Private LLM Training.
Your MSP data is a goldmine — years of tickets, resolutions, documentation, and client context. We help you train private Large Language Models on that data so your AI stays inside your environment, learns your specific workflows, and never shares your clients' information with a third-party cloud provider.
Why Private LLMs Matter
ChatGPT doesn't know
your clients.
General-purpose AI tools like ChatGPT are useful, but they're not trained on your environment. They don't know your clients, your SLAs, your runbooks, or your escalation procedures. Every prompt you send them also carries a data privacy risk — your clients' infrastructure details should never leave your control.
A privately trained LLM changes that equation. It runs on your infrastructure, learns from your data, and stays completely within your security boundary. You get the productivity of AI without the compliance risk.
Public AI Tools
- Client data sent to third-party servers
- Generic answers, not your specific environment
- Compliance risks (HIPAA, data sovereignty)
- No memory of your past tickets or context
Your Private LLM
- Runs entirely on your infrastructure
- Trained on your tickets, docs, and runbooks
- Full data sovereignty — zero third-party exposure
- Gets smarter as your data grows
What You Can Build
AI built for MSP operations.
Intelligent Ticket Handling
Train a model on your historical tickets, resolutions, and runbooks. Your AI assistant can categorise, route, and draft first responses — cutting Tier 1 handle time significantly.
Private Knowledge Base
Turn your IT Glue documentation, SOPs, and internal wikis into a queryable AI model. Technicians get instant, accurate answers without searching through 500 articles.
MSP Chatbot for Clients
Deploy a branded AI assistant trained on your approved FAQ and troubleshooting guides. Clients get instant self-service answers — reducing inbound ticket volume.
Automated Report Generation
Train a model to generate client-facing monthly reports, executive summaries, and QBR content from your RMM and PSA data automatically.
Alert Triage Assist
Fine-tune a model on your alert history and resolution patterns. It learns what's noise and what's critical — and can suggest remediation steps before a technician touches the ticket.
Compliance Documentation
Generate HIPAA, SOC 2, or custom compliance reports from your environment data. Trained on your specific policies so every output reflects your actual standards.
The Process
How we train your model.
Data Audit & Preparation
We review your existing data sources — tickets, documentation, runbooks, reports — and prepare a clean, structured training dataset. Sensitive data is identified and handled appropriately before it ever reaches a model.
Model Selection
We select the right open-source base model for your use case (Llama, Mistral, Phi, or others) based on your performance requirements, data volume, and infrastructure. No dependency on OpenAI or third-party cloud APIs — your model runs on your infrastructure.
Fine-Tuning & Training
Using your prepared dataset, we fine-tune the model to understand your specific domain — your tools, your terminology, your clients, and your workflows. Training runs on isolated hardware with no data leaving your environment.
Deployment & Integration
We deploy the trained model to your preferred environment — on-premises, private cloud, or VPC — and integrate it with your existing tools via API. This includes ConnectWise, Autotask, IT Glue, or any PSA/RMM with an accessible API.
Testing & Validation
We run structured validation tests to measure accuracy, relevance, and hallucination rates against your real-world scenarios. You review outputs before the model goes live.
Ongoing Refinement
As your data grows and your needs evolve, we retrain and update the model. Continuous improvement is built into the engagement — your AI gets smarter the longer we work together.
Our Security Commitment
Your data never
leaves your environment.
Every training engagement is architected so your data stays in your control from start to finish. We use open-source models that can be fully self-hosted — no API keys, no cloud subscriptions, no third-party data processing agreements required.
Start the ConversationOn-Premises or Private Cloud
Models deployed on hardware you own or a private VPC. No shared infrastructure.
Open-Source Base Models
Llama, Mistral, Phi, and other openly licensed models — no vendor lock-in, no hidden data usage.
Data Anonymisation
Client PII and sensitive identifiers are scrubbed from training data before any model ingests it.