AirOps helps you produce and refresh owned content at scale. That is useful — but it works on one input among many, and only on the pages you own. The deeper problem is that organisations have lost control over how AI understands them. Kodiac is the control layer for AI-mediated discovery: understand how AI interprets your organisation, improve what it relies on, then participate directly as a system AI depends on. The comparison below sets out the differences capability by capability.
Kodiac is built as three phases of one journey — understand how AI interprets your organisation (Kodiac Audit), improve what it relies on with structured, governed content across your existing systems (Kodiac Content), and participate directly as the system AI depends on (Kodiac Agent). The capabilities below are present in Kodiac and absent or materially weaker in AirOps.
AirOps connects AI visibility data to content execution, but the visibility layer is single-layer - it tracks where the brand appears in AI outputs and routes that data into the execution engine. Kodiac's three-layer audit adds Layer 2 (a 10-dimension website AI-readiness score) and Layer 3 (Source Intelligence on Reddit, Wikipedia, G2, news, Hacker News with per-source AI weight). Two layers of diagnostic depth that AirOps does not currently provide.
AirOps is fundamentally an owned-content platform. The product is built to update and refresh pages on the brand's own site at scale. It does not currently provide per-source AI weight scoring, sentiment alerts, or category-specific intervention playbooks for the third-party sources (Reddit, Wikipedia, G2, news, Hacker News) that drive most AI brand representation. The most consequential layer of brand visibility sits outside what AirOps operates on.
AirOps has no equivalent to Kodiac Agent. AirOps connects visibility to content updates; Kodiac Agent connects your brand directly to customer AI systems via MCP and REST so they can query your authoritative source. Different category of product, addressing the strategic frontier rather than the execution layer.
AirOps' thesis is that better-structured owned content closes the AI visibility gap. Kodiac's thesis is that most of the gap is structural - it comes from sources the brand does not own - and that the long-term answer is direct participation via Brand Agent. Different bets on where AI-mediated discovery goes next.
A capability-by-capability view of where the two platforms diverge. Kodiac on the right, AirOps on the left, no padding or rounding.
| Dimension | AirOps | Kodiac |
|---|---|---|
| Architecture | Content engineering platform with AI visibility input | Control layer: Audit + Content + Agent + Workspace |
| Audit layers | Layer 1 (output) feeding execution | All three layers, full depth |
| Source Intelligence | Not a primary surface | Per-source AI weight + sentiment + playbooks |
| Owned content workflows | Strong (Grids, Page360, Workflows) | Kodiac Content (connect, govern, author, serve) |
| Bulk page operations | Spreadsheet-style Grid for thousands of pages | Available; not the marketing primary |
| Brand Agent (MCP/REST) | Not available | Kodiac Agent with Playground |
| Multi-brand / agency workspace | Not a primary feature | Native multi-brand + white-label |
| Best fit | Enterprise content ops teams running large refresh programs | Enterprise + agencies running all three phases end to end |
The questions buyers ask most often when deciding between the two.
AirOps is a content engineering platform with AI visibility tracking as an input to its execution engine. Kodiac is a four-product platform built around the three-layer audit, Source Intelligence depth, and direct participation via Brand Agent. AirOps is strong if your bottleneck is content execution at scale on your own site. Kodiac is built for teams who recognise that the overwhelming majority of AI brand representation comes from third-party sources and want a platform that operates on all three layers.
Not at the depth Kodiac provides. AirOps tracks AI citations and uses them to prioritise content updates, but the product surface is built around owned-content execution. It does not currently provide per-source AI weight scoring, sentiment alerts on individual third-party sources, or category-specific intervention playbooks for Reddit, Wikipedia, G2, and news the way Kodiac's Source Intelligence does.
Partially. There is real overlap on the content-layer surface - both products help teams operationalise content for AI discovery. The structural difference is that Kodiac Content is a connect-and-serve layer that integrates with existing CMSs (Sitecore, AEM, Contentful, SharePoint) and exposes a RAG retrieval endpoint for AI engineering teams; AirOps is a content engineering platform that produces and refreshes content directly. Different operating models for different teams.
No. AirOps does not currently offer an MCP server or REST agent endpoint. Kodiac Agent - exposing your brand to customer AI systems for direct query - is a different category of product than content execution.
The decision usually breaks on whether your single biggest constraint is owned-content execution at scale or end-to-end control across the whole journey — understand, improve, participate. If you have thousands of pages that need bulk refreshing and AI-aware optimisation, AirOps is purpose-built for that. If you want to diagnose across output, website, and third-party ecosystem - then improve at source and participate directly via a Brand Agent - Kodiac is built for that mission. Many enterprise teams may eventually need both.
Run the free Kodiac audit. Sixty seconds, no credit card — a clear view of how the major AI systems interpret your organisation today, and what the next two phases would change.