IKL — Intelligence Knowledge Layer

The IKL is the layer that transforms individual domain measurements into network intelligence.

The problem it solves

A single domain’s measurement history cannot answer most of the questions that matter for action selection. Can this domain realistically reach VS 60 in this industry? If entity-schema is fixed, how much VS improvement should be expected, and how quickly? Which content formats produce better VS outcomes on ChatGPT vs. Google AIO? What does the citation landscape in this industry look like?

These questions require data across many domains, many industries, and many completed fix-and-verify cycles. The IKL is where that data aggregates and from which Ava draws its projections.

How it works

Every domain AVO measures contributes a Signal Profile — a compact, anonymised summary of its measurement state — to the IKL after each run. Every action verified, every content piece measured, every outcome recorded adds to the IKL’s corpus.

Ten active modules process this data:

• Industry Profiles — AS and VS averages by industry, DP benchmarks (daily refresh)
• Platform Behavior — how each AI platform responds across industries, including which platforms surface which entity types (weekly)
• Citation Authority — which domains AI platforms most frequently cite per industry (weekly)
• Entity Landscape — which entities co-appear in AI responses (weekly)
• DP-Signal Correlations — which Datapoint improvements produce the most VS lift, per industry and pillar (weekly)
• Format Effectiveness — which content structures and formats perform best per platform (weekly)
• Compound Effects — which combinations of DP improvements produce VS lift greater than the sum of their parts (monthly)

Privacy architecture

The IKL is org-blind. No individual client’s data is exposed to another. Minimum 5 comparable domains must exist in a cohort before any aggregate is surfaced — this prevents small-cohort inferences from reverse-engineering individual domain data. Signal Profiles are designed to be informative for aggregation while being uninformative about the specific domain that generated them.

Synthetic seeding

For industries where client coverage is thin, Samhita seeds the IKL with synthetic probe data — VS-style measurements run against industry queries without a specific client domain. This allows Ava to provide industry benchmarks even in categories where the AVO client base is still developing.