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Small Language Models

The Rise of Small Language Models (SLMs)

The Rise of Small Language Models (SLMs)

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Over the past year, large language models (LLMs) have dominated the AI conversation, but a quiet revolution is happening behind the scenes. Enterprises are shifting their focus from experimentation to implementation, and in that transition, Small Language Models (SLMs) are emerging as the practical choice!

From AI Hype to Real-World Adoption

Between 2023 and 2024, many enterprises experimented with large, general-purpose AI services from major LLM providers. While these tools proved powerful, organizations quickly realized that relying on third party platforms introduced challenges around data privacy, regulatory compliance, and cost control. By 2025, the narrative has shifted from experimentation to practical integration. Businesses are now seeking AI solutions that are tailored to their needs, systems that fit neatly within existing infrastructure, align with compliance requirements, and deliver measurable ROI. 

Smaller language models (SLMs) have become the preferred choice for companies looking to embed AI directly into their operations. Unlike large, externally hosted LLMs, SLMs can often be deployed and managed within a company’s own environment. They offer greater control over data privacy, regulatory compliance, and ongoing operational costs. For example, a regional bank adopting a 1B-parameter SLM to handle customer query automation internally, reducing data transfer to third-party clouds, cutting operational expenses, and maintaining full control over customer data privacy and compliance.

What’s Driving the Shift Toward SLMs

SLMs are smaller and more efficient, allowing organizations to run them within their corporate infrastructure budget without the need for massive, AI focused data centers. This approach delivers faster performance, reduced dependency on external providers, and stronger control over sensitive data. For instance, a manufacturing company recently implemented an SLM to automatically summarize maintenance tickets directly on its local servers, improving response times by 40% and reducing downtime across production lines.

Security and compliance have also become decisive factors. Many industries, especially healthcare, finance, and government, need data to stay inside their walls. With SLMs, they can keep sensitive information within their own networks. A healthcare provider, for example, can fine-tune a local SLM on anonymized patient data to securely generate visit summaries while maintaining full compliance with HIPAA standards.

Finally, cost control is a major motivator. The usage-based pricing of commercial LLM APIs can quickly escalate, especially for enterprises running thousands of queries daily. SLMs provide a more affordable alternative, offering comparable intelligence for many operational tasks at a fraction of the cost. One logistics company saw its monthly AI expenses drop by 60% after moving from a commercial API to a lightweight, open-source SLM hosted privately, demonstrating that efficiency and economy can go hand in hand.

Where SLMs Are Making the Biggest Impact

SLMs are proving their value across several key business functions. At customer experience, they power real-time chatbots and knowledge assistants trained on company data, providing faster and more relevant responses than generic models. In operations, SLMs automate document processing, report generation, and task triage, all while running efficiently on internal infrastructure. This helps teams streamline workflows without relying on external systems. For analytics and insights, SLMs are being embedded into BI dashboards to enable contextual Q&A and analysis without exposing proprietary data, giving decision-makers secure, on-demand access to insights. And in IT and security, SLMs act as internal AI copilots that assist staff with coding, anomaly detection, and compliance documentation, all within tightly controlled, secure environments.

How CCG Accelerates Enterprise Adoption

Many organizations understand why they want to adopt SLMs, but struggle with how to deploy them effectively. CCG helps identify and validate specific, ROI-driven use cases for AI and automation. Our team develops and aligns the optimal technical approach, sourcing the right technology partners as needed to ensure seamless execution and measurable outcomes..

To maximize performance, we also oversee implementation and help ensure the application is properly managed, maintained, and optimized for long-term success. Adoption is accelerated this way by ensuring that there is good data on which to base the SLM! Finally, we assist in building governance frameworks that promote AI compliance and risk management.

In one example, CCG supported a mid-size financial institution in deploying an on-prem SLM to automate Know Your Customer (KYC) document reviews. The result was a 70% reduction in turnaround time while maintaining complete data control and regulatory compliance!

Ready to make AI work for your business? Connect with us to learn how purpose-built SLMs can help you move from experimentation to real-world impact!

Building Custom Automations: Crawl → Walk → Run Framework

Building Custom Automations: Crawl → Walk → Run Framework

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Why Crawl, Walk and Run?

Businesses today are under increasing pressure to adopt AI and automation. Leaders see the potential for faster operations, smarter workflows, and better customer experiences. However, many initiatives fail because they are rushed, unstructured, or lack a solid foundation.

Employees often experiment with “shadow AI” tools that introduce compliance and security risks. IT teams struggle to keep up with the demand for AI while ensuring governance and protection of sensitive data. Leadership, eager for results, may invest in large-scale automation before the groundwork is in place, which leads to low adoption and wasted resources.

The “Crawl → Walk → Run” framework provides a phased roadmap for adopting automation responsibly. Each stage builds on the last: Crawl focuses on laying the foundation, Walk on streamlining processes, and Run on scaling with custom builds.

Crawl: Establishing the Foundation

The objective is to build trust, introduce AI safely, and eliminate shadow AI within the organization. By introducing a company AI assistant, employees gain access to a sanctioned, user-friendly tool that supports everyday tasks such as summarizing documents, drafting responses, and answering company FAQs. This approach delivers immediate value without requiring deep integrations or extensive technical effort. Compliance and visibility are built in, every interaction is logged to create a clear audit trail, and all sensitive information remains within approved environments to ensure data protection!

In addressing the risk of shadow AI, employees no longer need to rely on external tools like ChatGPT or other consumer-grade assistants. Centralizing AI access mitigates data leakage risks and ensures consistent guardrails. Beyond security, the initiative fosters cultural benefits: employees develop confidence and AI literacy in a safe setting, while leaders gain visibility into AI usage patterns and emerging needs. Ultimately, this establishes a secure, trusted foundation for AI adoption, achieving early wins, reducing risk, and gathering valuable insights for future opportunities.

Walk: Automating Processes

The purpose of this stage is to expand beyond individual productivity and begin streamlining team and organizational workflows. This marks a shift in focus, from supporting specific employees to removing bottlenecks that slow down cross-departmental work. Here, automation evolves from being a convenience to becoming an operational necessity! 

Practical use cases span functions. Within HR, it can automate onboarding, document collection, and answering employee questions. In the Finance sector, it can help handle invoice approvals, expense processing, and regulatory reporting. In Operations, streamlining ticket routing, inventory tracking, and scheduling; and at Customer Support, managing first-line inquiries, ticket triage, and maintaining the knowledge base. The benefits are clear that repetitive, rules-based tasks are consistently managed at scale, freeing employees to focus on strategic work. 

As a result, workflows become faster, more predictable, and less prone to error. The business impact is tangible, productivity gains are measurable, cost savings are realized, and both customers and employees experience faster, more reliable service. Ultimately, AI becomes embedded into daily business operations, proving its ROI and building momentum for more advanced initiatives.

Run: Scaling with Custom Builds

The goal at this third stage is to move beyond operational efficiency and begin creating true business differentiation through AI. While off-the-shelf automation tools can deliver immediate value, they rarely provide a lasting competitive edge. Real advantage comes from custom AI solutions, built around an organization’s unique systems, data, and goals, that unlock innovation competitors cannot easily replicate. 

In this “Run” phase, companies develop bespoke AI workflows, models, and applications that integrate deeply into enterprise systems such as CRMs, ERPs, and data warehouses. AI is customized to meet specific compliance, regulatory, and customer requirements, and specialized models are trained using proprietary company data. Examples of such custom builds include a bank deploying real-time fraud detection powered by AI-driven signals, a retailer offering personalized promotions based on individual customer histories, or a logistics company predicting supply chain disruptions and automatically rerouting shipments. 

To scale effectively, organizations must establish strong governance and monitoring frameworks to maintain compliance, while continuously refining models based on real-world performance. Training and change management are equally vital to ensure adoption across the enterprise. The outcome is a transformation of automation from a support function into a strategic differentiator, one that fuels growth, strengthens resilience, and drives long-term innovation.

The Roadmap to Responsible Automation with CCG by Your Side

Moving too fast invites risk and moving too slowly risks being left behind! A phased, structured approach unlocks compounding value at every stage while keeping governance, security, and adoption in check.

CCG is the right partner to guide organizations through every stage of their AI and automation journey. With deep strategic and technical solutions, we provide the strategic guidance and technology advisory needed to navigate the complexities of infrastructure, integrations, and vendor selection, ensuring every automation initiative is both technically feasible and aligned with business goals. 

First, assess where your organization currently resides in the “Crawl → Walk → Run” continuum. Then, engage with CCG to map out the right next steps, whether that’s selecting your first AI assistant, designing process automations, or building bespoke models. Together, we can begin or accelerate the journey toward structured, scalable automation. Contact us today to unlock value at each phase while maintaining control, compliance, and alignment to business needs!

What Are Small Language Models (SLMs) and Why Enterprises Need Them

What Are Small Language Models (SLMs) and Why Enterprises Need Them

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Enterprises are quickly realizing that large language models (LLMs) aren’t always the best fit for every task. While powerful, they’re often expensive, resource-intensive, and difficult to control.

This is where small language models (SLMs) come in. These models are designed to be lighter, faster, and easier to deploy, making them highly practical for enterprise use cases. As AI adoption matures, SLMs are becoming a strategic tool for companies that want both efficiency and performance without the high costs of LLMs!

What Are SLMs?

SLMs are compact AI models designed to handle language-based tasks with fewer parameters than LLMs. Instead of aiming for broad, general intelligence, they focus on doing specific jobs well. They require less computing power, deliver faster responses, and can often be run on standard enterprise infrastructure. This makes them more accessible for businesses that don’t want to rely solely on heavy cloud setups.

How SLMs Differ From LLMs

LLMs often need clusters of GPUs and massive cloud spend, while SLMs can run efficiently on local servers or private cloud environments. Additionally, LLMs shine in open-ended, creative scenarios, but SLMs excel at speed, focus, and precision: qualities that matter most in enterprise operations. Models like Microsoft’s Phi, Mistral, and Meta’s LLaMA variants showcase how the AI landscape is shifting toward smaller, more specialized options.

SLMs vs. LLMs: Choosing the Right Model

LLMs should be used in broad, creative, and open-ended tasks where general knowledge is needed. SLMs should be used in predictable, structured, or compliance-heavy workflows where speed, security, and cost matter most. Many enterprises find value in combining the two by using LLMs for exploration and SLMs for execution.

Why Enterprises Need SLMs

SLMs offer clear advantages when it comes to cost efficiency and scalability. Their smaller size significantly reduces infrastructure and cloud costs, making it possible to scale AI across multiple teams or locations without ballooning budgets. They also deliver greater speed and agility. Faster response times are crucial for real-time applications like customer support, and because SLMs are easier to fine-tune, they can be quickly adapted for niche use cases within an industry.

Data security and privacy are another strength. SLMs can be deployed on-premises or in private clouds, giving enterprises full control over sensitive data. This approach also reduces dependence on third-party APIs that may expose information to external providers. When it comes to high performance on specific tasks, SLMs often outperform LLMs in narrow, domain-focused work. They excel in areas such as compliance reviews, automated internal support, or industry-specific workflows where accuracy matters more than creativity.

Finally, SLMs are far more energy efficient and sustainable. With smaller compute needs, they consume much less energy than their larger counterparts. This not only lowers costs but also helps companies meet sustainability goals and ESG commitments.

Enterprise Use Cases for SLMs

SLMs have a wide range of enterprise use cases. They can automate customer service chatbots with faster, more reliable responses, ensuring smoother interactions and are valuable for reviewing compliance and risk-related documents at scale, helping organizations handle large volumes of critical information more efficiently.

Another application is in building knowledge management and search tools that improve employee productivity by making information easier to access and use. SLMs can also power edge AI in IoT devices, retail kiosks, or field operations where resources are limited, making them practical in environments that demand lightweight solutions. They support everyday tasks like summarization, drafting, and workflow automation, all of which contribute to greater efficiency across teams.

How CCG Can Help Enterprises Leverage SLMs

We specialize in building and optimizing cloud, hybrid, and on-premises environments which is exactly the kind of setups where SLMs thrive. They help enterprises right-size infrastructure to balance performance and cost. With partnerships across major providers, we can help organizations navigate the crowded AI ecosystem to select the right mix of SLMs, LLMs, and supporting platforms. Our deep focus on security ensures that enterprises deploying SLMs in regulated industries (finance, healthcare, government) can maintain compliance while keeping sensitive data protected. Beyond deployment, we can guide enterprises to build governance frameworks to ensure AI is accurate, ethical, and aligned with business goals

From initial proof-of-concept projects to full enterprise-wide adoption, CCG guides companies through every stage of integrating SLMs into business operations. AI isn’t set-and-forget! Contact us today for ongoing support, monitoring, and optimization to ensure enterprises like yours continue to see ROI as AI models evolve!