Advantages for Businesses Choosing Small, Specialized Language Models (SLM) over LLMs to automate business processes

Introducing Our Author

Venky Verrapotu, CEO and Co-Founder at 4CRisk has over 20 years of experience in building high performance GRC technology platforms and more importantly onboarding hundreds of customers onto the platforms.  In this blog he carefully explains the differences between Small Specialized Language Models and Large Language Models (LLM) to automate business processes

Organizational Concerns in Adopting AI Large Language Models (LLMs)

Organizations are now strategizing how to adopt AI Large Language Models (LLMs) for many use cases, but are running into roadblocks as they grapple with the need for:

• Greater Security and Privacy: Does our enterprise information leave our virtual walls? ‍
• Domain-Specific Knowledge: Is this model trained on the corpus we need to use? ‍
• Hallucinations, Drifting, and Data Bias: How accurate is this, anyway? What if I give my customer the wrong information? ‍
• Explainability and Transparency: Do we know how this works? Can we verify responses and results? ‍
• Use Cases: Will this technology support all our needs? We don’t need to boil the ocean, but we need help with security, privacy, policy, compliance, risk, and other highly manual tasks ‍
• Technical Capabilities: Do we need Conversational AI, RAG, Parsing, NLP, complex reasoning, simple text classification or refined precision parsing for accurate matching or Information Retrieval? What are the differences? ‍
• Cost to Scale: Do we have the skills and budget to scale this model? ‍
• ROI of high priority use cases: What are the specific use cases, benefits, costs, and ROI for this project?

Choosing the right language model for your enterprise requires balancing all these factors. While public LLMs can deliver strong, out-of-the-box performance for a wide range of applications, on closer inspection, they aren’t always the right choice for automating Risk and Compliance processes. Smaller, specialized models can be cheaper, and if pre-trained, on a closed domain, like 4CRisk’s Compliance models, they can be the safe and fast choice for many compliance and risk use cases.

A comparison between Private Specialized Language Models (SLMs) and Public Large Language Models (LLMs):

There you have it: private, closed-domain Small Language models, such as those deployed in 4CRisk products, are the safest, all-around choice for enterprises seeking to leverage the transformative power of AI in their risk and compliance programs.

What sets 4CRIsk.ai apart?

• Speed, Accuracy, and data privacy are paramount for risk and compliance customers.
• Unlike other AI tools that use public LLM, 4CRisk.ai has built language models that are pre-trained on carefully curated regulatory content and fine-tuned to automate manual tasks in the Risk and Compliance domain.
• We have addressed the data bias, IP infringements, and hallucination issues that are typically associated with LLM with our approach.
• Our model sizes are smaller, meaning they have a significantly lower carbon footprint than an LLM and can be deployed in a dedicated environment. ‍
• Customer data is not used to train our language models, as we use synthetic data generation techniques to tune our models.
• Our customers benefit from robust Security and Access Control enforced based on business-defined roles and responsibilities. ‍
• Customer data never leaves the customer’s virtual walls – it’s never in the public domain.

Check out these related blogs and resources  

• https://www.4crisk.ai/post/ai-game-changer-specialized-language-models-the-safest-alternative-to-llms-for-regulatory-risk-and-compliance-programs
• ‍https://www.4crisk.ai/post/ai-and-the-humans-how-2025-will-be-the-year-of-smarter-teams-not-just-smarter-tech ‍
• https://www.4crisk.ai/specialized-language-models