Enterprise AI agents
Agents designed around defined business processes. They retrieve information, collect inputs, follow approved workflow logic, prepare outputs, interact with business systems, and route exceptions to the right people.
Custom AI systems for enterprise
AI agents built around real business operations
NovusAI designs, deploys, and operates AI agents that work across business systems, data, rules, and approval processes.
From vehicle insurance quoting to customer-specific model deployment and fine-tuning, we build AI for defined operational work, not generic demonstrations.
Enterprise agents · MLOps · Open-source models · Fine-tuning · System integration
What NovusAI delivers
AI built for specific operational work
Every organisation has its own workflows, systems, rules, and exceptions. NovusAI builds AI around those realities.
We combine agent engineering, model deployment, MLOps, and enterprise integration to move AI from experimentation into day-to-day use.
Production AI engineering
AI systems designed for deployment, monitoring, maintenance, and continued improvement. We build the pipelines and operational controls required to run models and agents beyond the prototype stage.
Open-source and specialised models
Open-source models deployed for customer-controlled environments and adapted for specific business requirements through model serving, customer-specific fine-tuning, and techniques such as LoRA.
Deployed capability
Vehicle insurance quoting agents
NovusAI has developed vehicle insurance quotation agents used within vehicle insurance broker operations.
The agents support the quotation process by gathering required information, checking completeness, retrieving relevant vehicle and policy data, and preparing quote options for review.
Guided information collection
Collect the customer, vehicle, usage, and coverage information required for a quotation.
Completeness and consistency checks
Identify missing, contradictory, or unusual information before the quote progresses.
Business-rule support
Support the application of approved quotation and underwriting rules while preserving the broker’s review and approval process.
Quote preparation
Prepare quotation information and available options for staff or customer review.
Exception routing
Identify cases that require specialist attention and route them to the appropriate person.
System integration
Work with existing broker systems, approved data sources, and operational processes.
The agents reduce repetitive handling and support a faster, more consistent quotation process, with clear escalation where professional judgment is required.
NovusAI vehicle quoting agents have been deployed within vehicle insurance broker environments. They support quotation work while brokers and underwriters retain responsibility for professional review and final decisions.
AI engineering and model services
AI engineering beyond the agent interface
A useful AI application depends on more than the conversation layer. NovusAI works across the model, deployment, integration, monitoring, and improvement lifecycle.
MLOps
We build and maintain the processes required to deploy, monitor, version, and improve machine-learning and language-model applications.
- Model deployment pipelines
- Environment management
- Model and prompt versioning
- Monitoring and logging
- Evaluation workflows
- Release processes
- Performance tracking
- Operational maintenance
Open-source model deployment
We deploy and configure open-source models for customer-specific applications and operating environments selected for privacy, control, performance, and operational requirements.
Model fine-tuning
We fine-tune models for defined customer tasks, terminology, formats, and operating requirements. The use case, available data, and evaluation results determine whether fine-tuning is appropriate.
LoRA adaptation
Low-Rank Adaptation enables targeted model adaptation without retraining every model parameter. It makes specialised deployments more practical where the use case supports it.
Model evaluation
We evaluate model behaviour against the task it is expected to perform. Evaluation is part of engineering and model delivery.
- Task accuracy
- Output consistency
- Required format adherence
- Domain terminology
- Retrieval quality
- Unsupported responses
- Performance before and after fine-tuning
Delivery process
How we work
Each engagement begins with a defined business problem. We determine whether the right answer is an agent, a specialised model, a workflow integration, or a combination of these.
- 01
Understand the operation
We examine the workflow, users, systems, data, decisions, exceptions, and controls involved. The goal is to understand the work before selecting the technology.
- 02
Design the system
We define what the AI system will do, which information it will use, which systems it will access, and where people remain responsible. For model work, we determine whether prompting, retrieval, fine-tuning, LoRA, or another approach fits the requirement.
- 03
Build and integrate
We develop the agent or model application and connect it to the approved systems, data sources, interfaces, and operational processes.
- 04
Validate
We test the system against representative business scenarios before wider use. Checks reflect the project and cover relevant workflow, output, retrieval, rule-handling, exception, model, and integration behaviour.
- 05
Deploy and improve
We deploy into the agreed environment, monitor operational performance, resolve failure patterns, and improve the system as requirements evolve within the agreed delivery scope.
Enterprise integration and control
Designed for enterprise environments
AI becomes operational when it works with the systems, data, permissions, and controls an organisation already uses.
Integration
NovusAI integrates agents and model applications with approved business systems, APIs, databases, document repositories, customer platforms, and communication channels.
Permissions and human control
Access is limited to the systems, information, and actions defined for the application. Review points, approval requirements, exception paths, and restricted actions are designed into the workflow.
Traceability
Logging and operational records provide visibility into system activity and support investigation when an output or action needs review.
Deployment flexibility
Deployment architecture is selected around the customer’s technical, privacy, performance, and operational requirements.
Delivered work
Selected experience
NovusAI has delivered AI engineering work across agent development, insurance workflows, model deployment, fine-tuning, and production operations.
Vehicle insurance quoting
Quotation agents deployed within vehicle insurance broker environments to support information collection, data retrieval, quote preparation, and exception handling.
Open-source model deployment
Deployment of an open-source language model for a customer-specific application and operating environment.
Customer-specific fine-tuning
Fine-tuning work to adapt model behaviour to a customer’s task, terminology, data, and expected output.
LoRA model adaptation
Parameter-efficient model adaptation using LoRA for a specialised customer requirement.
MLOps
Operational work supporting model deployment, versioning, monitoring, evaluation, and maintenance.
Tell us what you are solving
Share the workflow, model requirement, or operational problem you are exploring. We will respond with a practical next step and an honest view of whether a custom AI system is the right fit.
- Discuss your use case
- hello@novusai.com
We use your details only to respond to your enquiry. See our privacy policy.