Retrieval-augmented generation (RAG)
Use RAG to retrieve relevant information from your own knowledge sources before the chatbot generates an answer. Get responses grounded in company data instead of generic model knowledge.
Key AI chatbot capabilities we deliver
Our AI chatbot development company combines proven AI technologies with your business data and systems to make your chatbot interactions accurate, useful, and convenient.
Large language models (LLMs)
Integrate the right LLM for your use case, balancing response quality with privacy and infrastructure requirements. The right choice improves response quality while avoiding unnecessary costs or privacy issues.
Agentic AI
Go beyond question answering with chatbots that can take action on the user’s behalf. Agentic AI works with connected tools and APIs to complete multi-step tasks, update business systems, or coordinate workflows based on the conversation.
Multimodal AI
Integrate multimodal models that can work with text, images, and documents. Provide information in the format that is most convenient instead of converting everything into a text request first.
Voice-enabled chatbots
Add automatic speech recognition (ASR) and text-to-speech (TTS) so users can interact with the chatbot without typing.
Voice and pronunciation
Customize speech behavior to match the interaction you want to create. Brand names, industry terminology, and specific pronunciations can be configured for more natural conversations.
Business system integration
Connect the chatbot to internal and third-party systems through APIs. Access current information or complete tasks without switching between multiple applications.
Secure access to business data
Use conversational AI with sensitive internal data without giving everyone the same level of access. Add authentication and role-based access control so the chatbot only retrieves information a particular user is allowed to see.
Scalable infrastructure
Design chatbot infrastructure to handle growing conversation volumes and traffic spikes without slowing down the user experience. This lets the solution grow from an early rollout to wider adoption without requiring a complete architectural rethink.
Private AI deployment
Take greater control over how sensitive data is processed and where it is stored. Deploy LLMs within private or controlled infrastructure when public AI services do not meet project requirements.
Fast resolution
Design flows around what the user is trying to achieve. The chatbot asks only for the information it needs and moves the interaction toward a clear result.Actions, not just answers
Connect the chatbot to your business systems so it can do more than provide information. Place orders, make bookings, update records, or move a workflow forward directly from the conversation.Brand-consistent communication
Adapt tone, terminology, and response style to your brand. The chatbot becomes part of the wider customer experience rather than a generic AI layer.Continuous improvement
Review how users interact with the chatbot and where friction remains. We improve conversation flows to increase the share of requests the chatbot can resolve successfully.
Value delivered
- Private AI deployment: The solution runs on the client’s own infrastructure, with Mistral selected for the question-answering workflow.
- Custom knowledge access: The chatbot retrieves project-specific information from the company’s historical data.
- RAG and SQL-based retrieval: We first tested RAG, then introduced SQL query generation where structured data required higher accuracy.
- Accuracy optimization: Prompt tuning increased answer accuracy from 70% to 85% in Apiko’s testing.
The solution reduces the time estimators spend searching through past project records. It also keeps sensitive company data within the client’s own infrastructure.
When deployed for a pizzeria chain, the solution:
- Answered 100% of incoming calls
- Processed 2,284 orders in 63 days
- Captured 256 orders that would otherwise have been missed
- Saved around $12,000 in customer service costs
The agent adapts to the brand’s tone, terminology, and pronunciation to make conversations feel more natural. It also handles several calls at once and makes outbound calls for reminders or updates.
Integration with existing business systems allows the agent to trigger actions such as order submission or receipt printing. The system also tracks interaction history and call metrics, giving the business useful insights into customer demand and service quality.
The result is a voice assistant that resolves complete customer interactions while reducing support costs and manual work.
Custom AI chatbot development process
We build AI chatbots step by step, starting with the use case and data requirements and carrying the solution through to production and long-term support.
Define the use case and data requirements
We start by identifying what the chatbot needs to do and which information it should work with. This helps define whether the solution needs private deployment, external integrations, or other custom components.
Design the conversation experience
We define how the chatbot should communicate and move users toward their goals. This includes its brand voice, response style, and when it should ask follow-up questions or involve a human.
For voice agents, we can also define pronunciation and other speech behavior so the experience feels consistent with your brand.
Select the LLM
We evaluate large language models based on the task and project requirements. Response quality, privacy, and infrastructure all influence the choice. Depending on the use case, we can work with commercial APIs or privately hosted models such as Mistral and Llama.
Choose the embeddings model
For RAG-based chatbots, we select an embeddings model that can represent your documents and user questions as vectors. This makes semantic search possible and allows the system to retrieve information based on meaning rather than exact keywords.
Build the knowledge base
We prepare your business data for retrieval. Documents are processed and divided into smaller chunks, then converted into embeddings and stored in a vector database such as FAISS, Chroma, or Pinecone.
Build the RAG pipeline
We connect the knowledge base with the LLM. When a user asks a question, the system retrieves relevant information and adds it to the model context before the answer is generated.
Develop and integrate the chatbot
We build the chatbot interface and connect it to the systems your business already uses. Depending on the project, this can include internal tools, databases, APIs, or customer-facing platforms.
Test and optimize
We test the chatbot with realistic user interactions to see whether it provides useful answers and moves conversations toward the right outcome. We then refine its behavior based on where users encounter friction or the AI performs inconsistently.
AI chatbot development services for industries
We provide AI chatbot app development services according to the workflows, data, and operational requirements of your industry.AI chatbots for healthcare
Support patients and staff with faster access to information. AI chatbots can help with appointment-related questions, internal knowledge search, onboarding, and other administrative workflows.AI chatbots for fintech
Build secure conversational tools for customer support, onboarding, and internal operations. Chatbots can work with financial documentation, account information, and business rules while keeping access controls in place.AI chatbots for construction
Help teams find information across project documents, reports, proposals, and historical records. RAG makes it possible to retrieve relevant project data without searching through files manually.AI chatbots for logistics
Give teams and customers faster access to shipment, order, and delivery information. Chatbots can also support internal coordination by connecting to logistics systems and operational data.AI chatbots for real estate
Use AI chatbots to support property search, tenant communication, document access, and internal operations. They can also help teams work with listing data and customer inquiries more efficiently.AI chatbots for software and SaaS
Add conversational support directly to digital products. AI chatbots can guide users through onboarding, answer product questions, and provide access to technical documentation or account information.Customer support chatbots
Handle common customer questions, provide instant assistance, and escalate complex cases to human agents when needed.Knowledge assistants
Help employees find information across company documents, policies, manuals, and other internal sources without searching through them manually.Agentic AI chatbots
Let users do more than ask questions. Agentic AI chatbots can take action across connected systems, complete multi-step tasks, and move workflows forward directly from the conversation.Sales chatbots
Engage potential customers, answer product questions, qualify leads, and guide users toward the next step in the sales process.Onboarding and training chatbots
Guide employees or customers through onboarding and provide contextual help as they learn a product, process, or internal system.Why choose Apiko for AI chatbot development
We combine hands-on AI expertise with years of software product development experience, so your chatbot is built as a reliable part of your product rather than an isolated AI feature.
Practical AI experience
We work with LLMs, RAG, agentic AI, and AI-assisted QA in real projects. Our team understands where AI adds value and where strong engineering control is required.
Proven RAG expertise
We have built custom knowledge systems for business data and tested different retrieval approaches in practice. When one architecture does not provide the required accuracy, we know how to evaluate alternatives and improve the result.
Strong software engineering background
An AI chatbot still needs solid architecture, secure integrations, testing, and production infrastructure. Our experience across backend, frontend, cloud, and QA helps us build solutions that are ready for real business use.
Technology chosen for your use case
We do not default to one model or provider. Our engineers select the architecture and AI stack based on your data, privacy requirements, expected performance, and product goals.
Trusted long-term partner
Our clients value clear communication, reliable delivery, and long-term cooperation. Apiko has 62 verified Clutch reviews and a 4.9/5 rating, reflecting the way we work with teams from development through ongoing product growth.
FAQ
A custom chatbot is built around your workflows, data, and technical environment. Custom AI chatbot development services are useful when you need deeper integrations, private knowledge access, or behavior that standard chatbot platforms cannot support.
Yes. We can connect the chatbot to internal documents, databases, and other business sources through RAG or direct integrations. This allows the chatbot to answer questions using your own information.
We evaluate the model based on the requirements of the product. Response quality, privacy, infrastructure, and expected usage all influence the choice.
Yes. Our AI chatbot app development services can support chatbots embedded into web or mobile products, as well as internal business platforms.
The cost depends on the chatbot’s complexity and the amount of custom development required. Integrations, data preparation, model choice, and deployment requirements can all affect the final scope.
The timeline depends on the use case and technical complexity. A focused chatbot with a limited knowledge base can be delivered faster than a solution with custom integrations or agentic workflows.
An experienced AI chatbot development company can handle both the AI layer and the software engineering around it. This helps ensure the chatbot is properly integrated, tested, secure, and ready for production.
Yes. Our chatbot development services can continue after deployment with performance monitoring, updates, optimization, and further feature development.
