Custom AI Models with Your Data

AI That Understands Your Business

Beyond Generic AI Solutions

While pre-trained AI models offer good general capabilities, they lack the specific knowledge and context of your unique business environment. Our custom AI model development service creates tailored solutions that understand your industry terminology, recognize your document formats, and align with your specific business processes.

By training models on your own data — documents, communications, historical records, and domain-specific information — we create AI systems that deliver significantly higher accuracy and relevance compared to generic alternatives. These custom models integrate seamlessly with your existing workflows and speak your business language.

All training and model development is performed securely within your data perimeter, either on your premises or in our Jersey data center, ensuring your sensitive information never leaves the island.

Types of Custom Models We Develop

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Document Understanding

AI models that process, classify, and extract information from your specific document types:

Form recognition and data extraction
Contract clause identification
Regulatory compliance checking
Custom document classification
Industry-specific terminology recognition

Perfect for financial services, legal firms, and regulated industries dealing with large volumes of documents.

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Conversational AI

Custom chatbots and conversational systems that understand your specific domain:

Internal knowledge assistant
Client service automation
Industry-specific Q&A systems
Process guidance chatbots
Custom terminology understanding

Ideal for customer service, internal support, and knowledge management in specialized domains.

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Predictive Analytics

AI models that forecast trends and outcomes based on your historical data:

Risk assessment models
Client behavior prediction
Resource allocation optimization
Demand forecasting
Anomaly detection systems

Essential for finance, operations planning, and strategic decision-making in any industry.

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Intelligent Search

Enhanced search capabilities that understand context and relevance:

Semantic enterprise search
Knowledge graph development
Intelligent document retrieval
Context-aware recommendations
Multi-format content search

Valuable for knowledge-intensive organizations with large information repositories.

Our Model Development Process

We follow a structured, iterative approach to create AI models that perfectly align with your business requirements and data characteristics.

Requirements Analysis

We begin by thoroughly understanding your business needs, use cases, and desired outcomes. This includes identifying key performance indicators, success metrics, and integration requirements for the AI model.

Data Assessment

Our data scientists evaluate your existing data assets to determine suitability for model training. We identify any data gaps, quality issues, or preprocessing requirements to ensure optimal model performance.

Data Preparation

We prepare your data for model training by cleaning, normalizing, and structuring it appropriately. This may include anonymization for sensitive data, format conversion, and creating training/validation datasets.

Base Model Selection

Based on your requirements, we select the most appropriate foundation model architecture to serve as the starting point for customization. This ensures we leverage proven AI capabilities while tailoring to your specific needs.

Model Training

We train the custom model using your prepared data, fine-tuning it to recognize your specific patterns, terminology, and business logic. This process occurs securely within your data perimeter or our Jersey data center.

Validation & Refinement

The model undergoes rigorous testing and validation against your specific use cases. Based on performance metrics and feedback, we refine and optimize the model through iterative improvements.

Deployment & Integration

Once the model meets performance criteria, we deploy it to your environment and integrate it with your existing systems. This includes API development, documentation, and ensuring seamless operation.

Monitoring & Continuous Improvement

We implement monitoring systems to track model performance over time and establish feedback mechanisms for ongoing improvement. As your business evolves, the model can be periodically retrained with new data.

General vs. Custom AI Models

Understanding the differences between general-purpose AI and custom-trained models helps clarify why customization delivers superior results for specific business applications.

Feature	General AI Models	CodeRock Custom Models
Domain Knowledge	Limited to general knowledge	Deep understanding of your industry
Terminology	Generic vocabulary	Recognizes your specific terminology
Document Formats	Basic document understanding	Trained on your exact document types
Contextual Understanding	Limited contextual awareness	Understands your business context
Accuracy for Specific Tasks	70-80% typical accuracy	90-95% accuracy for specialized tasks
Data Privacy	Often requires data to leave premises	Training occurs within your data perimeter
Regulatory Compliance	Generic compliance understanding	Trained on your specific regulations
Workflow Integration	Requires significant adaptation	Designed to fit your existing workflows
Continuous Improvement	Generic updates only	Evolves with your business data

The performance gap between general and custom models widens significantly for specialized tasks and industry-specific applications, making custom model development a superior investment for businesses with unique requirements.

Frequently Asked Questions

How much data do we need for a custom model? ▼

The amount of data required varies based on the complexity of the task and the desired accuracy level. Generally, we recommend:

Document understanding models: 500-1000 sample documents
Conversational AI: 1000+ example conversations or question-answer pairs
Predictive models: At least 2 years of historical data with sufficient samples

However, we can work with smaller datasets by leveraging transfer learning techniques that adapt pre-trained models to your specific domain with less data. We'll assess your available data during the initial consultation and provide recommendations.

How long does custom model development take? ▼

Development timelines vary based on project complexity, data readiness, and scope. Typical timeframes are:

Simple models (basic document classification, straightforward predictions): 4-6 weeks
Moderate complexity (conversational AI, advanced document processing): 8-12 weeks
Complex projects (multi-model systems, highly specialized applications): 12-16+ weeks

These timelines include all phases from requirements analysis through deployment. We provide detailed project plans with milestones during the initial engagement phase.

How do you ensure data security during model training? ▼

Data security is our top priority. We employ multiple layers of protection:

On-premises training: For highly sensitive data, we can perform all model training on your premises, ensuring data never leaves your control
Jersey data center: Alternatively, training occurs in our secure Jersey facility with strict access controls
Data anonymization: When appropriate, we implement anonymization techniques to remove personally identifiable information
Secure transfer protocols: All data movement uses encrypted channels
Access restrictions: Only designated team members have access to training data
Compliance alignment: Our processes adhere to Jersey regulatory requirements and international best practices

We also offer data protection impact assessments for projects involving sensitive information.

Can models be updated as our business evolves? ▼

Yes, continuous improvement is a key aspect of our approach. AI models should evolve alongside your business. We offer:

Scheduled retraining: Regular updates with new data (quarterly, bi-annually, or annually)
Performance monitoring: Systems to track model accuracy and identify when retraining is beneficial
Incremental learning: Some models can be designed to continuously improve with new data without full retraining
Feedback incorporation: Mechanisms to collect user feedback for model refinement

We recommend maintenance plans that include periodic model reviews and updates to ensure optimal performance as your business processes, terminology, and requirements evolve.

What if we don't have enough data yet? ▼

If you have limited data, we can still develop effective custom models through several approaches:

Transfer learning: Adapting pre-trained models to your domain with minimal additional data
Synthetic data generation: Creating additional training examples based on your existing data
Data collection strategies: Implementing processes to gather more relevant data over time
Hybrid approaches: Combining rule-based systems with machine learning where appropriate
Iterative development: Starting with a baseline model that improves as more data becomes available

We can also advise on data collection strategies to help you build a valuable dataset over time while still providing immediate AI capabilities.