Deep Dive – AI, Machine Learning and SR&ED

Artificial intelligence (AI) and machine learning (ML) are now embedded across a wide range of software products, business systems, industrial applications, and digital services.  This practical guide looks at what types of AI and ML development qualify for SR&ED.

ML and Advanced Data Processing

Developing ML capabilities and advanced data processing methods qualify as SR&ED eligible activities, particularly where companies encounter technical limitations in the accuracy, reliability, scalability, or performance of data driven systems.

This may involve experimentation with model training and tuning, feature extraction, classification, prediction, pattern recognition, and data-processing methods under challenging real-world conditions.

Limited, Incomplete or Imbalanced Data

In many real-world applications, sufficient high-quality training data is simply not available. Datasets may be small, biased, poorly labelled, sparse, or highly unbalanced.

This can require experimentation with feature extraction, augmentation, sampling, transfer learning, model design, or alternative training approaches to determine whether reliable outputs can be achieved despite the available data limitations.

Applications can include:

• Sentiment analysis and predictive analytics where industry-specific terminology, limited labelled data, or rapidly changing language makes reliable classification difficult.
• Automated feedback systems that evaluate client progress in counseling, where available datasets may be limited, highly variable, or difficult to standardize.
• E-commerce applications where prediction or recommendation models must work with incomplete, uneven, or rapidly changing customer and product data.

High-Volume and Real-Time Data Processing

Some ML systems must process very large datasets or continuously changing information within strict performance requirements. Existing processing methods may not provide sufficient throughput, latency, or scalability.

Experimental development may involve alternative data transformations, streaming architectures, distributed processing methods, model optimization, or high-volume data pipelines.

Applications can include:

• Automated analysis of utility billing and solar irradiance data to estimate and validate solar energy output.
• Systems that analyze large volumes of equipment or process data to identify abnormal patterns or changing operating conditions.
• Advanced data processing systems that convert alarms, sensor readings, and operational records into meaningful equipment or asset states.

Model Training, Prediction and Classification

Standard models may perform adequately under controlled conditions but fail when exposed to noisy, incomplete, highly variable, or domain specific data. Development teams may need to experiment with alternative model architectures, training strategies, feature representations, validation methods, or combinations of techniques to achieve acceptable performance.

Applications can include:

• Sentiment analysis and predictive analytics for pharmaceutical social listening and market intelligence.
• E-commerce applications using behavioral, transactional, or product data to improve recommendations, predictions, or customer interactions.
• Predictive systems that analyze large and changing datasets to identify patterns, trends, or future outcomes.
• Automated feedback systems that evaluate client progress in counseling and support evidence based therapy decisions.

Generative AI and Natural Language Processing

AI systems working with specialized or domain-specific information may generate inconsistent, incomplete, or inaccurate outputs. Teams may need to investigate alternative model configurations, context construction methods, validation techniques, retrieval strategies, or system architectures to improve performance.

Applications can include:

• AI-assisted fire-safety auditing that interprets inspection information and supports identification of compliance or risk issues.
• Domain-specific AI assistants designed to interpret technical information and produce outputs requiring high levels of accuracy and consistency.
• AI-assisted cross-platform software porting, where AI is used to interpret, transform, and validate code or functionality across different technology environments.

Retrieval Augmented Generation

RAG systems can introduce their own technical challenges. Standard approaches may fail to retrieve sufficiently relevant information, particularly where documents are complex, inconsistent, highly technical, or distributed across multiple sources.

Experimentation may involve chunking methods, embeddings, ranking techniques, hybrid search, context-window management, validation methods, or retrieval architecture.

Applications can include:

• Industrial water treatment systems that retrieve maintenance, alarm, chemistry, and operating data to support process and intervention decisions.
• Oil and gas or manufacturing knowledge systems that retrieve context-specific information to help operators interpret abnormal conditions and determine appropriate actions.
• Safety critical knowledge systems that identify outdated, conflicting, or insufficiently validated information before supporting compliance, maintenance, or risk decisions.

Agentic and Multi-Agent Systems

As AI systems become more autonomous, new technical challenges arise around orchestration, tool use, context management, state, decision making, error recovery, and coordination between multiple agents.

Existing frameworks may produce loops, conflicting actions, inconsistent results, loss of context, or unreliable tool selection. Development teams may need to experiment with alternative agent roles, communication methods, routing logic, state management architectures, or control mechanisms.

Applications can include:

• Multi-stage AI workflows that extract information from technical documents, classify and analyze the data, cross-check results against business rules, and validate outputs before they are used in operational decisions.
• Franchise marketing systems that coordinate content generation, local recommendations, approvals, and location-specific actions across multiple stores.
• AI-assisted software porting systems that coordinate code analysis, transformation, testing, and validation across different platforms.
• Industrial operations systems that coordinate monitoring, diagnostics, maintenance data, and operator inputs to recommend and execute the next step in a troubleshooting workflow.

What Does Not Automatically Qualify

Using AI or ML technology is not, by itself, SR&ED.

Routine activities such as a straight-forward implementation of an established model, configuring an existing AI service, building prompts, or connecting an LLM through an API, would not generally constitute experimental development on their own.

AI and ML do qualify for SR&ED, where companies are pushing these technologies beyond standard implementations and adapting them to complex real-world environments.

Not sure where to start? We can help you identify and track eligible activities, optimize your SR&ED claim, and explore additional funding for costs not covered by SR&ED tax credits.

If this sounds like your experience, it’s worth a conversation before your filing deadline rather than after.

Our team brings decades of combined experience in successfully preparing and filing SR&ED claims for clients year after year. For assistance in preparing your submission and to position your application for approval contact EVAMAX Group to help you with the process. You may also email us at info@evamax.com or call 1-877-711-7733.