AI at the Core
We didn't just add a chatbot. We rebuilt the HR experience from the ground up using generative AI.
Conversational AI Engine
Trusted by innovators
From System of Record to System of Intelligence
Most HR platforms added "AI" features as an afterthought—usually just a basic chatbot that links to FAQs. At Kiework, AI isn't a feature; it's the architecture. We use state-of-the-art Large Language Models (LLMs) deeply integrated into your company's data layer to understand intent, context, and permissions.
Conversational Actions
Kiework understands natural language commands. An employee can type "I'm sick today" and the AI will:
- Identify the intent (Sick Leave).
- Check the leave balance.
- Apply for the leave for the current date.
- Notify the manager.
This drastically reduces the time spent navigating menus and filling forms.
Policy Intelligence
HR teams spend hours answering repetitive questions. Kiework ingests your PDFs (Employee Handbook, Travel Policy, Insurance) and turns them into a queryable knowledge base.
When an employee asks, "What's the per diem for a trip to London?", the AI scans the latest travel policy document, extracts the specific value for international travel, and provides a precise answer with a citation.
Generative Analytics
Data analysis shouldn't require a SQL expert. With Kiework, CXOs can ask, "Show me the attrition trend for the Engineering department over the last 6 months compared to Sales." The system interprets the query, fetches the relevant data points, and renders a comparative chart instantly.
Not Just a Chatbot: The Architecture of Intelligence
When we say "AI at the Core," we don't mean a simple rules-based chatbot that directs you to a URL. We mean a fundamental re-architecture of how humans interact with enterprise data. Kiework leverages the latest advancements in Large Language Models (LLMs) and Vector Databases to create an HR assistant that truly understands context, not just keywords.
The Power of RAG (Retrieval-Augmented Generation)
Standard AI models like GPT-4 are powerful, but they don't know your company. They don't know your leave policy for 2025 or your specific travel allowance for Sales Managers. Kiework solves this using Retrieval-Augmented Generation (RAG).
When an employee asks, "Can I take a sick leave next Monday?", our system first retrieves your specific leave policy document and the employee's current balance from the secure database. It then feeds this context to the AI, which generates a precise, legally compliant answer: "Yes, you have 5 Sick Leaves remaining. However, Monday is a company holiday, so you don't need to apply for leave." This level of contextual awareness eliminates the risk of "AI hallucinations" and builds trust.
Privacy by Design
Bringing AI into HR raises valid concerns about data privacy. "Is my payroll data training ChatGPT?" The answer is a categorical No.
Kiework employs a tenant-isolated architecture. Your data lives in a private, encrypted silo. When we use LLMs, we strip Personally Identifiable Information (PII) before processing where possible, or use enterprise-grade APIs with zero-retention policies. We do not use your proprietary data to train our base models. Your knowledge base remains yours alone.
The Future: Autonomous HR Agents
We are moving beyond "Q&A" to "Agency." In the near future, Kiework's AI won't just answer questions; it will perform complex tasks. Imagine an AI agent that notices a team is overworked based on timesheet data, proactively suggests a "No Meeting Friday" to the manager, drafts the announcement email, and reshuffles the calendar—all with one approval click. This is the era of the Autonomous HR Agent, and Kiework is building the foundation for it today.
AI Questions
Is my proprietary data used to train public models?
No. Your data is isolated within your tenant. We do not use your company data to train our base models or share it with third parties.
Does the AI hallucinate?
We use RAG (Retrieval-Augmented Generation) to ground all answers in your specific company documents. If the AI doesn't find an answer in your policy, it will say so rather than making one up.