analytics, bi & data·run 296 · Jul 2026

Queryward

An access gateway for small-business data exposed to AI agents that resolves workload and delegated-user identity, asks source systems to enforce tenant, row, field, purpose, action, and time scope, and records a tamper-evident, correctable audit event for each attempted question and governed result without claiming that the August 2026 EU AI Act date mandates this ledger.

Genesis score6.92/10

Make Queryward real.0/500

500 more votes and Queryward is authorized for build.

0%500 to authorize

Backing is the vote. When an idea crosses 500, we pull it into the build pipeline and ship it for real — the votes decide what gets built next, not an editor.

The opportunity

2Cross-references

2Inbound connections

0Direct connections

The case

Small businesses are wiring agents to customer, revenue, support, and operating data faster than they can explain who asked what and why a result was allowed. Queryward evaluates a versioned policy before each tool call, minimizes returned data, records decision and provenance, and preserves denial, approval, acknowledgment, readback, export, deletion, and correction events. Agent identity is not user authority, gateway approval is not source authorization, a returned row is not accurate business truth, and tamper evidence is not lawful immutability. The August 2, 2026 EU AI Act milestone covered specified transparency obligations in the supplied research; the more directly relevant high-risk audit-trail regime was reported delayed until December 2027. Authority, applicability, identity, delegation, policy decision, source enforcement, result, business use, legal conclusion, and correction remain distinct.

Who pays — and why

A security, data, IT, compliance, or operations owner at a small business exposing customer or operating data to internal AI agents through tool servers and connectors.

What it unlocks

An identity and delegation graph linking tenant, human user, service identity, agent instance, session, client, credential, role, group, customer, purpose, consent, delegated scope, assurance level, revocation, and provenance

A policy model covering source system, object, row, field, action, purpose, jurisdiction, sensitivity, time, budget, rate, approval, masking, aggregation, retention, export, deletion, version, test, and correction

A privacy-aware question lifecycle separating request, identity proof, delegation, policy evaluation, source authorization, query plan, executed query, result minimization, delivery, business decision, disclosure, incident, subject request, deletion or restriction, legal hold, and correction

How Genesis scored it

6.92across seven criteria

Temporal window

The August 2026 transparency milestone is current, but the more relevant high-risk audit regime is later and applicability remains case-specific.

Asymmetric leverage

Policy, identity, enforcement adapters, audit schemas, tests, and review workflows can repeat across connectors.

Convergence

Two cross-references and two inbound connections provide moderate corroboration without a direct connection.

Why it scored well

Two cross-references, two inbound connections, a clear small-business governance problem, verified authorization and tool-server infrastructure, and a reusable policy and event model support the direction.

What's holding it back

There are no direct connections, the immediate EU deadline does not establish a general audit-ledger mandate, high-risk timing is later in the supplied research, source systems must enforce policy, privacy may prohibit retaining question content or returned rows, incumbents can extend, and pricing is unvalidated.

Signals detected4 sources crossed

SignalSupplied EU AI Act research

SignalSupplied EU AI Act timing research

SignalSource-run interface verification

SignalSource-run market scan

Direction briefqueryward.md

queryward.md

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Discussion

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