Model lifecycle
Promote from sandbox to staging to production with signed model cards and version pinning.
Enterprise AI
AI Labs
Private experimentation for models, RAG, and governed copilots—with a clear path from notebook to production that security and legal can approve.
$ dmx labs deploy --env staging
✓ model card validated
✓ policy gates passed
→ rag corpus indexed 2.4M chunks
$ _At a glance
Everything below ships as a product: roadmap, SLAs, and named releases—not a one-off project handoff.
- Tenant-isolated inference, vectors, and model artifacts
- Benchmarks, eval harnesses, and red-team gates each release
- Salesforce, ServiceNow, and custom API integrations
99.95%
Uptime target tier
SOC 2
Control alignment
24/7
Engineering support
Platform capabilities
Deep enough for enterprise workflows, opinionated enough to go live without a year of customization science projects.
RAG & knowledge
Connect approved corpora, chunking strategies, and retrieval policies your compliance team can audit.
Safety & evaluation
Automated eval suites, human review queues, and blocked-topic policies before any user sees output.
APIs & embeddings
REST and event hooks so LOB apps consume completions and embeddings without shadow integrations.
How rollout works
A repeatable delivery model shared across our product suite—so procurement and IT see a familiar pattern every time.
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1
Discovery
We map use cases, data classes, and risk appetite—then agree on success metrics and guardrails.
-
2
Pilot
A bounded environment with real traffic slices, full logging, and a rollback path if metrics slip.
-
3
Scale
Hardened paths to production SLAs, cost controls, and continuous eval as models and documents change.
Outcomes that matter
Built for enterprises that cannot afford “experimental” AI in customer-facing or regulated workflows.
- Fewer escalations to legal and security because policy is encoded in the platform—not in email threads.
- Faster time-to-value when every team uses the same governed APIs instead of one-off scripts.
- Clear ownership: ML, security, and product share dashboards—not conflicting spreadsheets.
Who it is for
ML & platform leads
You need reproducible pipelines, not one-off notebooks, and a story the board understands.
Risk & compliance
You need retention, access, and audit evidence without blocking every experiment.
Ready to scope AI Labs?
Share your timelines and constraints—we’ll respond with integration assumptions, a pilot cut, and the right product + engineering contacts.