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Use case 098

Retrieval-Augmented Generation Gateways

A deployment concept for Enterprise search teams procuring governed retrieval services for internal assistants; RAG platform vendors integrating permission-aware gateway infrastructure; Specialist AI integrators building retrieval systems for regulated organisations.

Proposed deployment · Compatibility assessment required
Artificial Intelligence and Data Systems

Why this environment matters

A retrieval-augmented generation gateway supplies a language model with documents and search results. The key risk is that retrieved content can contain instructions while the requesting user has access to only part of the underlying collection. This concept binds retrieval to the user’s authority and keeps document text separate from tool permissions.

The security challenge

A proposed gateway would associate each request with an authenticated user and an allowed resource set. Search would operate within that set rather than retrieving broadly and hoping the final answer removes restricted information. Retrieved passages would retain document identifiers so the response can be traced back to the material used.

How the capsule model could help

NØNOS could isolate document parsers, retrieval connectors and response assembly. A connector would hold only the credentials needed for its approved collection. Text returned by that connector would not be able to grant access to another repository or create an outbound network capability. These controls would constrain possible actions even when the model follows a malicious instruction. Operating-system isolation does not establish factual correctness or reliable resistance to prompt injection. An answer may quote too much, infer sensitive information or misinterpret a permitted document. The application still needs response controls and an evaluation set reflecting its actual data and users. The prototype should also examine cached results and conversation history. A retrieval cache shared across users can undermine careful connector permissions if its entries lose their access context. A new request should not inherit documents retrieved under somebody else’s authority merely because the question is similar.

Separate address spaces and capability checks can limit cross-process reach. They cannot stop harmful use of legitimate permissions, prove AI decisions correct or substitute for domain-specific safety controls.

Deployment requirements

This proposal concerns resource access around a model. It does not make model output trustworthy, provide automatic data classification or establish a compliant enterprise AI service. Connector support and identity integration remain implementation work. Evaluation requirements: Place adversarial instructions in a test document and verify that they cannot enable a new connector or outbound endpoint. Ask equivalent questions as two users with different access and inspect both retrieval results and cache behavior. Remove document access during a session and test the defined policy for subsequent retrieval and stored context.

Current public-beta limitations, hardware support and application availability must be assessed before any pilot. Neither this use case nor an industry source establishes NONOS certification or a current customer deployment.

Authorise before retrieval, then check what leaves

A proposed gateway would associate each request with an authenticated user and an allowed resource set. Search would operate within that set rather than retrieving broadly and hoping the final answer removes restricted information. Retrieved passages would retain document identifiers so the response can be traced back to the material used.

NØNOS could isolate document parsers, retrieval connectors and response assembly. A connector would hold only the credentials needed for its approved collection. Text returned by that connector would not be able to grant access to another repository or create an outbound network capability. These controls would constrain possible actions even when the model follows a malicious instruction.

The model can still produce an unsafe or wrong answer

Operating-system isolation does not establish factual correctness or reliable resistance to prompt injection. An answer may quote too much, infer sensitive information or misinterpret a permitted document. The application still needs response controls and an evaluation set reflecting its actual data and users.

The prototype should also examine cached results and conversation history. A retrieval cache shared across users can undermine careful connector permissions if its entries lose their access context. A new request should not inherit documents retrieved under somebody else’s authority merely because the question is similar.

Who could buy or integrate it?

  • Enterprise search teams procuring governed retrieval services for internal assistants
  • RAG platform vendors integrating permission-aware gateway infrastructure
  • Specialist AI integrators building retrieval systems for regulated organisations

Industry examples: Elastic, Pinecone. Organisations shown illustrate the industry. No NONOS customer, partner or endorsement relationship is implied.

Market opportunity

Market benchmarks and device scenarios.

Published industry benchmark
US$1.2 billion

Retrieval augmented generation

Global · 2024 · annual market estimate

RAG products and services spanning document retrieval, recommendations and content generation; not standalone gateway OS sales.

Modelled global devices
40K–1.6M

Candidate OS endpoints

Hypothetical planning range · 2025

Low confidence: planning assumptions. Hardware compatibility, procurement and adoption have not been validated.

Illustrative annual licensing
$4M–$640M

USD / year at full model coverage

Device scenario × assumed US$100–$400 per device / year.

Not a revenue forecast, announced price or measured serviceable market.

Device calculation

Hypothetical global planning range, 2025 scenario: assume 20,000–200,000 organizations hosting production retrieval-augmented-generation services × 2–8 candidate OS endpoints per site/asset = 40,000–1,600,000 endpoints. Counting unit: dedicated RAG gateway servers/VMs; documents and queries excluded. Site and asset counts, and devices per site, are planning assumptions. The installed base has not been measured. Coverage is limited to the defined equipped subset; includes all candidate endpoints within that assumed subset. Hardware eligibility, certification, adoption and achievable NØNOS share are unverified; overlaps other cases.

Retrieval augmented generation market report ↗

Market context only; separate from device and site population estimates. Original monetary-market scope and geography are preserved in benchmark. This source does not establish the assumed worldwide site count or endpoint density.

How to interpret the figures

Adjacent or broader commercial market benchmark; not the NØNOS OS market, licensable-device count or revenue forecast.

Modelled candidate endpoints multiplied by an assumed annual USD price per endpoint. Pricing is a planning assumption, not a vendor quote. This illustrates the full scenario range, not revenue or total addressable market. It excludes adoption timing, procurement, certification, support costs, channel economics and achievable market share. Use cases can overlap, so their totals do not represent unique devices.

Research from 2026. Publisher estimates have not been independently audited.

Read the full methodology

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