you seen an engineering project get delivered behind schedule or with a reduced scope? Probably more often than you'd like.
We can all agree: Estimating is hard. Software development has a lot of known unknowns.
That said, seasoned leaders have found repeatable methods for improving estimations, which in turn help them allocate resources more confidently.
In a recent Faros webinar, Mustafa Furniturewala, SVP of Engineering at Coursera, shared his tips for improving predictability, including how to scope, estimate, and communicate your commitments.
Scoping: Invest a little more upfront
Mustafa is a big fan of the Shape Up methodology from the folks over at Basecamp.
Shaping involves an upfront investment in project clarity and risk management that pays off by making the development process more focused, efficient, and likely to succeed in delivering value.
The process helps identify the core elements that will make up the solution and how they fit together, without going into the minutiae of implementation. “The gist is that you shape the product in the right way to understand what the actual requirements are and to scope them more accurately,” says Mustafa.
By the time a project is shaped and ready for development, it should be de-risked, meaning the major uncertainties have been addressed. This allows teams to work with confidence and reduces the likelihood of major hurdles or project stalls during the development phase.
Estimating: Use the right tool
A common anti-pattern is trying to achieve extremely accurate estimations, down to the number of hours. Given that R&D work always has some unknowns, what is the point of getting to that level of detail?
Instead, Mustafa recommends using higher-level estimation tools like T-shirt sizing or story points, which are closely related.
T-shirt sizing is an estimation technique where projects or tasks are categorized into sizes (XS, S, M, L, XL) to represent the complexity or effort required, rather than assigning specific hours or days. It focuses on the relative size of a project rather than exact durations, so requests can be estimated quickly without getting bogged down in details.
Story Pointing involves assigning a point value to tasks or user stories to indicate their complexity, effort, and risk, using a predefined scale (often Fibonacci-like: 1, 2, 3, 5, 8, 13, etc.). Story pointing enables a more nuanced understanding of effort and complexity for teams that have a good understanding of their velocity. It’s commonly used for sprint planning and backlog prioritization.
Given story points' strong association with time (e.g., how many story points can fit into a two-week sprint?), some leaders are more partial to T-shirt sizing for high-level estimations. “I prefer using T-shirt sizing as much as possible, and then breaking it down into weeks if I need to,” shares Mustafa.
Committing: Set the right expectations
Communication is your friend. When you share an ETA with your stakeholders, it’s wise to set the expectation that this is only an estimation. "Expectation setting with stakeholders is important to make sure they understand that there are some risks here," advises Mustafa. Timelines may change as you begin to figure things out.
"Communicating is also extremely important," he continues. Part of getting your colleagues comfortable with the risk that timelines may shift is the commitment you make to them that you will inform them promptly if an ETA changes.
By showing you understand the importance of timely updates, you will strengthen the trust between engineering and its cross-functional partners and stakeholders.
Faros AI helps engineering leaders stay informed of how key initiatives are coming along and where the bottlenecks are, so they can be more proactive with that communication.
Initiative tracking in Faros AI helps engineering leaders promptly and proactively communicate when timelines change
In summary, while there is no silver bullet, if you want more precise estimations, you have to spend the time to spike and design what you want to build in a bit more detail.
And, equally important, you need to improve your ability to identify delays promptly, address them if possible, and communicate new ETAs promptly.
Naomi Lurie
Naomi is head of product marketing at Faros AI.
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