AI launchpad
Ship agent-ready data pipelines in days, not quarters.
LyftData gives data engineers a governed AI delivery layer: prepare model-ready records, run inference in-flow, and operate the full system with MCP-enabled agents and release guardrails.
Model-ready data
Prepare consistent datasets faster for training, evaluation, and indexing pipelines.
Inference in-flow
Run LLM, embedding, and anomaly workloads directly in your workflow runtime.
Versioned releases
Preview diffs, deploy safely, and roll back quickly when assumptions change.
Why now
AI pipeline complexity is compounding fast
Model churn is constant
Prompts, models, and embedding strategies change often. Teams need replayable pipelines, not one-off scripts.
Governance pressure is rising
Sensitive data and lineage scrutiny now apply to AI pipelines. Controls must be built in from step one.
Cost mistakes scale fast
Inference and indexing spend can balloon quickly when noisy, ungoverned data reaches expensive systems.
For data engineers
What you get on day one
Replace brittle AI script chains with versioned, governed workflows that can be reasoned about, tested, and rolled back.
Governed data prep
Extract, parse, normalize, dedupe, and protect sensitive fields before data leaves its boundary.
Inference with controls
Run LLM completions, embeddings, and anomaly scoring with explicit rate limits, concurrency, and timeouts.
Repeatable releases
Preview diffs, deploy safely, and roll back. Use the same workflow artifact for training, evals, and replay.
Blueprints
Concrete recipes you can ship
Start with one flow: source → govern → store. Add inference, embeddings, and additional routes without rewriting ingestion.
RAG indexing pipeline
Chunk → Embed → Index → Archive
Re-index quickly when prompts, models, or retrieval strategy change.
Open blueprint →
Training dataset factory
Extract → Normalize → Govern → Partition
Ship model-ready datasets without maintaining environment-specific script forks.
Open blueprint →
Evaluation harness
Slice → Infer → Score → Compare versions
Track quality drift and release confidence with reproducible workflow artifacts.
Open blueprint →
Streaming enrichment
Classify → Tag → Route → Retain
Send high-signal subsets to premium destinations while preserving full-fidelity archives.
Open blueprint →
Anomaly detect and route
Score → Flag → Fan out
Escalate anomalies to investigation paths and keep long-term evidence in low-cost retention.
Open blueprint →
MCP
Operate LyftData with your favorite agent
LyftData exposes an MCP server so Claude, Codex, and other agents can monitor, explain, and execute approved operations through a consistent tool interface. Treat it as an AI operations surface over the control plane.
Try asking your agent
- Trace this payload through the workflow and summarize output shape changes.
- What changed since the last deploy? Show the plan diff and blast radius.
- Why did this job slow down? Correlate throughput and errors with the last release.
- Suggest a cost-reduction filter/route plan and stage it as a proposed change set.
Guardrails that matter in production
- Prefer read-only tools for monitoring, drift detection, and investigation.
- Use "plan then apply": preview diffs and blast radius before changing anything.
- Scope access via role-based tokens so agents only see what they should.
- Keep rollback paths obvious: redeploy known-good workflow versions.
Teams
Built for data, security, and platform teams
Data Engineering
Build dataset factories and RAG pipelines without a sprawl of one-off scripts. Keep data reproducible, replayable, and governed by design.
Security & Compliance
Enforce masking/redaction upstream, keep auditable evidence of what ran, and reduce vendor lock-in by keeping data portable.
Platform & SRE
Operate deterministic pipelines with safe releases: preview diffs, control placement and scaling, and roll back quickly when needed.
Next step
Start with one pipeline, then expand
Build a governed dataset flow first. Then add embeddings or inference as a step, route outputs to the destinations you need, and keep a full-fidelity archive for replay.