What do you need to develop a custom AI chatbot?

Let’s assume you are looking for a custom AI chatbot development service.

Possible use cases for such AI chatbots:

• Company website widget with AI chatbot answering customer questions

It can potentially reduce the workload from departments answering phone calls

• Landing page AI chatbot

Customers could potentially learn more before they decide to buy your service

• Internal company knowledge base or documentation AI chatbot

An interactive documentation might boost productivity in large organizations

• Custom AI integrations with any business logic leveraging the text-based chat interface

AI Chatbot can integrate with your product or external services via APIs and suggest further actions such as URLs or Buttons.

AI engineering

Ideally, you will be looking for a freelancer or a team with experience in developing and delivering AI chatbots. This is crucial because AI is a fairly new field.

There is an outline of the development service that will be provided. The following steps are the general overview and do not assume the same execution order. Development is going to be conducted with the Agile approach, and the following steps will be executed iteratively many times, delivering results and business value on each iteration.

• Gathering requirements.

At this initial stage, they will communicate with you and collect requirements for your specific use case. They will create a draft of an initial plan and make a rough estimation for each step.

• Prototyping and Prompt engineering discovery.

Ideas and assumptions will be tested with the working quick prototype before moving into the development stage. This step includes crafting the ideal AI prompt, or a set of prompts, and an AI answer processing. You will have an MVP as a result.

• Storage selection, schema design, Database infrastructure set-up.

At this step, the team will use the prototyping and research results after investigating the company knowledge base, which needs to be converted into AI suitable format. The team will choose the proper Vector storage database, such as PostgreSQL pgvector, Milvus, FAISS, etc. Then they will deploy and configure it on a managed service or a company server.

• Knowledge base sourcing.

It is time to write a set of Python scripts to convert the company knowledge base into AI-compatible storage. Any source and any file format can be converted. Popular sources are API, SQL dump, internal or external web documentation, and wiki pages. Popular file formats are PDF, DOC, XLS, PPTX, text, markdown, etc.

• Back-end developing

A back-end tech stack will be selected and developed based on the use case and project complexity. It can be implemented in any desired tech stack if there are pre-defined technical preferences. One of the following back-end tech stacks is the most suitable for an AI chatbot:

• Front-end development

A front-end tech stack also will be selected and developed based on the use case and project complexity. One of the following front-end tech stacks is the most suitable for an AI chatbot:

• DevOps for CI/CD and quality control automation, Cloud or server deployment

Every interaction in this multi-step development process will be delivered to development and production servers. Ideally, the team will streamline continuous delivery and continuous integration for fast delivery. They will configure code quality control and test automation to demonstrate the high quality of the solution.

• Long-term support and maintenance

After finishing the project, you need to expect long-term support and further feature development offers. It makes a lot of sense since tech needs to be maintained, issues need to be resolved, and problems need to be fixed. The support and maintenance offer can be based on a rate per hour or as a fixed monthly package.

Cost of development

What about the cost? It depends on many factors. If we are talking about the base AI chatbot version, that is answering customer questions using the knowledge base, without any external integrations - here is an approximate calculation, based on the steps above.

Rought time and cost estimations for each step until the AI chatbot is finished and delivered:

Step 1-2: 2 weeks

Step 3-4: 2 weeks

Step 5: 3 weeks

Step 6: 3 weeks

Step 7: 1 week

Full project delivery is about 3 months.

Below is a calculation for a custom basic AI chatbot development based on an average freelancer rate per hour in Germany.

Approximate price: (560-800 eur per day) * 20 days per month * 3 months ~? 33600 EUR to 48000 EUR.

It will be fair to assume a cost of approximately 35k to 50k EUR depending on the complexity.

Be aware, that additional development, such as a very customized styling, or any additional features that will add to the complexity of the project - will lead to additional cost. Especially features like API integrations to internal company APIs or external services can be time-consuming and potentially even double the development time. Be sure to account for that in your initial development plan and budget.

Are you looking for a freelancer capable to develop and deliver a custom AI chatbot? Let’s talk! AI application is not limited only by chatbots. Do you need a custom AI integration in your product? Do you need AI integration support? Write me and we will discuss your idea.

Do you need a dev team for your bigger ambitious AI project led by an experienced Tech Lead? I am partnering with Hold My Code GmbH which can provide software engineers for your project.