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Koios Tenant Agent

explore the capabilities of OlivoAgent

IBAC Tech is your gateway to the next generation of broker-client interaction. We are proud to host an exclusive demo of OlivoAgent, a sophisticated AI insurance agent developed by Koios Intelligence. Designed specifically for the Canadian P&C market, OlivoAgent showcases how “agentic” AI can transform routine workflows like client intake and qualification. This demo allows you to experience a seamless, conversational journey for generating a tenant insurance quote—illustrating how these tools can automate administrative bottlenecks while allowing brokers to remain focused on their vital advisory roles.

As you explore the capabilities of OlivoAgent, please note that this is an educational simulation intended to demonstrate the technology’s logic and user experience. The premiums and quotes generated during this session are not actual insurance rates and do not represent binding offers from any carrier. IBAC has partnered with Koios Intelligence to provide this tool as a hands-on resource for our members, helping brokers visualize how AI can deliver 24/7 service, eliminate double data entry, and ultimately enhance the competitive edge of the independent broker channel.

Feature

Interaction Style

Context Awareness

User Experience

Complexity

Availability

Back-End Integration

Traditional Web Forms

Static & Rule-Based

Users follow a rigid, linear path with no ability to deviate from the script.

No Memory

Each form field is an isolated event; users often have to repeat information if they start over.

Transactional

Often filled with "dry legalese," leading to higher drop-off rates and "form abandonment".

Basic Tasks

Best for routine, predictable data collection that rarely changes.

Self-Serve Only

Users are left to navigate the form alone unless they call a human agent.

Manual Review

Information usually requires a broker to manually review and re-enter data into a BMS.

AI Agents (e.g., OlivoAgent)

Adaptive & Intent-Driven

The agent "listens," understands nuances, and adjusts the conversation flow based on user intent.

Full Contextual Memory

The agent remembers previous answers and policy history to avoid redundancy and provide personalized advice.

Relational

Uses natural language and real-time clarification, which can significantly increase quote completion rates.

High-Complexity Scenarios

Manages multi-step tasks like simultaneously processing a claim and a service complaint.

24/7 Proactive Support

Acts as a tireless, "hyper-organized colleague" available around the clock to guide users through every step.

Automated Workflow

Instantly extracts data and uploads it directly into management systems without human intervention.

Feature

Interaction Style

Traditional Web Forms

Static & Rule-Based

Users follow a rigid, linear path with no ability to deviate from the script.

AI Agents (e.g., OlivoAgent)

Adaptive & Intent-Driven

The agent "listens," understands nuances, and adjusts the conversation flow based on user intent.

Feature

Context Awareness

Traditional Web Forms

No Memory

Each form field is an isolated event; users often have to repeat information if they start over.

AI Agents (e.g., OlivoAgent)

Full Contextual Memory

The agent remembers previous answers and policy history to avoid redundancy and provide personalized advice.

Feature

User Experience

Traditional Web Forms

Transactional

Often filled with "dry legalese," leading to higher drop-off rates and "form abandonment".

AI Agents (e.g., OlivoAgent)

Relational

Uses natural language and real-time clarification, which can significantly increase quote completion rates.

Feature

Complexity

Traditional Web Forms

Basic Tasks

Best for routine, predictable data collection that rarely changes.

AI Agents (e.g., OlivoAgent)

High-Complexity Scenarios

Manages multi-step tasks like simultaneously processing a claim and a service complaint.

Feature

Availability

Traditional Web Forms

Self-Serve Only

Users are left to navigate the form alone unless they call a human agent.

AI Agents (e.g., OlivoAgent)

24/7 Proactive Support

Acts as a tireless, "hyper-organized colleague" available around the clock to guide users through every step.

Feature

Back-End Integration

Traditional Web Forms

Manual Review

Information usually requires a broker to manually review and re-enter data into a BMS.

AI Agents (e.g., OlivoAgent)

Automated Workflow

Instantly extracts data and uploads it directly into management systems without human intervention.

Key Takeaways

Higher Completion Rates

Olivo has seen average progression rates of 68% in the quote process, compared to much lower industry benchmarks for traditional forms.
Reduced Operational Friction
AI agents can resolve 60–80% of customer inquiries autonomously, allowing your team to focus on high-value advocacy and relationship building.
Scale Without Headcount
Unlike forms that require human follow-up, AI agents scale seamlessly, handling hundreds of simultaneous conversations during peak renewal seasons or natural disasters.

Seamless Handoff

If a case becomes too complex, the AI agent provides a full summary of the interaction to the human broker, ensuring a frictionless transition for the client.
Test The Koios Tenant Demo Agent

Aligning with Regulatory Frameworks and Standards

Principles will align with international AI guidelines (e.g., ISO 42001, OECD frameworks) and industry-specific regulations (e.g., RIBO, OSFI, AMF) upheld by stakeholders. Adherence to legal standards will help organizations navigate jurisdictional requirements, promote sustainable practices, and prevent misuse of AI data

Encouraging Responsible Al Innovation

Principles will encourage the broker community to innovate responsibly by developing AI systems that prioritize consumer well-being, inclusivity, and fairness, while also assessing the societal, environmental, and economic impacts of their AI solutions

Promoting Accountability in AI Oversight

AI principles will reinforce accountability across all levels of organizations and third-party collaborators. By defining roles, ensure human oversight in AI processes, this enhances traceability, enables informed decision-making, and embeds mechanisms for ethical redress when errors or adverse outcomes occur

Ensuring Consumer Trust and Fairness

AI governance principles support commitment to transparency, fairness, and accountability. By requiring explainable outcomes and proactive consumer communication, it fosters trust among the broader broker community, their clients, and external stakeholders

Supporting Ethical Standards and Stakeholder Collaboration

By incorporating AI governance principles, broker members can align with its mission of fostering an ethical culture among its stakeholders Address biases, safeguard consumer protection, and promote inclusivity, which will reinforce commitment to ethical AI practices in collaboration with industry stakeholders, regulators, and third-party solution providers

PoC Use Case Overview: Al-Assisted Coverage Discovery & Gap Analysis

Technical Requirements

AI models with context on industry benchmarks and policy structures to interpret existing policy terms, endorsements, and clauses.

Integration with BMS to retrieve client profiles, exposure information, and historical policy data.

Data ingestion and continuous updates to ensure alignment with typical coverage patterns and industry guidance.

Data security and compliance features to protect client information.

Functional Scope

Analyze client-submitted data, including exposures, business context, and other relevant information.

Extract and interpret existing policy terms (e.g., endorsements, exclusions, clauses).

Benchmark against typical coverage patterns and industry guidance to identify coverage gaps.

Prioritize identified coverage gaps and provide rationale based on considerations such as industry standards and risk.

Recommend relevant products and coverage options tailored to the client profile, and generate summaries for client discussions.

AI-Powered Client Onboarding & Data Intake – Overview:
Proof of Concept (PoC) Use Case

Technical Requirements

BMS Integration: Integration with BMS for secure data capture and storage.

Applied ARS Integration: Integration with Applied ARS to enable automated quote generation.

Dedicated Parsers for Renewal: Document parsing capability for 5–6 carrier renewal documents with high accuracy.

Extensibility Framework: Modular architecture to support future enhancements and additional automation.

Functional Scope

AI Chatbot for Client Onboarding: Collect client information, answer onboarding questions, and guide users through the onboarding process.

Data Collection & Storage: Capture and store collected data directly in the broker’s BMS.

Document Processing: Enable clients to upload renewal documents and extract key data to accelerate the onboarding process.

Quotes Generation: Generate quotes based on collected information.