Lead with smarter AI, not bigger models: Centific builds for fit

For years, AI conversations have revolved around scale: bigger models, more parameters, and larger infrastructure. But, the reality for most enterprise leaders is far less abstract. They’re working with limited compute, noisy data, unpredictable environments, and fragmented systems—trying to make AI work in places that don’t look anything like a cloud lab.

What we’re seeing now is a shift away from chasing size and toward building for fit. Enterprises need models that understand the problem, operate efficiently on their infrastructure, and deliver results they can trust. That’s not a step back from innovation. It’s a pivot to usability.

Centific fits squarely into this need. Rather than competing on raw scale, it has built its strategy around specialized language models/ small language models (SLMs)—models trained on domain-specific data, optimized for constrained environments, and designed to run where decisions are made. These aren’t stripped-down large language models (LLMs). They’re purpose-built systems that prioritize fit over flash.

The old AI battleground was scale; the new one is usability

General-purpose LLMs created a wave of enthusiasm, but their real-world utility in the enterprise is hitting friction. These models are compute-heavy, context-light, and slow to adapt. More critically, they’re often divorced from the operating environments they’re meant to serve.

The enterprise challenge isn’t just about model performance but how that model interacts with everything else. The real constraints are latency, cost, compliance, infrastructure diversity, and fragmented data ecosystems.

What’s needed now isn’t just more powerful models, it’s a rethinking of the full stack—one where data, models, and deployment are designed cohesively for the environments they’ll run in. The shift isn’t away from ambition. It’s a step toward applicability (see Exhibit 1).

Exhibit 1: The new battleground for AI fit

Source: HFS Research, 2025

Smaller, specialized models don’t do less—they do what matters

SLMs are designed with this new reality in mind. These are domain-trained AI models built to run efficiently on constrained infrastructure, often a single GPU. They’re tuned for specific tasks, environments, and user contexts. Unlike general-purpose LLMs, which are broad and compute-intensive, SLMs are smaller but sharper in focus. They are optimized for real-time inference, localized data, and meaningful outcomes within enterprise constraints.

What makes them different isn’t just size. They’re trained on curated, contextual data from the domain they’re meant to serve. They’re built with purpose, not just power.

This also makes them more adaptable and cost-effective. Because they’re designed to perform specific jobs in specific contexts, they can be fine-tuned and deployed faster, reducing training overhead and operational risk. Rather than over-engineering for generality, SLMs narrow the focus and get closer to how work is done.

Centific’s approach is grounded in building AI for the world as it is, not as it should be

Centific is leaning fully into this shift. Its strategy is anchored in SLMs and designed around usability, modularity, and trust. The company has built a platform that allows enterprises to deploy these models across a mesh of infrastructure, from core to edge to far edge, with governance built in from the start.

Its AI Data Foundry platform streamlines everything from data ingestion to model training, testing, deployment, and governance. Data Studio supports contextual labeling and human-in-the-loop review. Safe AI Studio enables stress testing and feedback loops. The Pentagon Framework embeds risk management and compliance from the start.

Crucially, Centific trains its models on what the enterprise actually looks like, not idealized datasets. CCTV footage from low-light stores, multilingual audio from body cams, and glitchy feeds from edge devices are the inputs that define operational AI.

This is what Centific calls ‘grounded intelligence.’ It’s not about chasing theoretical performance. It’s about learning continuously from the field. Model misfires, context drift, and behavioral anomalies aren’t just logged but rerouted into the training loop.

Real-world deployment: Turning body cam footage into decisions

Centific’s Verity platform shows what deploying AI in the real world means. In one implementation with a major transit authority, the goal was to help law enforcement make sense of chaotic, multilingual body and dash cam footage, without the hours of manual review. Traditional tools couldn’t handle these environments’ noise, context-switching, or infrastructure constraints.

Centific trained specialized language models on actual annotated footage—voices overlapping, shifting lighting, multiple languages—to transcribe, tag, and generate complete incident reports automatically. The models were also trained to detect early signs of escalation, using input from behavioral psychologists to flag patterns in group behavior before a conflict breaks out.

The same SLM-driven approach extends to financial risk. In partnership with AWS, Centific is training models that link cyber threat signals—login anomalies, dark web activity, device compromise—with transactional patterns to detect emerging fraud.

Underpinning all of this is Centific’s mesh architecture. It coordinates intelligence across far-edge devices, edge nodes, and core systems, ensuring AI doesn’t just observe, but acts where it’s needed. When an incident occurs, cameras reorient, visibility enhances, and operators get real-time alerts (see Exhibit 2).

Exhibit 2: Centific’s mesh architecture—far edge to core coordination

Source: HFS Research, 2025

The Bottom Line: The next wave of enterprise AI won’t be led by the biggest models. It will be led by AI models specialized for specific environments, constraints, and outcomes.

Centific has architected for this shift. The company shows that AI doesn’t need to get bigger through a modular stack, domain-specific SLMs, and a platform-first approach to data, governance, and deployment. It needs to get better at fitting the enterprise it serves.

For enterprises navigating complex environments, fit will matter more than scale. That’s the shift Centific is betting on.