Private & Secure AI for Business

Do you have concerns about using AI in your business?
If so, you're not alone.
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39%
of decision makers said privacy stopped them from using AI
69%
have experienced a data privacy issue
60%
of firms don't trust AI platforms
14%
Of small businesses don't use AI due to costs
75%
Of small businesses want to use AI

Sources: Forrester/KPMG/Deloitte 2024-2025

SELF Business addresses these concerns by being:

On-Premises
Hosting
With on-premises hosting, your organisation maintains full control over your data, safeguarding sensitive data whilst providing access to external resources if desired.
Security
& Compliance
 SELF ensures that your data is protected by the highest security and encryption standards available, while ensuring compliance with industry regulations.
Customisation
& Flexibility
SELF is highly customisable and can be tailored to meet specific needs. This allows you to adapt the AI to your unique operational requirements.
Integration
Capabilities
SELF ensures seamless integration with your existing IT infastructure and third-party applications, enhancing the overall efficiency and effectiveness of your operations.
Memory
& Document Upload
Upload documents to be used as reference, without worrying about proprietary company data being exposed.
Personalisation
& User Profiles
Personalisation based on custom forms for stakeholders, allowing for generation of custom user profiles. Read about some case studies here.
Scalability
& Performance
SELF can grow alongside your business requirements, ensuring that the system can handle increasing data volumes and usage demands efficiently.
Ease of
Configuration
SELF takes care of the technical aspects, meaning you don't need advanced technical skills to have your own AI platform. This sets SELF apart from other providers.
Support
& Maintenance
Our dedicated support team ensures smooth deployment, ongoing maintenance and maximum productivity.
Streamlined
Onboarding
Three phases with an emphasis on guidance, maximising user value, target objectives and responsiveness.
Future
Proofing
 SELF continuously innovates by adding new features and capabilities, providing a solution that not only meets current needs but also anticipates future challenges.
Cloud
Offerings
SELF Business initially offers on-premise solutions, but will soon expand the offering through select partnerships with decentralised AI compute/ storage providers and next gen encryption solutions.

Traditional AI

Cross
Data collection
Cross
Limited customisation
Cross
Regulatory risk factor
Cross
Relies on public security standards
Cross
Questions about who got access
Cross
Limited / no personality
Cross
External / no branding
Cross
Expertise to customise the AI
Cross
Expertise to integrate with infra.
Cross
Technical skills required

SELF - Private AI

Built for privacy
Highly customisable
Tailored to compliance needs
Tailored to security needs
No corporate surveilance
Your company personality
Your branding
Customisation handled by SELF
Integration handled by SELF
No need for technical expertise

How Others Are Using Private AI
(reporting 250% ROI)

Healthcare:
Philips
Philips Healtcare uses self-hosted AI solutions to enhance diagnostic imaging and patient monitoring systems. By deploying AI on-premises, Philips can ensure data privacy and integrate AI models directly iwth their medical devices. This approach allows for real-time analytics and personalised treament recommendations while adhering to stringent healthcare regulations.
Manufacturing:
Bosch
Bosch utilises self-hosted AI for predictive maintenance and operational optimisation in their manufacturing plants. By deploying AI on-premises, Bosch can ensure that data from their machinery is processed locally, improving responsiveness and security. This approach also allows for better integration with existing industrial systems.
Retail:
Walmart
Walmart has implemented self-hosted private  AI for inventory management and supply chain optimisation. By using AI locally, Walmart can analyse large volumes of data from its supply chain in real-time, improving accuracy and reducing latency. This setup helps Walmart manage its vast network of stores and warehouses more efficiently.
Finance:
JPMorgan
JPMorgan Chase employs self-hosted AI for various applications including fraud detection, risk management, and trading algorithms. By using in-house AI solutions, they maintain control over sensitive financial data and can customise AI models to meet their specific needs. This setup enhances security and performance for their critical financial operations.
Tech:
VMware
VMware has implemented a private production chatbot named vAQA (VMware's Automated Question Answering Service) for internal use by its employees. VMware sees private AI as a powerful architectural approach that allows organizations to benefit from AI without compromising control over their data, privacy, and compliance.
Insurance:
Liberty Mutual
Liberty Mutual has found success with private AI by implementing AI-informed intelligent choice architectures. These systems assist claims adjusters at Liberty Mutual in triaging incoming calls and resolving inquiries more efficiently.
Example Case Study: Legal Firm
Background
Challenges
Why SELF?
Implementation
Results
Team Member

A mid-sized legal firm with 50 staff and offices in six different UK locations faced growing concerns regarding data privacy when integrating AI into their workflows.

The firm relied heavily on Microsoft 365 for document management, client communications and legal research but was cautious about using AI-powered tools like Co-Pilot due to potential risks of exposing confidential client information.

Legal professionals handle highly sensitive data, including case details, client records, and priveleged communications. Any expsoure of such information to AI systems, whether internally or externally, could lead to compliance violations and loss of client trust.

Given these concerns, the firm sought a solution that would allow them to harness AI efficiencies while ensuring complete control over data privacy.

Additionally, the firm was concerned that competitors were ahead in AI implementation, potentially giving them an edge in efficiency and client service. To stay competitive and future-proof their operations, they aimed to increase their output, with a projected 40% expansion of their client portfolio over the next year.

Team Member

The firm identified several key challenges in adopting AI securely:
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Data Sensitivity: Legal professionals manage highly confidential client data that must remain protected.
‍
Compliance & Regulation: Adherence to UK GDPR and Law Society guidelines was crucial.
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AI Trust Issues: There were concerns about AI models inadvertently learning from or storing sensitive case information.
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Seamless Integration: The firm needed a tool that would work harmoniously with their legal case management systems without disrupting workflows.
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Balancing AI Benefits & Risks: While AI could enhance research and document drafting, privacy risks made the firm hesitant to fully integrate these tools.

Team Member

To address these concerns, the legal firm selected SELF as its self-hosted AI platform for the following reasons:
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Self-Hosted AI Model: A configurable, on-premise, interactive platform with persistent memory and preference management, ensuring full control over AI processing and data security, eliminating reliance on externally accessed AI providers.

Zero-Knowledge Architecture: Ensuring that no external platforms are learning from legal data.

Compute Power Guidance: As the firm lacked the necessary cloud/hardware/software to run a self-hosted AI, SELF provided clear and concise guidance on acquiring the right infrastructure.

Authentic Industry Expertise: SELF was founded by an internationally recognised thought leader and author on privacy and data ethics, providing comfort that the underlying purpose of SELF is aligned with the motivations of the firm.

Team Member

The legal firm implemented SELF in three stages over 20 weeks:

Discovery Phase (Weeks 1-4)
A discovery call identified the firm's AI needs and privacy concerns.
A virtual workshop helped define goals, such as integrating AI into legal research and document review workflows.
Budgeting & Maintenance Plan: The firm allocated a portion of its annual tech budget to ensure a cost-efficient implementation plan.

Configuration Phase (Weeks 5-14)
SELF developed the AI with the firm’s branding, legal terminology, and case-handling methodologies.
The AI was trained on legal documents, enabling it to assist lawyers with case research while maintaining privacy safeguards.
The firm’s IT team worked with the SELF Tech team to set up the necessary compute power required to host the AI in-house.

Integration Phase (Weeks 15-20)
The AI was deployed within the firm’s document management and case research systems.
Staff were trained on how to update AI knowledge with new case law and legal precedents.
The final system was tested and fine-tuned before the full launch.

Team Member

By choosing SELF, the legal firm achieved:
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4x Faster Document Review & Generation: AI-assisted legal research and document drafting reduced workload and improved efficiency across the firm.
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Complete Compliance Confidence: Lawyers could use AI confidently, knowing all interactions remained within data protection regulations and never left the firm’s on-premises environment.

30% Cost Savings: Compared to other AI providers, SELF saved a substantial volume of expenses, allowing funds to be reallocated elsewhere.
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Example Case Study:Conclusion
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For legal firms, data privacy is paramount. By implementing SELF as a self-hosted AI solution, the law firm successfully harnessed AI’s potential while safeguarding client confidentiality.

As AI adoption in the legal industry grows, such solutions will become increasingly vital to ensuring compliance, trust, and operational efficiency.

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