We build self-hosted AI chatbots with a custom knowledge base
Our services include designing, developing, and deploying secure on-premise AI chatbots. We ensure context-aware interactions by integrating your internal knowledge while prioritizing data privacy and control.
Our Generative AI development expertise
Custom chatbot development
Key steps we cover in self-hosted AI chatbot development
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1. Defining objectives and use cases
We start by understanding your business needs to define the chatbot’s role and expected outcomes.
Here’s how we do it:
- Identify core use cases, such as customer support, knowledge retrieval, or internal operations.
- Define measurable objectives, like response accuracy, latency, and response time.
- Evaluate existing workflows and systems for integration with the chatbot.
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2. Building and structuring the knowledge base
A well-organized knowledge base is the foundation of an effective chatbot.
To create it, we:
- Gather data from internal documentation, FAQs, and interaction logs.
- Preprocess and structure data to enable semantic search and context-aware responses.
- Implement automated pipelines for continuous data updates, ensuring the knowledge base remains current.
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3. Fine-tuning and optimizing AI chatbot
Customizing the AI model ensures it meets the specific needs of your business.
To achieve this, we:
- Train the model using proprietary datasets to align with industry terminology and workflows.
- Use retrieval-augmented generation (RAG) techniques to link the knowledge base for more accurate responses.
- Optimize performance with techniques like quantization and model pruning to reduce latency and computational costs.
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4. Infrastructure setup and secure deployment
We configure the chatbot for optimal performance and data security.
The following steps are essential:
- Use tools like Docker, Kubernetes, and AI frameworks like LangChain or Hugging Face to set up the environment.
- Deploy the chatbot on-premise or in hybrid setups to maintain full control over data.
- Implement encryption, access controls, and compliance monitoring to ensure regulatory adherence.
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5. Testing, monitoring, and continuous improvement
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Real-world testing and ongoing refinement ensure the chatbot meets performance expectations.
What we do:
- Conduct testing in live environments to measure accuracy, latency, and user experience.
- Use analytics dashboards to monitor key performance metrics and identify areas for improvement.
- Regularly update the model with new data and feedback to maintain relevance and accuracy.
High-quality data is the foundation of an effective AI chatbot
Data preparation in AI chatbot development
Ensure optimal performance for your self-hosted AI chatbot
Self-hosted GPT vs. API-based GPT
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Self-hosted “chat GPT”
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Data control and privacy
All data remains within your infrastructure, ensuring compliance with regulations like GDPR, HIPAA, or CCPA. Sensitive information never leaves your servers. -
Customization
Models can be fine-tuned and optimized for your specific needs. You have full control over model updates, configurations, and performance enhancements. -
Cost structure
Requires upfront investments in hardware, infrastructure, and ongoing maintenance. Long-term savings are significant for high-volume usage.
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API-based GPT
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Data control and privacy
Data is processed externally by third-party servers, which may pose privacy concerns for industries handling sensitive or regulated data. -
Customization
Limited to predefined configurations and capabilities. Customization options are constrained by the service provider. -
Cost structure
Operates on a pay-as-you-go model, with costs tied to token usage. Expenses can escalate for frequent or high-complexity queries.
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Set up the foundation for secure AI
Technical infrastructure for self-hosted AI chatbots
Hardware requirements
Software stack
Knowledge base integration
They trusted our expertise
Our featured self-hosted AI chatbots projects
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AI Agent
AI chatbot for Customer Support at a leading bank
CLIENT: CREDIT AGRICOLE
- We developed and deployed a self-hosted AI Agent powered by the bank’s internal knowledge and documentation.
- Simple requests are handled automatically for faster resolution, freeing up agents for more complex and personal interactions. More complicated messages are passed to the right teams.
- The system creates customized draft replies and snippets. It can format them into PDFs for sending. It helps improve customer satisfaction scores, and meet service-level agreements.
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GenAI assistant
Knowledge-based AI chatbot for consultants at an international bank
CLIENT: BANK • UAE
- We built a self-hosted AI chatbot with a custom knowledge base drawn from the bank’s internal documents and product information.
- The assistant analyzes input from the salesperson in real-time and compares it against the conditions of over 20 different credit card products. Then, it issues accurate recommendations that meet both client expectations and bank requirements.
- Direct integration with the bank’s product database ensures recommendations are based on the latest offer conditions.
Testimonial
What our clients say
By automating certain customer interactions, bank employees are provided with a prepared “semi-product”, which enables them to dedicate more time to personalizing and empathizing with customer communication, and thus taking even better care of their needs.
Why choose us
AI chatbot development company
Self-hosted chatbot experts
Compliance with industry standards
Domain expertise
Get in touch
Let’s talk
Book 1-on-1 consultation
Grzegorz Motriuk
Head of Sales | Application Development
Our consultant is at your disposal from 9 AM to 5 PM CET working days from Monday to Friday for any additional questions.
Frequently asked questions
Self hosted AI chatbot FAQs
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What is a self-hosted AI chatbot, and how is it different from cloud-based solutions?
A self-hosted AI chatbot is deployed within your own infrastructure, either on-premise or in a hybrid environment. Unlike cloud-based solutions, all data remains under your control, ensuring better security, compliance, and customization options.
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What kind of data is needed for LLM fine-tuning?
We use domain-specific data that reflects your business context, such as customer interactions, industry-specific terminology, or internal documents, to fine-tune the model.
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What kind of data can be used to build the chatbot’s knowledge base?
We can integrate internal documents, FAQs, customer interaction logs, product manuals, and database entries. These sources are preprocessed and indexed to create a comprehensive, searchable knowledge base.
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What are the hardware and software requirements for hosting an AI chatbot?
You’ll need infrastructure capable of handling the computational demands, such as servers with GPUs, sufficient memory, and scalable storage. Software requirements include frameworks like Python, Docker, LangChain, or Hugging Face, along with database tools like Pinecone for knowledge indexing.
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How do you ensure the chatbot complies with data protection regulations like GDPR or HIPAA?
We implement advanced encryption, role-based access controls, and compliance workflows. All data remains within your infrastructure, ensuring adherence to industry regulations and best practices for data security.
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Can the AI chatbot be customized for specific use cases or industries?
Yes, we fine-tune the chatbot with proprietary datasets and configure it for your industry’s terminology and processes. This ensures it delivers precise, context-aware responses tailored to your business needs.
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How long does it take to develop and deploy a self-hosted chatbot?
Timelines depend on project complexity, but most deployments take 8–12 weeks, including data preparation, model customization, integration, and testing.
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What kind of ongoing maintenance is required for a self-hosted AI chatbot?
Regular maintenance includes monitoring performance metrics, updating the knowledge base, retraining the model with new data, and applying system updates to ensure optimal operation.