InteliG vs Faros AI: Data Aggregation vs Execution Intelligence

Faros AI does something genuinely useful: it pulls data from 50+ engineering tools into one place. Jira, GitHub, CI/CD, incident management, calendars — all consolidated into a unified data model you can query and visualize.

If your biggest problem is that engineering data lives in fifteen different tools and nobody can see the full picture, Faros solves that. It’s a real product solving a real problem.

But aggregation is not intelligence. And that distinction matters more than most CTOs realize when they’re evaluating tools.

What Faros AI Does

Faros AI is an engineering analytics platform built on data aggregation. It connects to dozens of integrations, normalizes the data into a graph model, and lets you build dashboards on top of it. They have an open-source core (Community Edition) with an enterprise layer on top for advanced features.

The value proposition is straightforward: stop logging into ten tools to understand what’s happening. Bring everything into one view. Build custom dashboards. Track DORA metrics. See developer activity across systems.

It’s well-built for what it is. The open-source foundation is a smart move — it lowers the barrier to adoption and gives engineering teams a way to evaluate before committing. The breadth of integrations is impressive.

But here’s the thing: once all that data lands in Faros, you’re still the one who has to make sense of it.

The Dashboard Problem

Dashboards are a presentation layer, not a thinking layer. They show you what happened. They don’t tell you why it happened, what it means, or what you should do about it.

Faros gives you a chart showing deployment frequency dropped 30% last month. Now what? You open Jira to look at sprint data. You check Slack for context on what the team was dealing with. You pull up the Git log to see if there was a refactoring effort that slowed things down. You schedule a meeting with the tech lead to ask questions.

The dashboard didn’t answer anything. It gave you a data point that generated more questions — questions you still have to chase down manually across multiple systems.

This is the fundamental limitation of aggregation-first platforms. More data in one place is better than more data in fifteen places. But it’s still just data. You’re the reasoning engine. And your time is the most expensive resource in the building.

What InteliG Does Differently

InteliG doesn’t aggregate data from fifty tools. It starts with one source of truth — Git — and builds intelligence on top of it.

Git is where the facts live. Commits happen automatically. Pull requests capture real collaboration. Deployments mark what actually shipped. None of this depends on someone remembering to update a ticket or log their time. The data is a byproduct of the work itself.

The core of InteliG is Cognis, an AI reasoning engine that connects your Git data across five pillars:

• Code Intelligence: What’s changing in your codebase, who’s driving it, what patterns emerge across commits and contributors.
• Strategy: Initiatives tracked through actual code changes, not ticket status. ROI graded on what shipped.
• Finance: Cost per contributor, investment distribution by initiative, and the real return on engineering spend.
• Knowledge: Decisions from meetings and architecture reviews linked to the code outcomes they produced.
• Cognis (AI): Ask a question. Get an evidence-backed answer reasoned across all four pillars.

You don’t build dashboards. You ask questions. “Which initiative delivered the most value last quarter?” Cognis reasons across contributor activity, code changes, deployment patterns, and cost data to give you an answer with evidence attached.

That’s not a chart you have to interpret. It’s an answer you can act on.

Aggregation vs. Opinion

Faros is deliberately unopinionated. It collects data from whatever tools you use and presents it however you want to see it. That flexibility is a feature for teams that want to build their own analytical workflows.

InteliG is deliberately opinionated. It connects commits to strategic initiatives. It grades contributors on ROI. It links meeting decisions to code outcomes. It tells you when an initiative is burning money without delivering value.

Opinionated means you get intelligence out of the box instead of spending weeks building custom dashboards that approximate the same insights. It means the platform has a point of view about what matters — and that point of view is grounded in what actually happened in the code, not what someone reported in a standup.

When Faros Makes Sense

If you’re already running a mature data engineering practice and you want raw engineering data in a queryable format you control, Faros is a solid choice. If you need to integrate with dozens of tools and build custom analytics for a large, complex org — the open-source core gives you a foundation to build on.

When InteliG Makes Sense

If you’re a CTO who is tired of staring at dashboards and drawing your own conclusions. If you want to ask a question and get an answer instead of building a chart and guessing at the narrative. If you believe the codebase is a better source of truth than a collection of tool integrations. If you want intelligence that connects code to strategy to finance to decisions — and reasons about all of it.

Faros gives you data. InteliG gives you execution intelligence.

Connect GitHub and ask Cognis →