Faros API integration and workflow automation

Faros is an engineering operations platform that pulls data from across your development toolchain (Jira, GitHub, GitLab, PagerDuty, CI/CD pipelines) and...

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What you can automate with Faros

Faros is an engineering operations platform that pulls data from across your development toolchain (Jira, GitHub, GitLab, PagerDuty, CI/CD pipelines) and unifies it into a single analytics layer. Instead of manually stitching together spreadsheets to understand deployment frequency, cycle time, or incident response, Faros gives engineering leaders a consolidated view of how their teams actually ship software. The core problem Faros solves is visibility. Most engineering organisations run dozens of tools, and each one holds a fragment of the picture. Faros connects to these tools through pre-built connectors, normalises the data, and surfaces metrics like DORA (deployment frequency, lead time, change failure rate, mean time to recovery). This makes it useful for engineering managers tracking team performance, CTOs reporting to the board, and platform teams identifying bottlenecks in the delivery pipeline. At Osher, we work with Faros as part of our system integration projects. When clients have fragmented dev toolchains with data sitting in silos, we connect Faros to their existing stack and build dashboards that give leadership real answers about engineering throughput. For teams already using n8n or similar automation platforms, we can also pipe Faros metrics into automated alerting workflows through our automated data processing services.

Faros FAQs

Frequently Asked Questions

Common questions about how Faros consultants can help with integration and implementation

Your existing tools (Jira, GitHub, PagerDuty, etc.) each track their own slice of the engineering workflow. Faros sits on top of all of them, pulling data through pre-built connectors and normalising it into a unified data model. The result is cross-tool metrics you can't get from any single platform, such as the full cycle time from ticket creation to production deployment, or correlation between code review turnaround and incident rates.

Faros has pre-built connectors for most major engineering tools, including GitHub, GitLab, Bitbucket, Jira, Linear, PagerDuty, Opsgenie, Jenkins, CircleCI, and more. It also supports custom connectors via its API, so if you're running a less common tool, it can usually still be connected. Our integration team can assess your specific toolchain and confirm compatibility.

Faros ingests data from your source control, CI/CD, issue tracking, and incident management systems. It then maps events across these systems to calculate the four DORA metrics: deployment frequency (how often you deploy to production), lead time for changes (commit to deploy), change failure rate (percentage of deployments causing incidents), and mean time to recovery. The accuracy depends on how well your tools are configured, which is something we help with during setup.

Yes. Faros exposes its data through APIs and supports webhook-based notifications. We commonly connect Faros outputs to Slack channels for automated engineering health updates, or feed metrics into existing BI tools like Looker or Metabase. Using n8n, we can also build custom automation workflows that trigger alerts when metrics drop below defined thresholds.

For a straightforward setup with common tools (GitHub, Jira, a CI platform), a basic deployment can be running within a few days. More complex environments with custom connectors, multiple teams, and specific dashboard requirements typically take two to four weeks. The biggest variable is usually data quality in your source systems rather than the Faros configuration itself.

Faros can work for teams of any size, but the return on investment is highest for organisations with at least 15-20 engineers running multiple tools. Smaller teams with a simple toolchain (say, GitHub and Linear) might get enough insight from those tools' built-in reporting. If you're unsure, we can do a quick assessment of your stack to determine whether Faros would add meaningful value.

How it works

Implementing Faros

Step 1

Process Audit

We review your current engineering toolchain end-to-end: source control, issue tracking, CI/CD, incident management, and any custom internal tools. The goal is to document which data lives where, how teams currently measure engineering performance, and where the gaps are. This audit typically involves interviews with engineering leads and a walkthrough of existing dashboards or reporting.

Step 2

Identify Automation Opportunities

Based on the audit, we identify which Faros connectors to deploy and which metrics matter most to your organisation. We also look for automation opportunities beyond basic dashboards, such as automated sprint health reports piped to Slack, threshold-based alerts for deployment failures, or weekly engineering summaries generated and distributed without manual effort.

Step 3

Design Workflows

We map out the data flows from each tool into Faros, design the dashboard layouts for different stakeholder groups (engineering managers, VPs, the board), and plan any downstream automations. This phase produces a clear implementation plan with defined data sources, metrics, access controls, and integration points with tools like n8n or Slack.

Step 4

Implementation

Our team configures Faros connectors for each tool in your stack, sets up the data synchronisation schedules, builds the agreed dashboards, and deploys any automation workflows. We handle API authentication, data mapping, and connector testing. For self-hosted Faros instances, we also manage the infrastructure deployment.

Step 5

Quality Assurance Review

We validate that metrics are accurate by cross-referencing Faros outputs against source systems. This means checking that deployment counts match your CI/CD logs, incident timelines align with PagerDuty records, and cycle time calculations correctly span from commit to deploy. Any discrepancies are traced back to connector configuration or source data issues and resolved.

Step 6

Support and Maintenance

Post-launch, we monitor connector health and data freshness, troubleshoot any sync failures, and adjust dashboards as your team structure or toolchain evolves. If you add new tools or teams, we configure the additional connectors. We also run periodic reviews to ensure the metrics being tracked still align with your engineering goals.