Data Flow & Architecture

Bring AI to the data.Not the other way around.

The fundamental data architecture problem with cloud AI is data gravity. Your data lives in your environment. Moving it to a cloud inference endpoint — even temporarily, even encrypted — creates risk, latency, and liability. Gridlight's architecture inverts this: the inference capability moves to your data, not the other way around.

Request a data architecture review →See the data pipeline

The data gravity principle

Data has gravity.
Inference should
follow it.

Data gravity is the tendency of large data assets to attract services toward them rather than the reverse — because moving data is expensive, slow, and risky. Cloud AI violates this principle by design: it requires you to transmit data to the inference endpoint.Gridlight resolves the gravity problem. The inference layer — models, routing, context management — runs where your data already lives. No data gravity violations. No transmission events. No exposure window.For data architects who have spent years designing compliant, governed data pipelines, this is the only AI architecture that doesn't introduce a new class of data movement risk.

CLOUD AI MODEL

Your DataEncrypted TransmissionCloud EndpointInferenceEncrypted TransmissionYour Application

Data leaves your environment on every inference call. Two transmission events per request.

GRIDLIGHT MODEL

Your DataLocal Context LoadGridlight Inference (on your hardware)Your Application

Data never leaves your environment. Zero transmission events outside your perimeter.

Inference data pipeline

What happens to your data on every inference request.

Every inference event follows a deterministic pipeline entirely within your environment. Here's exactly what happens at each stage — and what doesn't happen.

STEP 01User Query

Application sends request to Gridlight

STEP 02Auth & Policy

Identity verified, data class assigned

STEP 03Context Load

RAG retrieval from local vector store

STEP 04Model Routing

Optimal model selected by policy

STEP 05Local Inference

Model runs on your GPU. Data never leaves this layer.

STEP 06Log & Respond

Audit written

✦ Everything inside this boundary stays in your environment

What does not happen

Data transmission to cloud endpoint

Vendor access to query content

Model weight updates from queries

Inference logs leaving perimeter

Context management & RAG

Local retrieval. Local context. No external vector database required.

Gridlight includes an on-premises vector store and retrieval-augmented generation (RAG) pipeline. Your document corpus, knowledge base, and structured data stay in your environment — the entire context pipeline runs locally.

DOCUMENT INGESTION

Documents are chunked, embedded, and indexed into a local vector store using Gridlight's ingestion pipeline. Supported formats include PDF, DOCX, HTML, plain text, and structured JSON/CSV.

supported: Incremental indexing — new documents added without full re-index
supported: Metadata preservation for source attribution and lineage
supported: Access-controlled indexing — per-document RBAC inheritance

RETRIEVAL PIPELINE

At inference time, the query is embedded and matched against the local vector index. Retrieved chunks are assembled into context and passed to the selected model — entirely on-premises.

supported: Hybrid search: dense vector + sparse keyword retrieval
supported: Configurable retrieval depth and relevance thresholds
supported: Source attribution preserved in model output metadata

CONTEXT ISOLATION

Each inference request is context-isolated. Retrieved context is assembled per-request and discarded after inference. There is no persistent prompt history shared across users or sessions unless explicitly configured.

supported: Per-request context assembly — no cross-session leakage
supported: Tenant isolation for multi-department deployments
supported: Context window management configurable by policy

Model contamination prevention

Your inference data never becomes someone else's training data.

Model contamination is the largely unspoken risk in cloud AI: when your queries, documents, and business context are processed on a shared inference endpoint, there is a risk — however small — that this data influences future model behavior for other users. Gridlight's architecture eliminates this risk by design.

The contamination risk in cloud AI

not supported: Your queries processed on shared multi-tenant infrastructure where model fine-tuning may incorporate usage patterns
not supported: Proprietary terminology, domain knowledge, and process context potentially surfaceable to other users through model responses
not supported: Vendor data retention policies may preserve query logs even when contractually prohibited from training — creating residual exposure

How Gridlight eliminates this risk

supported: Models run locally on your hardware. Inference does not occur on shared infrastructure — at any layer.
supported: Model weights are static during inference. Gridlight never fine-tunes models on query data without explicit configuration and approval.
supported: No query data ever transmitted to Gridlight's systems. The vendor has zero access to inference content.

Data lineage

Every inference event. Full lineage. No gaps.

Gridlight's audit system captures a complete lineage record for every inference event — from query origin to model response, including context sources retrieved, policy decisions applied, and the hardware and model version used. This record is immutable, tamper-evident, and stored entirely in your environment.

Data architecture review

Ready to review the data architecture in detail?

Our data architecture team will walk through your specific pipeline, data classification requirements, and compliance constraints to design the right deployment for your environment.

Request a data architecture review →See compliance coverage