Data and ML platforms
Private model deployment
Deploy appropriate pretrained language, vision, or predictive models inside the required client or isolated cloud boundary.
Primary buyer: Enterprise technology and data ownersThe real constraint
Teams accumulate scripts and model demos without a dependable path from data change to safe production release.
How we approach it
We design contracts and automation around the smallest platform that supports the real model and team lifecycle.
How we prove it
A clean path from source data to evaluated deployment is reproduced with rollback, cost, access, and ownership tested.
Production system
Data and ML platforms
The pipelines, evaluation, deployment, observability, and governance that make model delivery repeatable.
- 01Data ingestion and quality contracts
- 02Experiment and evaluation workflow
- 03Model registry and deployment automation
- 04Observability, drift, and cost controls
- 05Access, lineage, rollback, and ownership
Typical fit
Model capability aligned with privacy, latency, cost, and operational ownership
FAQ
Questions before a pilot
01Do we need perfect data before starting?
No. Discovery establishes whether representative data exists, what quality gaps matter, and the cheapest evidence needed before a production promise.
02Can Tandemora own the complete implementation?
Yes. Scope can include product interface, models, data, cloud, integrations, observability, deployment, and handover, with specialists added when the system requires them.
03How is pricing determined?
Uncertain work begins with a bounded paid discovery or pilot. Production is priced after evidence clarifies data, integration, quality, operations, and ownership.
Service focus
Private AI and On-Premise Model Deployment
Deploy suitable pretrained AI models in an isolated cloud or client boundary aligned with privacy, latency, cost, access, and support.
- Built for
- Enterprise technology and data owners
- Designed to achieve
- Model capability aligned with privacy, latency, cost, and operational ownership
Related delivery scope
- on-premise LLM deployment
- self-hosted AI consulting
- private generative AI implementation
- enterprise model serving
Need a different system?
Not quite your solution?
Explore nearby systems or bring the outcome, workflow, and constraints that do not fit a predefined category. Tandemora can scope a custom solution around the real operating problem.
Your version will have different constraints.
Bring the workflow, current system, available evidence, and the assumption you trust least. We will identify the smallest useful next step.
Discuss this system