Best custom AI solutions
Being behind major reports like The Mother of All Breaches and RockYou2024, our in-house cybersecurity experts and journalists provide unbiased, real-world testing and in-depth analysis.
We maintain complete transparency by openly sharing our testing methodologies with our audience.
Learn more
Sometimes, commercially available AI solutions are just not enough. Their usage limits, privacy policies, or limited customization may make your business feel like you need more. Luckily, many providers offer more bespoke products. In this article, I look at the best custom AI solutions and explore how they can take full advantage of generative AI.
Let’s start by explaining what exactly custom AI solutions are. They are quite simply AI tools tailored to the needs of a given enterprise. Rather than a pre-built solution, these tools use generative AI to create text, images, code, or video as building blocks for most of the functionality you may need.
That’s why the main thing you need to consider is whether you actually need a custom solution. Many existing SaaS (software-as-a-service) tools fill the major niches of many businesses, but custom solutions let you tailor a single tool to your exact needs, without combining multiple ecosystems.
To help you decide which tool best suits your needs, I tested a range of generative custom AI solutions alongside the Cybernews research team. I go through some of the best tools offering custom solutions, and give you the pros and cons and best use cases for each product.
Best custom AI solutions - shortlist
- ChatGPT – best custom AI assistant
- Claude – best for processing enterprise documents
- Writesonic – best for custom SEO automation
- GitHub Copilot – best custom AI for coding assistance
- Midjourney – best custom AI for image generation
- RunwayML – best custom AI for video generation
Best custom AI solutions compared
Before I break down each tool in detail, here’s a breakdown of what each tool offers in terms of AI solutions
| Tool | Overall rating | Standout features | Starting price | Free or trial version criteria | Best for |
|---|---|---|---|---|---|
| ChatGPT | Admin console with advanced analytics and unlimited access to advanced reasoning models | $25.00/month/user (Business Plan); custom pricing (Enterprise Plan) | Free basic version available (usage limits apply) | Large enterprises needing a scalable, highly secure general-purpose AI assistant | |
| Claude | 500K+ token context window with native GitHub integration | $20.00/user/month (Team plan); custom pricing | Free basic version available (usage limits apply) | Financial, legal, and software engineering enterprises | |
| Writesonic | Custom AI model training perfectly tuned to your specific brand voice | $199.00/month (Standard team/business tier); custom pricing (starts around $830.00/month billed annually) | Free tier available (limited generation credits) | Global marketing teams and large SEO agencies | |
| GitHub Copilot | AI is fine-tuned and indexed entirely on your company's private codebase | $19.00/month/user (Business); $39.00/month/user (Enterprise) | 30-day free trial available (primarily for individual tier) | Enterprise software engineering and DevOps teams | |
| Midjourney | Stealth Mode for private, unreleased IP generation | $120.00/month (Mega); custom (enterprise mode) | No free trial currently available | Creative agencies and enterprise design teams | |
| RunwayML | Custom model training for brand-specific video generation | $35.00/user/month (Pro plan for teams); custom pricing for enterprises | Free basic tier available (limited credits, watermarked) | Broadcast networks, film studios, and large ad agencies |
6 best custom AI solutions – our detailed list
In order to help you decide which custom solution is the best for your company, I have created a detailed breakdown of each tool I tested:
1. ChatGPT – best company-wide AI assistant
| Overall rating: | |
| Standout feature: | Unlimited access to advanced reasoning models and |
| Starting price: | $25.00/month/user (Business Plan) or custom pricing (Enterprise Plan) |
| Best for: | Large enterprises in need of scalable assistant |
ChatGPT offers two options for businesses – an out-of-the-box Business plan and a custom Enterprise plan. I’ll focus on the latter, which offers full customizability, allowing you to tailor it to your business's needs, while also giving your team access to unlimited access to OpenAI’s newest advanced reasoning and multimodal AI models.
The first thing I really liked when testing ChatGPT’s enterprise solutions was its centralized admin console. This offered advanced analytics and gave me access to SSO and permission control. This means that, for example, if you’re using ChatGPT as a general-purpose AI assistant, you can have the finance department have access to sensitive data that other departments aren’t allowed to access.
ChatGPT Enterprise can be integrated with your company’s systems via an API connector. This gives it access to your internal files. Note that ChatGPT isn’t trained by this data at any point, so you don’t have to worry about that being a source of data leaks. I also appreciated the fact that ChatGPT offers data residency options for jurisdictions that require storage in specific locations.
You can also use a wide collection of MCP (model context protocol) connectors to link to other services in your ecosystem. Using this feature, I could easily pull data from other apps like Slack or create JIRA tickets without having to leave the assistant window.
ChatGPT Enterprise also helps teams organize their knowledge and information. While employees can use standalone chats, they can also easily collaborate by creating Projects. These are workspaces for teams, where teams can place specific documents and information. For example, a marketing team may upload its strategy PDFs, brand book, and past metrics to help the assistant with a new campaign. I was also impressed with ChatGPT’s Agent Mode, which let the assistant perform multi-step tasks autonomously, using connectors to limit the need for your interaction.
That said, ChatGPT Enterprise isn’t perfect. Its pricing model is opaque and is reported to require at least 150 users, which is a high entry point. With it being an AI assistant, its effectiveness will depend on your employees’ prompting skills – bad prompts are unlikely to increase efficiency.
2. Claude – best for nuanced writing and analyzing massive documents
| Overall rating: | |
| Standout feature: | Huge context window and good responses |
| Starting price: | $20.00/month/user (Team plan); custom pricing (Enterprise plan) |
| Best for: | Long-form content, coding, and document analysis |
Claude is the top competitor to ChatGPT, offering a generative AI model created by some of the original OpenAI team. While it doesn’t feature ChatGPT’s image and video generation capabilities, its larger context window, coding, and long-form writing capabilities make it a worthy competitor as a reliable AI assistant.
The first thing that stood out to me when using Claude was its ability to produce a casual, conversational tone that didn’t feel as robotic or repetitive as competitors like ChatGPT or Gemini. That said, the biggest feature was its context window, which is the size of documents it could process and produce, allowing me to summarize even extremely long articles (over 500,000 tokens – around 375,000 words).
The large context window doesn’t just allow Claude to produce long summaries, but also helps reduce hallucinations. Of course, just like ChatGPT, Claude offers a robust privacy and governance policy, ensuring your data isn’t used to improve its models.
Claude can be integrated and customized thanks to its robust API. I know some developers actually view it as easier to work with than ChatGPT’s API, given its ability to process massive amounts of data. This makes Claude a perfect choice for building a robust AI assistant for data-heavy departments. Claude’s coding capabilities are also some of the most popular among developers, making it an excellent choice for a coding assistant.
The big drawback when compared to ChatGPT is that Claude cannot generate videos or images, so if you’re looking for an assistant that can help you with every aspect of your business, you might want to look elsewhere.
3. Writesonic Enterprise – best for SEO and content writing
| Overall rating: | |
| Standout feature: | Custom SEO AI model matching your tone of voice |
| Starting price: | $199.00/month (Standard team/business tier); custom pricing (starts around $830.00/month billed annually) |
| Best for: | Global marketing teams and large SEO agencies |
Writesonic Enterprise is essentially a robust, AI-driven content marketing machine. Its enterprise version gives you access to tools that let you generate content consistent with your brand’s tone of voice and strategy.
Writesonic is quite impressive. Although it’s not going to replace a professional writer, it can help companies looking ot create a comprehensive SEO strategy, and quickly generate assets that will help it rank in both traditional and AI search engines with the help of multiple templates combined with generative AI capabilities
Writesonic’s enterprise is robust. It reminds me of a CMS like WordPress. It’s not just a content creation suite but rather a fully fledged SEO strategy hub, where you can look for keyword opportunities and immediately generate appropriate content. I was really impressed by both its robust functionality and how well it generated written content. Unfortunately, it’s very dense and can be overwhelming to new users.
However, the biggest enterprise feature is its API integration. This allows you to quickly build a workflow that puts Writesonic right in the middle of your work. You can then use it to automatically generate SEO-optimized articles and place them as drafts in your WordPress, create eCommerce listings in bulk, or draft a highly personalized cold email to a generated lead.
4. GitHub Copilot Enterprise – best for secure, organization-specific code generation
| Overall rating: | |
| Standout feature: | AI fine-tuned and indexed entirely on your company's private codebase |
| Starting price: | $19.00/month/user (Business); $39.00/month/user (Enterprise) |
| Best for: | Enterprise software engineering and DevOps teams |
GitHub Copilot is a programming assistant that can be hooked into your workflow to provide assistance based on your company’s documentation and practices. This provides excellent insights to programmers and makes working with an AI far more efficient.
Copilot’s Enterprise version works on two levels. On the user’s end, it integrates with an IDE (integrated development environment) seamlessly, letting programmers use it to autocomplete and debug on the fly.
On the enterprise-side, Copilot’s admin side really impressed me. In the GitHub web UI, I was able to view pull request summaries with the AI explaining the code differences. This feature is excellent for non-technical stakeholders, as they can understand their programmer’s work, and if they have any questions, ask the tool to explain the codebase.
GitHub Enterprise provides granular control over which repositories the AI can access. This allows you to exclude sensitive code from the software. Of course, the model isn’t allowed to train on your code, meaning your intellectual property stays safe. On the other hand, with multiple teams using multiple code repositories, the permission controls can get messy if you want to ensure detailed access.
Copilot can integrate with multiple databases and tools through MCP and can be routed through custom-hosted LLMs. This means that you don’t have to use GitHub’s default GPT model, instead routing your code through Claude or Gemini. This also further increases security, as it lets you avoid your code passing through third-party servers.
The big con here is quite obvious: with GitHub Copilot, you’re locked into using GitHub’s ecosystem for version control in your enterprise. This limits your customization options, but given its capabilities, the drawback might be worth it.
Pros
- Bases its suggestions on your code
- Excellent privacy
- Seamless IDE integration
Cons
- GitHub ecosystem lock-in
- Permission management can get complicated
5. Midjourney – best image generation custom AI
| Overall rating: | |
| Standout feature: | Stealth Mode for private, unreleased IP generation |
| Starting price: | $120.00/month (Mega); custom (enterprise mode) |
| Best for: | Creative agencies and enterprise design teams |
You may know Midjourney as a Discord-based image generation bot. However, its business and enterprise plans are far more than what you may remember from its initial version. With a robust web interface and now an Enterprise API, Midjourney went from an interesting and fun novelty to a powerful tool for creators.
The most important feature for Midjourney is Stealth Mode. Previously, anything you would generate in Midjourney would end up in a public community feed. This made it very risky to use it for work-related tasks, especially when dealing with new IPs and projects. Now, with Stealth Mode, I could keep my generations private, which makes Midjourney far more usable for work-related projects. The Mega and Enterprise plans give extensive access to Fast GPU mode, ensuring quick and efficient generations.
Midjourney can be used in two ways. It has a very intuitive web interface, which is a big improvement over its old prompting system. I was able to edit my image, point out specific areas I wanted improved, and refine it to my liking. It can also be used through a Discord bot that will deliver images straight to your server.
Unfortunately, no official API for Midjourney exists, although the company is considering introducing an Enterprise API in the near future. There are some workarounds for this using its prompting engine, but they can potentially violate Midjourney’s terms of service.
Despite its drawbacks, Midjourney is still the best image generation tool on the market, offering custom business solutions. It offers unmatched aesthetic quality and produces production-ready images.
6. RunwayML Enterprise – best for secure, collaborative video production pipelines
| Overall rating: | |
| Standout feature: | Custom model training for creating brand-specific videos |
| Starting price: | $35.00/user/month (Pro plan for teams); custom pricing for enterprises |
| Best for: | Film studios, broadcast networks, marketing agencies |
RunwayML is the state-of-the-art enterprise video generator, allowing collaborative video creation and post-production. All of this is achieved while maintaining SOC 2 compliance and enterprise-level billing practices, ensuring safety for even the biggest enterprises.
Runway turns AI models into a familiar interface similar to a typical NLE (non-linear editor) like Final Cut Pro or Adobe Premiere. This allows editors to use generative AI capabilities in a way that they’re familiar with.
Runway also offers API access, which can be very useful for quickly generating localized videos across multiple markets or for simple animations on baseline images. With some video editing experience, I found the NLE interface familiar and easy to use.
If you’re looking for customization, Runway allows you to train it on very specific instructions and presets. This, in turn, ensures that its outputs are consistent with your branding and style. Unfortunately, its generative capabilities aren’t perfect, as they still often require human intervention to ensure consistency, as characters and sets can change between scenes in a way that can produce an uncanny valley effect with viewers.
Generative video is probably the most token-consuming aspect of generative AI. That’s why RunwayML provides the option to set strict credit limits, limiting them per user or per project. Its admin dashboard also allows you to manage access, letting editors, producers, and even clients collaborate and leave comments for each other to ensure the product meets its requirements.
Different types of AI used by businesses
AI in business has become a wide net, including many different solutions. While a few years ago, most businesses used ChatGPT to shorten repetitive tasks, generative AI’s capabilities have now made it a big part of many different business decisions. Here are a few ways in which businesses use AI:
- Predictive and analytical AI. Businesses use AI to analyze data and provide robust forecasts and predictions based on past performance. This can be used in a variety of fields, from sales to video game production.
- Recommendation and personalization AI. AI can also be used in a customer-facing role, analyzing user behavior and suggesting products, content, or actions.
- Generative AI. Many businesses use AI to create text, images, code, or video. Virtually all the tools on our list can generate some form of content.
- Decision and automation. With the rise of agentic AI, an increasing number of businesses now use it to automate and orchestrate workflows, limiting the need for mundane human tasks.
Note that many custom AI solutions often combine these four features. For example, ChatGPT or Claude can perform across all four categories, while Midjourney focuses on only one. Deciding which one is best for your business depends on your specific needs.
Why businesses need custom AI solutions today
In an increasingly competitive world, more and more businesses need custom AI solutions to stay competitive. Having tried out multiple generative AI tools and having used them myself in various corporate environments, I can tell you that there are a few reasons to start using AI.
First off, speed. Companies using AI can churn out highly personalized content at a speed unmatched by humans. While more advanced tasks will still require human input, basic tasks, like cold leads, producing coupon copy, or creating personalized newsletters, are far more efficient with the use of AI.
Another aspect comes from employees themselves. Automating repetitive tasks can quickly increase both your work efficiency and your employee satisfaction. After all, if employees don’t need to fill out large spreadsheets or copy-paste information between knowledge bases, they’ll do their work more quickly and be more satisfied with their day-to-day job.
Finally, with everyone having access to AI tools, a custom solution can make your output stand out. Work faster, generate higher quality content, build better code, answer more tickets, and produce more accurate analyses. AI is no longer an experiment; it’s a core system in marketing, product, support, and engineering.
Benefits of using custom AI solutions
In order to help you decide whether you should use a custom AI solution, I decided to break down the main business-level benefits of generative AI I’ve seen in my test cases and hands-on experience.
Efficiency and cost saving
The most obvious benefit I’ve seen is purely in efficiency. By delegating repetitive tasks like drafting cold emails, summarizing meetings, or providing basic support, I was able to save a tremendous amount of time with the help of AI. This, in turn, let me focus on creative tasks that require a human touch, increasing output and saving costs.
Better customer experiences and personalization
Personalizing emails or support messages can be hard when doing it manually. Luckily, AI can parse massive amounts of data, generating ready-made messaging or contextual briefs. This allows customer-facing teams to provide highly detailed personalization, building relationships in a far more effective way than generic solutions.
Improved decision-making and insights
Making a business decision can be tough. Luckily, you can leverage generative AI to make it slightly easier. AI can provide complex information by summarizing data, flagging anomalies, and simulating scenarios, making your decision easier. At the same time, it can also explain complex technical topics to non-technical teams, making communication between departments easier.
Faster experimentation and innovation
Research and development can be hard, especially for companies without a dedicated team. AI helps in this process by enabling rapid prototyping. For example, if, as a product owner, you want to build a wireframe of a proposed functionality, you won’t need to engage multiple teams to transform your vision into a visual. Instead, AI can build a basic app that can then be used to evaluate the viability of your idea.
Consistency and scalability
A custom AI solution can help maintain your company’s logic, tone, or policies. By providing analyses, creating documentation, and troubleshooting code, AI can ensure everyone’s on the same page and fix errors as they go.
Implementing generative AI in business (step-by-step strategy)
In order to help you to set up your custom AI, I have prepared a step-by-step strategic implementation guide based on research and experience working in large companies.
Step 1. Identify high-impact problems
The first thing you should do is map your exact needs. Ask department and team leads to compile the most common issues and pain points. Look at the ones that are most common, e.g., long content backlogs and developer bottlenecks.
Having worked for several large companies, I found that teams that start by looking at issues, rather than starting by searching for tools, are the ones that create effective implementation.
Step 2. Audit data, systems, and constraints
Once you have a list of use cases you want to solve, you should perform a wide audit of your systems. Map your data structure across CRMs, databases, and documentation platforms. Consider privacy laws and corporate governance to ensure that your solution will be compliant. Check what integration points you have, like webhooks or APIs – these can be very useful for custom AI solutions.
Step 3. Choose your building blocks (tools and architecture)
Once you have your needs and structure map, think about how you use AI. First off, consider that primary use case. After all, using Midjourney for responding to customer complaints won’t work.
Once you know what you need, think about your workflow. Ask yourself a few simple questions: How much automation do you need? Where do you need human intervention? This is where you decide whether off-the-shelf solutions are enough or whether you need a custom-built AI that will suit your team’s needs. You can also choose to combine various tools, for example, giving developers access to GitHub Copilot while giving graphic artists access to Midjourney.
That said, you may need a very specific workflow that involves high levels of automation and orchestration – in those cases, a custom AI integrated through an API may be your best choice. In my experience, I looked at both off-the-shelf and embedded usage and found that the choice depends on the results of the previous two steps. Sometimes, a simple ChatGPT Team plan is enough. Other times, a completely custom solution will change the way you run your business.
Step 4. Run a controlled pilot
Before going all-in, set up a small test run of your solution. Focus on one or two teams that have shown the biggest need for the solution. Establish clear metrics. Look for time savings, quality improvements, or revenue increases. In combination with your team’s feedback, this will help you decide whether the tool is serving its purpose and find common pain points.
Step 5. Govern, scale, and continuously improve
If your pilot is successful, you can start scaling the solutions to more teams and training them as you go. Be sure to establish clear guardrails, grant appropriate access, and set data access limits for the AI to prevent sensitive information from being leaked. These policies should be regularly audited and updated to ensure full security. This isn’t a one-and-done action, but rather an iterative process in which you regularly build on experiences and data.
Common challenges and best practices of generative AI solutions
Generative AI solutions have significant potential, but they also come with drawbacks that can’t be ignored. Here’s what you should look out for when using a custom AI solution:
- Data privacy and security. Since custom AI models often have access to sensitive data, they can leak it. Ensure proper access controls, redaction, and clear governance to mitigate this risk.
- Hallucinations and unreliable outputs. AI isn’t perfect, and the way it is programmed makes it susceptible to making things up. This is why you should set up validation steps and human reviews for high-importance tasks.
- Integration complexity. Integrating a tool into an existing ecosystem can be complicated. This is why you should roll out slowly and on a small scale, to detect bugs before they become critical.
- Change management and adoption. Large organizations can struggle with implementing new tools. Teams can be resistant to change, which is why you should produce clear guidelines and organize training sessions to ensure all employees are familiar and ready to implement the tool.
- Vendor lock-in and long-term costs. Relying on a single provider can leave you exposed to rising usage costs and price hikes. You can mitigate this by creating a modular architecture around the tool and monitoring its usage to ensure it doesn’t exceed a given threshold.
With multiple different AI models on the market, picking the right AI solution can be hard. This is why you should consider the following criteria when making your decision:
- Business goal alignment. Think about how the given solution can help you execute your business goals. Align your choice with the tool that best solves your primary pain points.
- Data and integration fit. Check whether a given tool can be easily integrated into your existing systems, like a CRM, CMS, or code repositories.
- Level of customization. Consider whether you need a fully custom build or if an out-of-the-box implementation of a tool like ChatGPT or Copilot would be good enough for you.
- Security, compliance, and governance. Examine how the provider deals with sensitive data and whether it’s appropriate for the corporate governance requirements in your jurisdiction.
- Ease of use and team skills. Think about whether a given tool is easy enough to use for your team, given both its user experience and your employees’ skills.
- Pricing, usage model, and ROI potential. Consider the price and payment model of a given tool. Some may have a large flat fee, while others may charge based on usage, with different models being more suitable for different uses.
Future trends: how generative AI is evolving and what to expect?
Generative AI is a relatively new technology with a bright future. In the past few years, we have seen a shift from standalone tools to fully agentic systems. Rather than providing a single task, these systems can integrate deeply with your business process, autonomously planning and orchestrating various other tasks.
Another big trend is the creation of more industry-specific models, particularly in fields like healthcare, ecommerce or finance. These industries often have specific needs that can’t be fully met by a broad model.
I believe that these trends will continue. Just as generic solutions in the Web 2.0 era eventually became highly customized to fit specific niches. Soon, existing AI models will be trained for highly specified functions, enabling greater use of out-of-the-box solutions, even in very unique niches.
Which AI custom AI solutions should you try?
Now that you have an understanding of custom generative AI and what the best AI tools for business are, I want to give you a quick summary of my top picks and who I would recommend them to.
Start with ChatGPT if you want a general-purpose text and reasoning assistant for your company. It will provide everything from content to analyses and prototyping, while being easily integrated via APIs.
Try Claude if you have a document-heavy workflow and you want an AI that is capable of advanced reasoning and creating large-scale projects.
Use Writesonic if you need a marketing or SEO copywriting workflow. Thanks to its multiple templates and excellent SEO-strategizing and generation tools, Writesonic will help you rank on search engines and in AI search quickly.
Adopt GitHub Copilot if you want to boost developer productivity, while maintaining control and understanding of produce code.
Use Midjourney if you need high-quality images for campaigns, concepts, and branding.
Choose Runway ML if you’re focused on creating video and advanced media workflows.
Your decision will depend on your specific AI-related business needs. Think about your use case, choose a tool based on my breakdown above, and then customize it to fully meet your needs.
FAQ
What’s the difference between a custom AI solution and using tools like ChatGPT or Midjourney out of the box?
Out-of-the-box tools share public infrastructure, which risks exposing your sensitive inputs to public training datasets. A custom AI solution is securely hosted within your own private cloud or on-premise servers. It connects directly to your proprietary databases for highly specific outputs, ensuring your corporate data remains completely isolated and under your control.
How do I know if my business is ready to invest in custom AI solutions?
You are ready if public AI tools no longer meet your strict privacy, security, or operational needs. If generic tools are no longer viable, ensure your company is prepared by conducting audits and cleaning up data structures.
Do I need in-house developers to build on top of generative AI tools?
No, not always. You can automate basic workflows using no-code platforms like Zapier without writing a single line of code. However, building a highly secure, integrated AI application tied to private databases requires either an internal engineering team or a trusted technical agency.
How can I estimate the ROI of a custom AI project before I start?
First, calculate the annual human labor cost of the specific bottleneck you want to automate. Next, estimate your total AI costs, including initial development, API token usage, and ongoing cloud hosting. If your projected savings significantly outweigh the total AI infrastructure costs, your project likely has a positive ROI.
Are custom generative AI solutions safe for sensitive or regulated data?
Yes, enterprise platforms like ChatGPT Enterprise or Claude Enterprise offer the option to enable strict zero-data-retention policies, meaning your inputs are never used to train public models. For maximum security, you can even run open-source models on completely air-gapped, local servers to guarantee HIPAA or SOC 2 compliance.
