How can my business own and control its own AI data?

Is a Privately Controlled AI-Knowledge Base Right for Your Business?

We say yes!…

New AI tools and platforms emerging almost weekly: AI agents, AI chat engines, AI knowledge bases, AI meeting notes, AI automated business sales development agents… the list goes on and on.

As you encounter new tools these present a common challenge: each requires you to feed them your valuable business data separately. This potentially creates a cycle of repeatedly uploading, formatting, and managing your information across multiple platforms – a time-consuming process that can leave your data scattered across various services, and complicated to maintain.

Faced with these difficulties it then becomes harder to consider new AI services because each one represents a large commitment. But there’s another way, instead of thinking of each AI service as stand-alone, you can build your own knowledge base using all your content, and then plug in new AI services on top of it– at will; all while controlling your data and keeping it up to your security standards.

Breaking Free from Platform Lock-in

Building your own knowledge base is transformative. Instead of repeatedly uploading your data to each new AI service, you maintain a single, organized collection of your business information. This approach offers several immediate benefits:

The Practical Reality of Data Ownership

Many businesses assume creating their own knowledge base is overwhelmingly complex or expensive. The reality is more encouraging. With modern RAG (Retrieval-Augmented Generation) systems, you can start small and grow systematically. The process is similar to organizing a digital library – one that any AI tool can readily access and understand.

What makes this approach particularly valuable is its scalability. You can begin with a focused set of information, perhaps your product documentation or customer service guides, and expand as needed. The key is that you’re building an asset that grows in value over time, rather than repeatedly investing in temporary solutions.

Understanding RAG: Your Business’s AI Foundation

RAG systems act as a bridge between your business knowledge and AI applications. Think of RAG as creating an AI-friendly index of your information. When someone asks a question, the system:

1. Retrieves relevant information from your knowledge base
2. Provides this context to the AI model
3. Generates accurate, business-specific responses

This means your AI applications can deliver responses that reflect your exact products, services, and procedures – while maintaining the natural conversation style of modern AI.

When you use a 3rd party AI application, and you are “training their agent” with data you provide, you are basically giving them data to make a RAG “for you”. But if you have this information already, you can just ask your newest “shiny-AI-application” to pull data from your existing knowledge base instead.

Is a Custom AI Knowledge Base Right for Your Business?

We generally recommend that businesses under $1M stay away from building a RAG just yet. Exceptions exist of course, especially if you are heavily focused on data and the value that your data brings– or some kind of edge that you are getting by pushing your data out and connecting it to lots of AI systems.

Businesses that are at $10M or higher should strongly consider having their own knowledge system, and any over $100M either already are working on this, or I believe they will be in the near future.

Eventually having a RAG will be as ubiquitous as having a Website, an imperative. –Sebastian Chedal, 2025

Real-World Implementation and Investment

These benefits aren’t just theoretical – we’ve seen them play out in practice. Based on our experience, here’s what you should consider:

The primary investment isn’t usually in technology – it’s in organizing your data effectively. For businesses with well-structured information, implementation can be straightforward. Those starting from scattered or unorganized data will need to factor in additional preparation time.

Full-service solutions typically start around a thousand dollars, with ongoing costs often comparable to standard business software subscriptions (~$100+ a month). If you have a lot of data that needs work before it can be integrated into the RAG system, this is usually where all of the time goes, so make sure you have a trusted-vendor who can help you organize and structure your data to create your RAG.

Key Implementation Decisions

When you select a partner to help you implement your knowledge base, make sure you find someone who can help you address these critical decisions:

Hosting Options

Cloud-hosted for easier maintenance: Cloud hosting offloads the technical maintenance to established providers, making it ideal for organizations that want to focus on using their knowledge base rather than maintaining it. You’ll benefit from automatic updates, scalable resources, and professional security management. While this option often has higher monthly costs, it requires less technical expertise and can be implemented more quickly.

Self-hosted for maximum control: Self-hosting gives you complete control over your data and infrastructure. This approach works well for organizations with existing IT infrastructure and specific compliance requirements, like HIPAA. You’ll manage your own servers, updates, and maintenance, but gain the ability to customize every aspect of your system. This option typically requires more technical expertise but can be more cost-effective in the long run for larger implementations.

Hybrid approaches for different types of data A hybrid approach lets you keep sensitive data on-premises while leveraging cloud services for public-facing content. This flexibility helps organizations balance security, compliance, and ease of use. You might, for example, keep customer data on local servers while using cloud services for processing public documentation and marketing materials.

Platform Choice

Microsoft / Google / AWS ecosystems If you are deeply embedded in one of these all-in systems, adding your knowledge base into the ecosystem you are already using can make a lot of sense. The pricing and eco system setups though might be too narrow focused if you plan on using a wide array of tools and the billing structures can become really complicated with lots of “pay as you go” noodles to detangle in your dashboard.

Independent solutions There are a lot of different eco systems out there for your RAG once you leave the big names. From totally open-source to 1-click hosted options. Which choices you want to make here will be influenced by your business size, your technical aptitude, whether you want it to be hosted or self managed and how you want your data to be continually updated.

Security Requirements

Data privacy needs Consider both your internal policies and external regulations. This includes data encryption methods, storage locations, and access patterns. You’ll need to evaluate how data is transmitted, stored, and processed, ensuring appropriate protection at each stage. This might involve implementing end-to-end encryption, securing API endpoints, and establishing data retention policies.

Regulatory compliance Different industries and regions have specific requirements for data handling. Healthcare organizations must consider HIPAA compliance, financial institutions need to address SOC 2 requirements, and companies handling European data must ensure GDPR compliance. Your implementation must include appropriate documentation, audit trails, and compliance reporting capabilities.

Access control requirements Establish who can access different parts of your knowledge base and how that access is managed. This involves creating role-based access controls, implementing authentication systems, and monitoring usage patterns. Consider both internal users (employees, departments) and external users (customers, partners), ensuring each group has appropriate access levels while maintaining security.

A Methodical Approach to Getting Started

If this all sounds great… You want to build your own RAG, now what? Here is the process we recommend you take:

1. Define Your Use Case

We recommend starting with a specific goal, such as:

• Enhancing customer service through AI-powered support
• Creating an intelligent internal knowledge search
• Developing personalized product recommendations
• Creating an AI identical twin
• Creating an AI bot that will do business development for you (write emails, connect on linkedin)

Having a clear first-goal will not only give you focus on what data you need to start collecting in your knowledge base, but it will also give you a clear goal that can show value and start generating cost savings or new income.

2. Map Your Data Landscape

• Identify internal vs. external information sources
• Document all data sources (documents, databases, websites, etc.)
• Develop a systematic categorization approach

Begin your data mapping process by taking inventory of both your internal resources (like employee handbooks, process documents, and product specifications) and external content (such as marketing materials, client communications, and public documentation).

Document each source systematically, whether it’s stored in databases, shared drives, content management systems, or scattered across various platforms – this documentation becomes your roadmap for implementation.

With your sources identified, develop a clear categorization system that makes sense for your business; for example, you might organize content by department, information type, or user access level, ensuring that your knowledge base will be both comprehensive and easily navigable when implemented.

3. Assess Data Preparation Needs

• Evaluate current data organization
• Consider AI-assisted bulk processing options
• Identify content gaps that need filling

Before implementation, take a close look at how your existing data is structured and formatted – you may find that some content is well-organized while other information needs significant cleanup or reformatting to be useful in an AI system.

A critical part of your data preparation strategy will be establishing reliable processes for extracting data from your various sources, transforming it into a consistent format, and loading it into your knowledge base. This ongoing process, known as ETL, needs to be planned carefully as it ensures your AI system always has access to accurate, up-to-date information. While the technical details can be handled by your implementation team, you’ll want to ensure your planning accounts for how often data needs to be updated, what resources will be required, and who will be responsible for maintaining these processes.

Ironically (or maybe not!) AI can be leveraged help here to streamline this process by automatically categorizing documents, extracting key information, and converting various file formats into a consistent structure, saving considerable time in preparation.

During this assessment, you’ll likely discover gaps in your documentation where tribal knowledge or undocumented processes need to be captured and added to your knowledge base to ensure comprehensive coverage. If this data is essential, you may need to add additional steps to create any missing data.

Filling data gaps could be something AI can do for you, for example by converting transcripts into text files. Or it could involve hiring someone to literally create this content from zero… if this is the case, this will certainly be the hardest part of the process but afterwards you will be rewarded with data that can be used for years to generate value and grow your business.

(If the knowledge is only in your head, you want it documented anyways if you are serious about growing the business, and leaving behind a legacy!)

Meta data

As you get your data ready, you will want to also consider your meta tags. Here are the main properties you will most likely want tag your data sources with:

Bonus tip:  It is a great idea to always set a checksum hash on each piece of data you load into the RAG so you can easily later check if the data has been modified and when.

4. Load Data & Configure Your RAG System

• Set up all the data imports
• Plan update frequencies
• Establish maintenance procedures
• Implement quality monitoring

A successful RAG system does require thoughtful planning for ongoing operations.

You should start by establishing regular update schedules that align with how frequently your business information changes – this might mean daily updates for dynamic content like product information, while other content may never need updating or only needs quarterly reviews.

Create clear maintenance procedures that define who’s responsible for updates, how changes are approved, and how new information gets incorporated into the system. It is important to think about this upfront and to document it since you want your data to remain usable, useful and strong as time passes and your knowledge base grows.

5. Plug into your AI Applications!

At this point you are ready to plug your RAG into various applications. You will now also have a system that can be maintained and updated over time with all your new knowledge and data.

If you started with an objective AI application, this is where that project takes over and integrates into your RAG, usually through their API.

If you are building your own internal AI solutions, you can add some very quick tests to ensure it is working by asking your data questions related to what it knows and getting back answers that prove it knows your data and how to access it.

If you want to make the data retrieval even more sophisticated, you can also rank the data it gets back –but that is going much deeper and is a subject for another time. 😘

Popular RAG Solutions Compared

Okay so next up: Which platform do you actually use for the RAG? Well, like with many things right now, there are A LOT of choices!

Below is a table we’ve prepared that reviews some of the more popular and upcoming options we are aware of, you can click to review the different company pages and explore some of their materials. It is often easier though to just look at a demo or have someone walk you through the basics.

Pricing is its own puzzle since everyone has a different method of generating your costs. Thankfully most of these are quite affordable but of course over time you may want to use your RAG for many services, so it is still important to consider how the costs scale with expanded use.

On the other hand: If you keep all the sources alive that feed into the RAG, switching RAG services later can be easy. What could get complicated of course is the number of integrations you hook up into your knowledge base, so spending a little time here upfront is worth your time.

If you just want to create a knowledge base as a trial, to see how easy it can be and what it can do as a sand box, my current recommendation is Pinecone.io. With pinecone you can set up a small knowledge base for free, and it has a ton of integrations out of the box, and is really easy to use. Once you get a feeling for how it works you can then rebuild in another RAG if deemed necessary without overly investing.

For the vast majority of small and many medium sized businesses, the costs and performance of Pinecone.io could be more than enough for you and your needs. It’s extensive integration options also mean it is very flexible.

*List last updated on February 2025

Moving Forward With Confidence

While implementing an AI knowledge base requires careful planning, we’ve found that breaking it down into manageable steps helps organizations succeed. The key is starting with clear objectives and working with experienced partners who understand data, the tech and your business needs.

Our team specializes in guiding businesses through RAG implementation, focusing on practical solutions that deliver real value. We’d be happy to explore how a custom AI knowledge base could benefit your specific situation.

Have questions about implementing RAG in your organization? Feel free to reach out by leaving a comment or through the contact link below.

Sebastian Chedal brings over 27 years of experience helping businesses implement practical technology solutions. As a principal founder at Fountain City, he aims to make complex technical concepts accessible to business leaders.