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NativeAI

NativeAI is a unified AI gateway that gives enterprises a single control plane for every model and agent framework. With no-code workflows, built-in RAG pipelines and data-governance guardrails, teams can collaborate across departments while optimizing cost, latency and compliance.
Rating:
5
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AI gatewayunified LLM gatewaymulti-model routingLLMOps platformRAG pipelineno-code AI workflowAI cost optimizationenterprise AI governance

Features of NativeAI

Single endpoint for every model and agent framework—route, rate-limit and audit from one place.
Task-aware routing across models to boost reliability and cut runtime cost.
Prompt hub that versions, A/B tests and pushes prompts to any provider.
Full-stack observability: latency, throughput, token cost, error rate and audit logs.
Data & prompt classification with token-level access controls and PII redaction.
Workspace-level RBAC, prompt versioning and policy enforcement.
Drag-and-drop workflow builder—ship AI apps in minutes, compare KPIs side-by-side.
End-to-end RAG: ingest, chunk, embed, store, retrieve and evaluate in one flow.
Pluggable adapters for any LLM or agent framework—OpenAI, Anthropic, Llama, LangChain, CrewAI, etc.
Gateway plugin SDK for custom models, fine-tunes and post-processing rules.

Use Cases of NativeAI

Centralize access control and cost tracking when operating hundreds of models.
Let business and IT co-build agent workflows without writing code.
Enforce data residency and prompt compliance across production environments.
Deliver real-time, citation-backed answers with enterprise RAG.
Expose internal AI capabilities to partners through a single, governed API.
Benchmark models on cost, latency and relevance before rolling to users.
Design, test and iterate on LLM/agent orchestration visually.

FAQ about NativeAI

QWhat is the NativeAI unified gateway?

It is one endpoint that routes requests to any model or agent, while handling auth, rate limits, logging and cost tracking.

QWhich models and frameworks are supported?

Any—OpenAI, Anthropic, Google, Llama, LangChain, CrewAI, Autogen, etc.—via standard SDK or REST.

QWhat does the no-code workflow do?

Lets you build, test and deploy AI apps with drag-and-drop blocks and compare live KPIs without writing code.

QWhat are the core parts of the RAG pipeline?

Data ingestion, chunking, embedding, vector storage, retrieval strategy and built-in evaluation.

QHow does NativeAI handle data governance?

Classifies data & prompts, enforces token-level access controls, audit trails and compliance policies.

QDoes it provide cost and performance monitoring?

Yes—latency, throughput, token cost and error metrics are tracked in real time; pricing is not disclosed.

QWho should use NativeAI?

Enterprises that need to operate LLMs at scale, across teams, while staying compliant and cost-efficient.

QHow are security and boundary rules enforced?

Via sensitivity labeling, role-based access, audit logs and policy checks on every request and response.

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