horoz.aiCustom AI assistants and agents
We build assistants that work with your own data and tools and take on tasks in customer support or internal operations. The assistant does not behave like a generic chatbot; it works from your knowledge and your rules.
What it covers
- Defining which questions the assistant answers and which tasks it takes on, including the limits
- Preparing your documents, knowledge base and other data sources so the assistant can use them
- Connecting the assistant to the tools and systems you already use
- Testing answer quality on real examples and making the assistant say so when it does not know
- Monitoring usage after launch and continuing to improve the assistant
When it is the right starting point
- Your team keeps answering the same questions
- Knowledge is spread across many documents and systems and finding it takes time
- You want to answer customers quickly outside office hours too
How we work
Defining the scope
We decide together who the assistant will serve, which questions it will answer and which tasks it will take on. Just as important is what the assistant will not do and when it should hand the conversation over to someone on your team.
Preparing the knowledge sources
We review your documents, knowledge base and other sources and, with you, weed out information that is outdated or contradictory. We set the assistant up to draw only on the sources you have approved.
Prototype and testing
We build a small, working prototype and try it with the questions your team actually receives. Together we assess the accuracy and tone of the answers and whether the assistant says so clearly when it does not know something.
Trial within your team
Before opening the assistant to your customers, we can, if you wish, make it available to your team only for a while. Your colleagues try the answers in their real work and flag what is missing or wrong; with that feedback we correct the knowledge sources and the assistant’s rules before launch.
Launch and improvement
We connect the assistant to the channels and systems you use and put it into service. We monitor how it is used in production and keep improving it by reviewing unanswered questions and feedback on a regular basis.
What it can look like in practice
Example scenario: Picture a business that keeps receiving similar questions about its products. The support team spends a large part of its day answering the same questions about delivery, returns and terms of use, while the correct information is spread across several documents. An assistant that draws on those documents could answer the common questions outside office hours too, and pass requests it is unsure about, or that need a personal decision, on to the team. The team could then spend its time on more complex requests. Which topics the assistant answers, when it hands over to the team and which sources it relies on would be agreed together from the start. This example is hypothetical and does not describe a specific client; its purpose is to show how such an assistant could be designed.
Frequently asked questions
What is the difference between an AI assistant and a generic chatbot?
Generic chat tools answer from broad knowledge, but they do not know your products, processes or rules. The assistant we build draws on your documents and systems, stays within the scope you define and, instead of guessing on a topic it does not know, says so plainly.
What happens if the assistant gives wrong information?
No language model answers correctly every time, which is why we discuss the risk from the start. We test the answers on real examples, set the assistant up to rely on approved sources and make it hand the conversation to someone on your team when it is unsure. We also keep monitoring its answers in production.
Which systems can the assistant work with?
That depends on the tools you use and the ways they can be connected. In discovery we decide together which systems the assistant will read information from and in which systems it will take actions, and we say plainly where a connection is not possible or carries risk. We keep connections limited so the assistant can reach only the information and actions it needs.
How is our data protected?
We access only the data the project needs and we protect it with care. Right from the start we agree with you which information the assistant can access, who it can share that information with and how conversations are stored.
Does the assistant only answer questions, or can it take on tasks too?
Both are possible. You can start with an assistant that finds information and answers questions; where it makes sense, tasks such as creating records, classifying requests or forwarding them to the right person can be added. We decide together which tasks to leave to the assistant by weighing benefits and risks, and where a person should keep the decision, we add approval points.
What happens after the assistant goes live?
We monitor usage and keep improving the assistant by reviewing unanswered questions and user feedback. We set up what is needed for the assistant to stay current as your knowledge sources change, and we document the system so your team can understand it.
Other services to consider
LLM integration and software development
If you need to add a language model feature to an existing application, explore the scope of product and backend integration.
Process automation
If your priority is processing documents or moving data between systems, explore how those tasks can form an automated workflow.
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Let us talk about what you need
Describe your situation in a few sentences and we will work out the right next step together.