alloIA - 2026 Pricing, Features, Reviews & Alternatives

alloia is a sovereign, neutral infrastructure layer that sits between a company's product data and the AI engines now answering buyers in its place. It does two things: it makes the catalogue readable by machines, and it measures what those machines actually say.

Ingestion

alloia connects to the systems that hold product truth: ERP, PIM, PLM, DAM, CMS, e-commerce platforms, product feeds, databases, and flat files. Integration is API-driven rather than connector-dependent. Sync is live and multi-source, with reconciliation across systems when the same attribute exists in several places. No replatforming and no site rebuild are required.

Knowledge layer

Ingested data is structured into a knowledge graph. Each product is expressed as schema.org JSON-LD, with product schema, offer schema, review aggregation and FAQ attached. The graph carries the attributes, differentiators and trust signals that flat pages lose. Exposure is served in each engine's native format, with vector search and MCP-based tool access, CommerceTXT, ACP, UCP, A2A, x402, agent skills and an AI sitemap for discovery.

Exposure and control

Agent traffic is absorbed at the infrastructure layer rather than by the storefront. You control what is exposed and what is not, per product and per engine. Read latency is optimised for agent constraints: token cost of reading the catalogue is reduced by more than 40%, and read latency drops from 630ms to 33ms, a 19-fold improvement, which is what lets an engine read more of your catalogue in a single query window.

Measurement

alloia measures continuously, per product and per engine, across data completeness, hallucination rate, error rate, LLM extraction confidence, catalogue coverage and product discoverability. Behaviour is tracked end to end: indexation, training, live retrieval, agent tool queries, human AI referral and AI commerce actions. A 23-criteria technical exploitability score monitors the conditions under which agents access the catalogue, grouped in four families: on-site data structure, graph structure, discoverability, interoperability.

Measurement discipline is deliberate. AI output is compared against manufacturer ground truth, never predicted by another model. Sessions are clean. Results are reported in full, successes alongside failures.

Delivery

Data is available in the dashboard and through the API, so measurement can flow into the BI, warehouse and analytics platforms your teams already use. Everything is exportable.

Security and compliance

Product data is hosted in Europe on sovereign infrastructure. GDPR and AI Act compliant, certified AICPA SOC and Mila-TRAIL for responsible AI. The layer is neutral by architecture, no engine is favoured, and no vendor format locks the data in. Full reversibility: the data stays yours, in open standards, at all times.

Measured on live catalogues

Product specs completeness from 41% to 97%. Hallucinated specifications from 29.5% to 0.4%. LLM extraction confidence from 55.4% to 100%. Up to 10 times more products read by AI engines, catalogue coverage up 60%, product discoverability up 20%.