Private LLM
Dedicated model runtime per customer with policy-driven controls and no training on customer data.
Private Model Runtime
Dedicated, model-agnostic LLM runtimes deployed inside your security boundary.
Open-weight Enterprise Models
High-quality open-weight models deployed privately for enterprise workloads.
No training on customer data
Fully on-prem or isolated VPC
Policy-approved model selection
Examples include open-weight enterprise-class models (e.g., Llama-family, Mistral-family).
Low-latency Ops Models
Smaller, optimized models for high-throughput operational tasks.
KPI summaries
Excel reasoning
Fast responses at lower cost
Long-context Document Models
Models optimized for large documents and retrieval-augmented generation.
Contracts & policies
Financial filings
Deep document Q&A
Model-Agnostic by Design
The platform is intentionally model-agnostic. Model choice is driven by customer policy, workload, and compliance—not vendor lock-in.
Private by design
Model choices
Open-weight enterprise model, compact low-latency model, and long-context model options.
Public AI vs Private LLM
Training on customer data
Often unclear by default
Disabled by design
Retention
Provider policy
Your policy only
Isolation
Multi-tenant
Dedicated runtime boundary
Audit logs
Limited
Exportable full lineage
Policy controls
Basic
Customizable guardrail suite
Tool permissions
Broad tool scopes
Allow/deny by team and action
Model runtime ownership
Provider-owned
Customer-owned deployment options
Model Runtime Options
CPU Inference (low scale)
Latency: 620ms
Throughput: 22 req/min
Runtime utilization
Single GPU
Latency: 240ms
Throughput: 95 req/min
Runtime utilization
Multi-GPU / high throughput
Latency: 120ms
Throughput: 280 req/min
Runtime utilization
Guardrails & Controls
What We Do — and What We Don't
What we do
Deploy a dedicated model runtime per customer
Use customer data only for retrieval and inference
Enforce policy controls and audit logging
Support on-prem and private cloud environments
What we don't do
Train on customer data
Retain data outside customer policy
Share data across tenants
Send prompts to public endpoints in private mode
For demos and pilots, we typically deploy an open-weight enterprise model in an isolated runtime. Final model selection is aligned to customer policy and workload.
Where the Private LLM Fits
Ops Ai bots mock output preview
Interactive screenshot placeholder with KPI summaries, citations, and action queue.
• Weekly KPI prep in minutes
• Anomaly triage with citations
• Action items auto-tracked
Example task
Generate a weekly KPI narrative from ERP exports and inbox status updates.
Why private LLM matters
• Operational metrics stay inside a dedicated customer runtime.
• Policies can enforce team-level access and retention windows.
Outcome
• Leadership receives a citation-backed operating brief in minutes.
What we don't do (Private LLM)
- • We do not train foundation models on customer prompts or files.
- • We do not retain prompts externally beyond your configured policy.
- • We do not route private-mode requests to public model endpoints.
- • We enforce policy-driven retention and deletion windows.