Solutions / AI & Agents
AI systems still need a controlled data layer.
Prepare, govern and route the data feeding models, retrieval systems and agents without building a separate ingestion estate for every AI workload.
Architecture
The model is only one part of the architecture.
What feeds a model, a retrieval index, or an agent still has to be collected, shaped and governed — an AI workload doesn't remove that requirement, it adds another consumer of it.
Data flow
Shape data before the model sees it.
Illustrative architecture
Enterprise sources
- Apps
- Databases
- Documents
- Events
LYFTDATA
Data control layer
- Collect
- Transform
- Route
- Govern
- Observe
Filter, normalize, enrich and govern once — before any consumer sees it.
Shaped
Model-ready data
Governed
Retrieval index
Full fidelity
Storage & analytics
Via MCP
Agent systems
Enterprise sources converge into one LyftData control layer that filters, normalizes, enriches and governs the data. Governed output flows to model-ready data, a retrieval index, storage, analytics, and agent systems.
Filtering
reduce what reaches a model or index to what's actually relevant.
Normalization
consistent shape across sources, so downstream consumers don't each solve it themselves.
Enrichment
add context before data reaches a workload, not after.
Sensitive-data handling
mask or redact before data reaches a model, retrieval index, or agent.
Two planes, not one
Governing data for AI is not the same thing as agents operating LyftData.
This page is the first. The second lives on MCP & Agents.
Data plane
LyftData → AI workload
Governed data flows to the model, retrieval index or analytics system that consumes it. This page.
Control interface
Agent ↔ MCP ↔ LyftData
An agent understands and operates the control layer itself, through MCP. See MCP & Agents →
LyftData is the data and control layer underneath AI and agent workloads — not the model, and not a replacement for the tools built on top of it.
Why LyftData
Independent
no single model provider or AI workload owns the data architecture feeding it.
Governed
sensitive-data handling and routing apply before data reaches a model or index — not after.
Reusable
the same governed data layer feeds multiple consumers instead of a separate ingestion estate per workload.
Give AI workloads a controlled data layer.
Start with LyftData, or talk through your AI data architecture with the team.