An AI agent that answers
from your documents,
built to your operating rules
We connect documents and operational data into an agent that shows where each answer came from. Channels, answer policy, and permission scope are reviewed before it ships.
documents and data
declining, and handoff
connected per condition
control over tool execution
There are plenty of chatbots.
There are not many you can trust
The same question, every day
Repeated questions about policy, process, and products eat up someone's day. The answer never changes, but a person handles it every time.
The knowledge exists, but nobody can find it
The answer is definitely somewhere in the company. In the wiki, the policy manual, a message thread. Finding it takes longer than asking.
Answers nobody trusts
A general LLM chatbot produces plausible wrong answers. One answer without evidence and the whole organisation stops using it.
Connect, configure, review, ship.
It is built through configuration
Connect knowledge
Upload documents or connect internal systems. If you already run Ontokit, the ontology you built is used directly as the knowledge base.
Persona and policy
Tone, the scope it will handle, and the topics it must not answer are set as policy. Your company's service standards become the agent's rules.
Test and review
Simulate expected questions and check answer quality before launch. Low-confidence answers are flagged automatically so they can be tuned first.
Ship to channels
Web widget, KakaoTalk, Slack, and internal portals are all candidate channels. We check each channel's API and authentication conditions and connect the same answer policy.
It answers with your company's knowledge,
and shows you the source
Source-backed answers
Each answer is linked to the source document and passage it used. When the evidence falls short of the bar, it can decline or point the person to a human.
Handoff to a person
Anything the agent cannot handle goes to a person along with the conversation context. The customer never repeats themselves.
Conversation analytics
A dashboard shows what gets asked most and where the answers failed. Questions that keep getting stuck turn into suggestions for filling knowledge gaps.
Permissions and security
The knowledge each department and level can reach is separated. HR policy for everyone, the salary table for HR only.
The scope we have delivered
on our own projects
Here is the knowledge base and agent scope we have built on our own projects.
Automating HR, admin, and IT enquiries
Answers about policy, process, and benefits are linked to their evidence. This is the scope delivered in the CJ Logistics HR AX transformation PoC.
Field knowledge Q&A
A structure that answers questions on the floor from production and quality domain knowledge. This is the scope delivered in the Hojeon Limited apparel OEM AX transformation.
Handling product and order enquiries
Product information, order and delivery status, and exchange or return procedures can all be in the answer scope. Anything it cannot resolve is configured to pass to an agent with the conversation context.
Frequently asked questions
The conditions people check most often before adopting.
What documents and data can you connect?
Documents such as policies, manuals, and FAQs, along with data from business systems. The actual scope is decided after checking file formats, whether an API exists, access rights, and your personal data and security policy.
Can answers show their sources?
Yes. Each answer links to the document and passage it used, and you can set the rule for declining or handing off to a person when the evidence is thin.
Can it ship to web, KakaoTalk, and Slack?
Which channels are supported and how they connect depends on each channel's API policy, your accounts, and the authentication method. A common path is to review on the web widget first, then connect the other channels in sequence.
Can we separate what each department can see?
Where user authentication and source data permissions are available, access can be configured by department, level, and role. Answers and tool actions that need permission are reviewed scenario by scenario before launch.
Who manages answer quality after it is built?
The project separates your team's role from the Alphaca Labs operations role. We review declined answers, low ratings, and evidence errors, and track change history for documents, policy, and prompts.
Let us map the scope
of your first agent together
We review your current documents and the questions that repeat, then propose a demo and a build scope.