Approach
Secure language model
A private LLM (Large Language Model) is essentially a language model deployed in a secure environment—either on-premises or in a private cloud—so that an organization can maintain full control over its data and operations.
Secure language model
Data privacy & security
Sensitive data never leaves the organization’s perimeter, reducing risks of data leakage.
Customization
Models can be fine-tuned on internal knowledge bases for domain-specific accuracy.
Compliance
Easier to meet regulations like GDPR, HIPAA, or SOC 2 because data stays within controlled infrastructure.
Integration
Private LLMs can be embedded into existing workflows and systems without exposing data to external vendors.
How we work
Valo’s method eliminates multiple ways of doing things.
Makes metrics meaningful because results can be measured consistently against the same processes and methods.
1
Define objectives and use cases
Identify why you need a private LLM, specify tasks and decide on performance metrics.
2
Choose the right base model
Select an open-source LLM (e.g., LLaMA, Falcon, Mistral) or licensed enterprise model and verify hardware compatibility.
3
Set up infrastructure
Deployment Options: On-premises servers, private cloud (VPC), or hybrid. Iplement network segmentation, IAM roles, and encryption for security.
4
Collect and preprocess data
Gather domain-specific datasets (internal documents, chat logs, manuals). Clean and tokenize data; ensure compliance with GDPR, HIPAA, or SOC 2.
5
Train or fine-tune the model
Apply instruction tuning and preference tuning for better conversational quality.
6
Implement security and compliance
Enforce access controls, audit trails, and encryption. Regularly validate compliance with industry standards.
7
Evaluate and deploy
Monitor latency, throughput, and accuracy. Deploy via orchestration tools for scalability and integrate with internal apps (CRM, ERP, knowledge bases).
8
Continuous monitoring and governance
Track usage, detect anomalies, and update security patches.
Why it matters
The opportunity
Secure language model is the privacy to an AI implementation
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Value in motion
AI is only valuable if it is secure, controlled, and aligned with your business context.
Questions?
FAQs
Who is Valo for?
Valo is designed for growth-minded business owners, founders, and CEOs—typically of privately held companies—who want clarity on their business value and a structured path to improve it. We also partner with advisors and institutions supporting these owners.
Is this only for business owners planning to sell?
No. While our work supports successful exits, many clients engage us years before any sale—or without any intention to sell at all. Increasing business value improves optionality, resilience, and decision-making regardless of the path ahead.
What risks do you typically focus on?
We commonly address risks such as owner dependency, poor financial controls, customer concentration, weak succession planning, and undocumented institutional knowledge—factors that often suppress value without owners realizing it.
What does the engagement actually look like?
We start with assessments and diagnostics, identify priority risks and value opportunities, and then guide execution using a blend of AI tools, structured frameworks, and expert human insight.
How does Valo use AI?
AI supports our work by accelerating analysis, surfacing insights, and enabling smarter decision-making. This includes AI agents, secure language models, and intelligent tools that help owners understand risk, value drivers, and opportunities faster.
Is my data secure?
Yes. Data security and confidentiality are foundational to our approach. We use secure, controlled environments and never train public models on client-specific data.
How do I know if Valo is right for my business?
If you want clarity on your business value, reduced risk, and more control over future outcomes, Valo is likely a strong fit. A short conversation is usually enough to determine alignment.