The AI data plane,
item by item.

Keyword retrieval is a native query shape over entity attributes.

Search embedding-bearing entities by semantic similarity without a separate vector service.

Fuse keyword candidates, facets, and optional cosine reranking in one product-search call.

Constrain retrieval to a record kind before ranking to keep application domains separate.

Normalise case and accents as text enters the native search index.

Use a persisted, incrementally maintained retrieval path for growing collections.

Plan vector retrieval that understands structured filters before candidate selection.

Apply explicit ranking policies across lexical, semantic, and business signals.

Retrieve over image, audio, and text representations under one query contract.

Learn how to combine retrieval signals from labelled relevance data.

Use one completion response shape across supported LLM providers.

Call the Anthropic Messages API through the pluggable provider layer.

Use endpoints that implement the OpenAI chat-completions surface.

Run local models through an Ollama-compatible provider without a cloud key.

Try configured model providers in a deliberate fallback order.

Keep embedding dimensions explicit at write and query boundaries.

Route workloads to models by latency, privacy, and task shape.

Generate and refresh embeddings as a first-party managed workload.

Attribute tokens, latency, and spend by model and application action.

Track deployable fine-tunes beside the data and evaluation records that justify them.

Define tool inputs with JSON Schema for provider-independent calling.

Carry tool calls and results through a common message model.

Store agent state in queryable collections that share the data plane.

Design operations around bounded tools rather than unrestricted shell access.

Keep an auditable record of agent operations and their effects.

Require explicit review before destructive or expensive agent actions.

Persist resumable, long-running agent work with checkpoints.

Run task suites against agent versions before promotion.

Coordinate specialist agents with clear privileges and handoffs.

Learn safe tool-selection strategies from reviewed traces.

Store flexible JSON entities as first-class durable records.

Use direct key reads for exact context and application state.

Filter retrieval against structured application attributes.

Keep file metadata and permissions aligned with the application data model.

Build event context from the same mutation stream as application records.

Expose strongly typed application views over flexible documents.

Turn source files and connectors into governed retrieval records.

Track source, transform, and chunk lineage for every retrieval record.

Detect near-duplicate knowledge before it enters a corpus.

Ask which facts were valid at a particular time in a changing corpus.

Use application authentication and API-key controls in the same system.

Model access to data and operations through roles, keys, and scopes.

Treat audit events as part of the application and operations model.

Scope records and retrieval to the owning project or tenant.

Align file access metadata with application permissions.

Define which collections and fields an agent may retrieve from.

Apply reviewable redaction before model calls or exports.

Choose deployment and storage boundaries that meet regional needs.

Preview whether an access policy changes what an agent can retrieve.

Score confidence from source authority, freshness, and transformation history.

Derive application updates from the same mutation stream as records.

Subscribe to realtime updates over server-sent events.

Keep agents and interfaces current with data-plane events.

Persist context as application data instead of an isolated session cache.

Run context storage close to the application in a portable deployment.

Push new evidence into an active retrieval session as it arrives.

Transfer a working context between a person and an agent in real time.

Invoke governed agent workflows from durable data events.

Model the events that caused a fact or recommendation to change.

Control what teams and agents may contribute to shared memory.

Run the core system without adopting a single cloud provider.

Start with one deployable database process for the data plane.

Develop locally and package the same data plane for deployment.

Use named deployment profiles instead of exposing provider knobs first.

Keep provider-specific behaviour outside the storage kernel.

Offer a tuned read-oriented deployment profile for edge-facing workloads.

Move data and retrieval state between compatible environments.

Plan low-friction movement of an AI app between cloud providers.

Package models, retrieval, and operations for disconnected environments.

Operate a fleet without exporting sensitive operational metadata.

Expose health checks for a portable data-plane process.

Operate through application metrics, logs, and explicit lifecycle commands.

Manage collections, keys, users, backups, and deploy targets from familiar operations surfaces.

Treat backup and recovery as first-class operating concerns.

Publish measurement context instead of unsupported speed claims.

Track latency, recall, and freshness together for production retrieval.

Run a bounded response playbook for provider or quality failures.

Plan storage, retrieval, and inference capacity as one service envelope.

Let reviewed agents propose or execute bounded operational repairs.

Explain which source or ranking decision changed a model outcome.

Integrate application data and retrieval over an explicit server API.

Use a TypeScript client for application-facing database interactions.

Connect compatible tools through a model-context protocol surface.

Initialize, start, and inspect a portable local data-plane process.

Build on a native Rust engine with explicit systems boundaries.

Add typed helpers for common retrieval and context patterns.

Version prompts with the application code and deployment context.

Run retrieval and agent checks before pushing an application change.

Inspect the context and ranking path behind an individual response.

Generate reviewed test queries from a product’s real information architecture.

Publish documented benchmark context for retrieval performance.

Exercise BM25 and vector paths with named retrieval workloads.

Separate implemented, partial, and future capability claims.

Use explicit proof requirements before broad production claims.

Link public available-state claims to the product evidence behind them.

Compare approximate retrieval against an exact baseline for known datasets.

Evaluate retrieval quality with a maintained, task-specific judgement set.

Track answer quality changes as prompts, models, and corpora evolve.

Collect structured reviewer preferences for consequential outputs.

Combine quality, freshness, source, and policy signals into a decision to answer.