# Faros AI AI visibility in Software Engineering Intelligence & Developer Productivity

Canonical: https://devtune.ai/verticals/software-engineering-intelligence/faros-ai

[Website](https://www.faros.ai/)

Latest AI responses: 2026-11-11
[Tracked on paid plans](https://devtune.ai/pricing): Google AI Mode, Microsoft Bing Copilot Search, Claude with web search, Grok Search
Prompts: 25
Runs: 3


## Platforms

- chatgpt-search
- google-ai
- perplexity

Rank: 5
Joint rank: true
Total brands: 9
Measured responses: 75
Presence percent: 6.666666666666667
Share of voice percent: 9.12863070539419
Average position: 26
Docs presence percent: 0
Blog presence percent: 0
Brand mention percent: 22.666666666666664


## Profile

Overview: Faros AI is a software engineering intelligence platform founded in 2019 by the team behind Salesforce Einstein. It connects to a company's engineering systems, from version control and task tracking to CI/CD, incident and survey data. It unifies that data into one model so leaders can measure developer productivity, delivery performance, software quality and the impact of AI coding tools. Its products include Engineering Efficiency, AI Transformation and Delivery Excellence. It targets large, complex enterprises that need customizable analytics across 100+ integrations. Its current messaging extends into AI coding observability, spend attribution and policy governance, which it calls token engineering. Faros AI is privately held and based in the San Francisco Bay Area. Public customer stories include Coursera, Vimeo, SmartBear and Autodesk.
Product summary: Faros is a software engineering intelligence and developer productivity insights platform. It ingests data from 100+ engineering tools into a unified model. It provides prebuilt and custom dashboards, DORA and SPACE benchmarks, AI-impact analytics, bottleneck diagnostics and delivery tracking. Its latest positioning adds token spend attribution, model-route evaluation and a policy engine that governs AI coding agents.


### Key capabilities

- Unified engineering data model joining tickets, commits, PRs, CI/CD, incidents and surveys
- DORA metrics, including rework rate as a fifth metric, plus SPACE and other productivity benchmarks
- AI Transformation: measure AI coding tool adoption, impact and ROI with cohort comparisons
- Engineering Efficiency: bottleneck diagnosis, code quality and team health insights
- Delivery Excellence: initiative tracking and cross-team delivery predictability
- Token and spend attribution per session, PR, team and model
- Policy engine for model routes, budgets and guardrails, with audit trail
- Custom dashboards and connectors, a GraphQL API, and Lighthouse AI natural-language analysis



### Target users

- CTOs and VPs of Engineering at large enterprises
- Platform engineering and developer experience leaders
- Engineering managers and program or delivery leaders
- Engineering operations and finance stakeholders tracking R&D investment
- Teams rolling out and measuring AI coding tools



### Key use cases

- Measuring the ROI and adoption of AI coding assistants such as GitHub Copilot
- Establishing an enterprise-wide engineering productivity baseline
- Identifying delivery bottlenecks and improving DORA metrics
- Tracking cross-team initiatives and delivery predictability
- Attributing AI token spend to outcomes and governing model usage
- Executive and board-level reporting on engineering investment

Integrations ecosystem: Faros says it analyzes task, coding, deployment, quality, incident, security, org-structure and survey data from 100+ tools and custom sources. It supports custom connectors and a GraphQL API. Its newer positioning connects to AI coding agents, harnesses, model routers and gateways. It is listed on AWS Marketplace and Google Cloud Marketplace and has a Microsoft Azure partnership story. It has also published work on GitHub Copilot impact measurement.
Pricing summary: Faros does not clearly publish list pricing. Its Gartner profile says customers choose pre-built intelligence modules, with per-module price falling as more are added, or license the whole platform. The AWS Marketplace listing shows a Pro dimension at $69 per month per person in the engineering org, with a free first month for monthly users and a proof of concept for annual users. An Enterprise edition is available through private offer. Third-party sites report a Professional tier from about $29 per contributor, plus Enterprise and Ultimate tiers that require contacting sales. These figures conflict, so confirm with the vendor.
Review summary: Faros AI has high ratings but small review volumes. Gartner Peer Insights shows 4.5 out of 5 from 25 ratings, and G2 shows about 4.8 from 11 reviews. Reviewers praise flexibility in connecting and correlating data, the breadth of integrations and responsive support. They criticize the learning curve, dashboard load times and limited self-service.
Competitive positioning: Faros AI positions itself as an enterprise-focused software engineering intelligence platform. It stresses an open, customizable data model that unifies data from 100+ tools and custom sources without requiring teams to standardize data first. Its main pitch is large, heterogeneous engineering organizations. It offers Engineering Efficiency, AI Transformation and Delivery Excellence products. It markets itself on measuring the impact of AI coding agents, and its current homepage frames the product as a "token engineering platform" covering AI coding observability, optimization and governance. Third-party reviewers describe the customizable data model as a strength and also as the source of a steeper learning curve. Several competitors publish comparison content that frames Faros AI as heavier to configure.
Limitations: Reviewers cite a moderate to steep learning curve for the flexible data model, especially for non-data analysts. Some report slow dashboard loads on large datasets and limited self-service, which can mean opening support tickets. Reviewers also mention limited out-of-the-box alerting. Public pricing is unclear, and no free tier or self-serve trial is documented. Gartner and G2 review volumes are small. Faros has moved its messaging toward AI and token engineering, so older content about the open-source Community Edition may be outdated.


### Source urls

- https://www.faros.ai/
- https://www.faros.ai/platform
- https://www.faros.ai/company
- https://www.faros.ai/blog/faros-ai-is-now-available-on-google-cloud-marketplace
- https://www.faros.ai/blog/what-is-software-engineering-intelligence
- https://techcrunch.com/2023/06/27/faros-ai-raises-20m/
- https://pitchbook.com/profiles/company/435439-81
- https://www.gartner.com/reviews/product/faros-ai
- https://www.gartner.com/reviews/market/developer-productivity-insight-platforms/compare/product/dx-vs-faros-ai
- https://aws.amazon.com/marketplace/pp/prodview-ueeciflmambza
- https://www.faros.ai/blog/customer-stories-vimeo
- https://www.casestudies.com/company/faros/case-study/coursera-cuts-p0p1-incidents-by-70-with-faros
- https://www.casestudies.com/company/faros/case-study/smartbear-cuts-reporting-from-days-to-minutes-with-faros
- https://codepulsehq.com/guides/faros-ai-alternative
- https://ventureblog.com/lobby-capital-faros-ai/

Research approved: 2026-11-05T21:22:57.60763+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Coursera | Coursera replaced homegrown dashboards with Faros as it grew from 40 to over 300 engineers. It kept time-to-deploy under 30 minutes and cut critical incidents through automated pre-deploy checks. | 70% reduction in P0/P1 incidents |
| Vimeo | Vimeo used Faros to correlate GitHub Copilot usage with delivery performance. It reports shorter lead times and more confident decisions about expanding the rollout. | 30% increase in GitHub Copilot adoption |
| SmartBear | SmartBear centralized data across 25 products into Faros dashboards. Manual reporting that took days now takes minutes. | Reporting cut from days to minutes |



### Reviews breakdown

| Platform | Score | Score max | Review count | Url |
| --- | --- | --- | --- | --- |
| Gartner Peer Insights | 4.5 | 5 | 25 | https://www.gartner.com/reviews/product/faros-ai |
| G2 | 4.8 | 5 | 11 | Not available |



### Review themes



#### Praised

- Flexibility to connect and correlate disparate data sources
- Large number of integrations plus custom connectors
- Responsive, helpful support and onboarding
- Out-of-the-box dashboards with room for customization
- Stable platform with minimal downtime



#### Criticized

- Moderate to steep learning curve, especially for non-data analysts
- Dashboards can load slowly on large datasets
- Limited self-service for custom reports; support tickets needed
- Limited out-of-the-box alerting setup
- Data sync requires ongoing upkeep




### Company facts

Founded year: 2019
Hq: San Francisco, USA


#### Founders

- Vitaly Gordon
- Shubha Nabar
- Matthew Tovbin

Employees range: ~65 (third-party estimate)
Total funding: $36M (through 2023 Series A; later Pitch
Valuation: Not available
Arr: Not available
Customer count: Not available
Status: Private


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Jellyfish | 35 | 75 | 46.666666666666664 | 5.211267605633803 |
| LinearB | 27 | 75 | 36 | 5.386363636363637 |
| Swarmia | 25 | 75 | 33.33333333333333 | 5.4 |
| DX | 17 | 75 | 22.666666666666664 | 8.045454545454545 |
| Waydev | 5 | 75 | 6.666666666666667 | 13 |
| Faros AI | 5 | 75 | 6.666666666666667 | 26 |
| Allstacks | 4 | 75 | 5.333333333333334 | 2 |
| Uplevel | 1 | 75 | 1.3333333333333335 | 3 |
| Hatica | 1 | 75 | 1.3333333333333335 | 5 |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| chatgpt-search | 5 | 20 |
| google-ai | 0 | 0 |
| perplexity | 0 | 0 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Which engineering intelligence vendors offer open APIs and custom data models so we aren't locked into their built-in metrics? | 1 | 1 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which developer productivity platforms sync with HR and payroll systems to attribute engineering investment by team and cost? | 3 |
| What engineering intelligence vendors have a track record of rolling out to 500+ developers without a long services engagement? | 3 |
| Which engineering intelligence platforms integrate with the widest range of source control, issue tracking, incident and CI/CD tools? | 3 |
| What are the best tools that combine developer sentiment surveys with delivery data to find what slows teams down? | 3 |
| Which tools support software capitalization reporting for finance, based on actual engineering activity? | 3 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 0 |
| Developer Experience | 5 | 0 |
| Integrations & Ecosystem | 5 | 2 |
| Performance & Reliability | 5 | 2 |
| Setup & First Run | 5 | 1 |



## Prompt results

- Prompt text: Which platforms measure the adoption, cost and real impact of AI coding assistants across teams and tools?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| DX | 1 |
| Jellyfish | 2 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |


- Prompt text: Which engineering analytics platforms can a 40-person engineering team set up in an afternoon to get baseline cycle time and deployment metrics?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Swarmia | 1 |
| LinearB | 2 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| LinearB | 1 |
| Jellyfish | 3 |


- Prompt text: Which engineering metrics platforms do developers trust and accept, without it feeling like surveillance or individual stack ranking?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Swarmia | 1 |
| DX | 2 |
| LinearB | 4 |
| Jellyfish | 5 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Swarmia | 1 |
| LinearB | 3 |
| Jellyfish | 4 |


- Prompt text: Which engineering intelligence tools surface insights in team chat and pull requests, so engineers don't need another dashboard?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Swarmia | 3 |
| Jellyfish | 8 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Swarmia | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Swarmia | 5 |
| Jellyfish | 6 |


- Prompt text: Which engineering analytics platforms forecast delivery risk and roadmap slippage across multiple teams and initiatives?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |
| LinearB | 6 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Allstacks | 1 |
| Jellyfish | 2 |
| LinearB | 4 |
| Waydev | 5 |


- Prompt text: Which developer productivity platforms sync with HR and payroll systems to attribute engineering investment by team and cost?


#### Brand position by platform

Chatgpt-search: 8
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |
| Swarmia | 3 |
| LinearB | 6 |
| Faros AI | 8 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Swarmia | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |


- Prompt text: Which platforms help team leads set working agreements and track them, such as review turnaround time and work-in-progress limits?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |


- Prompt text: What should I look at when picking a developer productivity platform to replace homegrown spreadsheets and ad-hoc scripts for engineering reporting?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search





##### Google-ai





##### Perplexity




- Prompt text: What engineering intelligence vendors have a track record of rolling out to 500+ developers without a long services engagement?


#### Brand position by platform

Chatgpt-search: 6
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| DX | 1 |
| Jellyfish | 4 |
| Faros AI | 6 |
| LinearB | 8 |
| Swarmia | 11 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Uplevel | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Jellyfish | 2 |


- Prompt text: What are the best developer productivity tools whose metrics hold up when teams use inconsistent ticket and branching conventions?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| DX | 1 |
| LinearB | 6 |
| Jellyfish | 9 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| DX | 2 |
| Swarmia | 3 |
| Jellyfish | 6 |


- Prompt text: Which engineering intelligence platforms integrate with the widest range of source control, issue tracking, incident and CI/CD tools?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| DX | 1 |
| Jellyfish | 3 |
| LinearB | 5 |
| Swarmia | 7 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Waydev | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |
| LinearB | 3 |
| Swarmia | 4 |


- Prompt text: What are my options for engineering metrics tools with a free tier for a team under ten developers that is just starting to measure delivery?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Swarmia | 1 |
| LinearB | 8 |



##### Google-ai

| Display name | Position |
| --- | --- |
| LinearB | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Jellyfish | 4 |
| LinearB | 6 |


- Prompt text: Which platforms have the most trustworthy DORA metric calculations, with transparent definitions we can audit and adjust?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| DX | 10 |
| Swarmia | 11 |



##### Google-ai

| Display name | Position |
| --- | --- |
| DX | 1 |



##### Perplexity




- Prompt text: What engineering analytics tools do engineering managers prefer for running retrospectives and one-on-ones with real data?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search





##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |
| LinearB | 4 |


- Prompt text: What are the best tools that combine developer sentiment surveys with delivery data to find what slows teams down?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| DX | 1 |
| Jellyfish | 2 |
| Swarmia | 3 |
| LinearB | 4 |
| Hatica | 5 |



##### Google-ai

| Display name | Position |
| --- | --- |
| DX | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LinearB | 1 |
| Jellyfish | 3 |


- Prompt text: Which engineering intelligence platforms connect to our source control, ticketing and CI systems with the least configuration and data cleanup?


#### Brand position by platform

Chatgpt-search: 5
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |
| Swarmia | 3 |
| DX | 4 |
| Faros AI | 5 |
| LinearB | 8 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Swarmia | 1 |
| LinearB | 2 |
| Jellyfish | 3 |


- Prompt text: Which engineering intelligence vendors offer open APIs and custom data models so we aren't locked into their built-in metrics?


#### Brand position by platform

Chatgpt-search: 1
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Faros AI | 1 |
| Jellyfish | 4 |
| LinearB | 5 |
| DX | 6 |
| Waydev | 27 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Waydev | 4 |


- Prompt text: What tools let us export raw engineering metrics to our data warehouse so analysts can build custom reports?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Swarmia | 1 |
| DX | 2 |
| Jellyfish | 9 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |


- Prompt text: Which tools support software capitalization reporting for finance, based on actual engineering activity?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Allstacks | 1 |
| Jellyfish | 2 |
| Swarmia | 4 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Allstacks | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Swarmia | 1 |
| Jellyfish | 2 |
| Allstacks | 3 |
| LinearB | 5 |
| DX | 8 |


- Prompt text: What engineering analytics tools work well with an existing internal developer portal or service catalog so ownership data stays consistent?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| DX | 1 |
| Jellyfish | 2 |
| LinearB | 3 |
| Swarmia | 4 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| DX | 1 |


- Prompt text: Which engineering analytics tools handle a mix of cloud-hosted and self-managed engineering systems, with private deployment options for regulated companies?


#### Brand position by platform

Chatgpt-search: 9
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Waydev | 1 |
| DX | 4 |
| Faros AI | 9 |
| Jellyfish | 11 |
| Swarmia | 13 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| LinearB | 1 |
| Swarmia | 3 |


- Prompt text: What are the best engineering intelligence tools for showing the board how engineering time splits across new features, maintenance and unplanned work?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Jellyfish | 4 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |


- Prompt text: Which engineering intelligence platforms stay accurate and fast when ingesting data from thousands of repositories across a large enterprise?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LinearB | 3 |
| Swarmia | 5 |



##### Google-ai





##### Perplexity




- Prompt text: Which tools give engineering leaders useful benchmarks against industry peers in the first month, before we have much history of our own?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| LinearB | 2 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Jellyfish | 1 |


- Prompt text: What platforms offer workflow automation, such as automatic pull request routing and review nudges, alongside DORA metrics?


#### Brand position by platform

Chatgpt-search: Not available
Google-ai: Not available
Perplexity: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LinearB | 1 |
| Swarmia | 4 |



##### Google-ai





##### Perplexity

| Display name | Position |
| --- | --- |
| LinearB | 1 |





## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://www.faros.ai/ | The complete token engineering platform \| Faros | faros.ai | Not available | commercial | landing_page | 7 | 7 |
| https://www.faros.ai/blog/customer-stories-f100-financial-services | Faros F100 customer case study | faros.ai | Not available | commercial | blog_post | 1 | 1 |
| https://www.faros.ai/blog/faros-ai-powers-ai-driven-engineering-with-microsoft-azure | Faros powers AI-driven engineering excellence with Microsoft Azure | faros.ai | Not available | commercial | blog_post | 1 | 1 |
| https://www.faros.ai/blog/towards-engops-scaling-engineering-orgs-with-data | Scaling Your Engineering Org with Data-Driven EngOps \| Faros AI | faros.ai | Not available | commercial | blog_post | 1 | 1 |
| https://www.faros.ai/blog/faros-ai-a-modern-data-stack-for-engineering-operations | Faros AI: A Cutting-Edge Data Stack for Engineering Ops | faros.ai | Not available | commercial | blog_post | 1 | 1 |
| https://www.faros.ai/blog/measuring-engineering-productivity-2026 | Measuring engineering productivity in 2026 | faros.ai | Not available | commercial | blog_post | 1 | 1 |
| https://www.faros.ai/blog/globant-faros-ai-partnership-for-efficient-agentic-projects | Faros and Globant Announce Partnership \| Press Release | faros.ai | Not available | commercial | blog_post | 1 | 1 |
| https://www.faros.ai/blog/how-faros-ai-strengthens-mckinsey-agile360-methodology | Faros Enhances McKinsey’s Agile360 Methodology | faros.ai | Not available | commercial | blog_post | 1 | 1 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| What should I look at when picking a developer productivity platform to replace homegrown spreadsheets and ad-hoc scripts for engineering reporting? | chatgpt-search | Website: Allstacks Faros AI Worth evaluating for engineering data integration, analytics, custom metrics, and broader engineering operations visibility. |
| What engineering intelligence vendors have a track record of rolling out to 500+ developers without a long services engagement? | chatgpt-search | My initial shortlist would be DX, Jellyfish, LinearB, Swarmia and Faros AI. However, there's an important distinction: evidence of operating at 500+ developers is not automatically evidence of a deployment without substantial vendor services. |
| Which engineering intelligence platforms connect to our source control, ticketing and CI systems with the least configuration and data cleanup? | chatgpt-search | ...is connecting source control, ticketing, and CI/CD with minimal setup and minimal manual data normalization, I would put Jellyfish, LinearB, and Swarmia at the top of the shortlist, with Faros AI worth evaluating for more complex enterprise environments. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| chatgpt-search | Jellyfish | The strongest candidates are Jellyfish, Swarmia, LinearB, and Faros AI. They differ in how deeply they integrate HR and payroll data versus simply estimating engineering effort. |
| chatgpt-search | Swarmia | The strongest candidates are Jellyfish, Swarmia, LinearB, and Faros AI. They differ in how deeply they integrate HR and payroll data versus simply estimating engineering effort. |
| google-ai | Swarmia | Swarmia * How it handles the connection: Swarmia integrates with your HRIS to account for part-time schedules, employee locations, vacations, and sick leave. |
| perplexity | Jellyfish | Jellyfish — combines engineering activity with roster, HR, payroll, and cost data to calculate team and initiative-level effort and cost, and to support capitalization reporting. |
| chatgpt-search | DX | My initial shortlist would be DX, Jellyfish, LinearB, Swarmia and Faros AI. However, there's an important distinction: evidence of operating at 500+ developers is not automatically evidence of a deployment without substantial vendor services. |
| google-ai | Uplevel | ### Alternative Models (Where Services Are Typically Required) Vendors like Uplevel or enterprise-heavy tiers of Jellyfish often approach rollouts for 500+ developers differently. |
| perplexity | Jellyfish | [1] Jellyfish is another candidate: it advertises fully self-service configuration and says it serves 1,000+ engineering organizations. |
| chatgpt-search | DX | If your primary criterion is the widest integration coverage across source control, issue tracking, incident management, and CI/CD, I would put DX (GetDX), Jellyfish, LinearB, and Swarmia at the top of the shortlist. |
| chatgpt-search | Jellyfish | If your primary criterion is the widest integration coverage across source control, issue tracking, incident management, and CI/CD, I would put DX (GetDX), Jellyfish, LinearB, and Swarmia at the top of the shortlist. |
| google-ai | Waydev | Waydev / LinearB (gitStream): Known for having some of the broadest plug-and-play integration ecosystems. |
| perplexity | Jellyfish | Jellyfish and LinearB appear to have the broadest documented coverage overall, with Swarmia also offering strong integrations across the four categories. |
| perplexity | LinearB | Jellyfish and LinearB appear to have the broadest documented coverage overall, with Swarmia also offering strong integrations across the four categories. |



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