The push to deploy artificial intelligence across enterprise workflows has largely focused on increasingly capable large language models (LLMs). These systems generate content, interpret complex instructions, and power applications from customer support to software engineering.
Yet as organisations move from experimentation to production, the priority shifts: the fundamental question is no longer just how capable a model is, but how reliably we can evaluate, govern, and control its behaviour.
This dynamic is bringing smaller language models (SLMs) to the forefront. Purpose-built for targeted tasks and specific domains, SLMs handle discrete functions such as document processing, classification, information retrieval, and workflow automation. Instead of depending on a single, monolithic foundation model across an entire pipeline, enterprises can distribute tasks across architectures of varying sizes.
However, industry experts caution against a common misconception: model size alone doesn’t proxy for safety.
“A smaller model is not automatically a safer model. Parameter count alone is not a safety guarantee,” says Anuj Gupta, founder and chief executive officer of KiteFishAI. “A poorly trained smaller model can be just as problematic within its intended application as a much larger model.”
Narrower Scopes Enable Tighter Evaluation
The core operational case for SLMs lies in scope definition. General-purpose LLMs are designed to handle an open-ended spectrum of user prompts, making it nearly impossible to anticipate every failure mode or edge case. Conversely, a specialised model operates within tightly defined parameters.
“Smaller, specialised models can be easier to control because their capabilities and intended operating boundaries are more clearly defined,” Gupta explains. “When a model is trained for a specific domain or set of tasks, developers can build targeted evaluation datasets, test edge cases more extensively, and understand failure patterns more systematically.”
Sourav Bandyopadhyay, founder and chief scientist at Shunya Labs, underscores that task-specific boundaries are vital for rigorous evaluation. “You can only evaluate what you can define. A general model’s job is ‘anything’, so its test set is effectively infinite,” he notes.
In contrast, when an SLM classifies customer requests into specific intent buckets, the challenge becomes a closed problem. Teams can curate a representative held-out test set, evaluate accuracy per class, and trace distinct failure mechanisms.
Conventional academic benchmarks often fail to capture whether a system can execute a banking workflow, fetch proprietary records, or adhere to compliance policies.
Building specialised pipelines addresses this disconnect. For instance, KiteFishAI is developing custom architectures and pretraining pipelines for document intelligence, workflow automation, and enterprise search across banking, financial services, and healthcare—though it has not yet publicly disclosed named client deployments or audited production metrics.
Multi-Model Architectures and Independent Verification Layers
Rather than outright displacing LLMs, organisations increasingly deploy SLMs alongside them in tiered architectures. In these setups, smaller models function as targeted verification and control layers.
A high-capacity LLM can generate responses while dedicated SLMs validate outputs against retrieved context, filter sensitive information, or scan incoming queries for prompt injection. In agentic environments—where systems interact directly with databases and enterprise APIs—this separation of duties becomes critical.
“Enterprises do not necessarily need to use one large model for every function,” Gupta says. “They can build a multi-model architecture where specialised smaller models act as independent control and verification layers.”
Bandyopadhyay warns against letting decision-making models evaluate their own actions. “For agents, I would never let the same model that decides what to do also decide whether it is allowed to do it,” he states. “A small, fast policy model, or plain rules, sits between the agent and its tools. It checks every action against permissions and can be audited line by line.”
Crucially, an SLM should support policy decisions rather than act as the sole arbiter of security. Deterministic, identity-based infrastructure controls must ultimately govern access to underlying databases and critical tooling.
Engineering the Safety Lifecycle
Specialisation narrows exposure to unexpected behaviour, but it does not eliminate hallucinations, biases, or out-of-distribution failures.
“Nothing about being small makes a model safe. A small model trained on bad data is a small bad model,” Bandyopadhyay says. “The advantage is indirect: small models are easier to inspect, test, retrain, and keep inside your walls. Safety comes from what you do with that access.”
According to Gupta, robust enterprise safety requires engineering across the full lifecycle—encompassing data curation, model alignment, adversarial testing, and deployment safeguards. KiteFishAI’s internal frameworks, for example, evaluate models within their full application environment—including APIs, external tools, and retrieval pipelines—testing for prompt leakage, bias, and instruction drift.
Production testing must reflect real operating conditions. “What works is a held-out test set built from your real traffic, labelled carefully, and never used in training,” Bandyopadhyay emphasises. “Report accuracy per class, not one average, because the average hides the class that fails.”
This matters especially in markets like India, where real-world traffic involves heavy code-mixing, dialectal variation, and non-standard phrasing that generic benchmarks cannot simulate.
Balancing Latency, Cost, and Orchestration Overhead
Transitioning to smaller models involves distinct trade-offs. SLMs often struggle with intricate multi-step reasoning, expansive context windows, and highly ambiguous inputs.
Operational orchestration also introduces complexity. Shunya Labs found that relying solely on an ultra-lightweight classifier to route queries directly to an LLM led to excessive escalations and latency bottlenecks. To solve this, the firm implemented a three-tier architecture for its multilingual customer-care voice platform.
Under this setup, an internal 143 MB, two-layer model autonomously resolves approximately 90% of requests, achieving 97.7% reported accuracy on high-confidence predictions. By routing intermediate requests through a mid-sized model before hitting an LLM, Shunya Labs reduced LLM traffic to 3.9% and accelerated system throughput by 1.9x compared to using an LLM-based classifier alone.
While these gains reflect proprietary implementations, they demonstrate the practical value of distributed systems. Small models handle routine operations efficiently, reserving larger systems for requests that genuinely require open-ended reasoning.
Deploying fleets of specialised models shifts the engineering burden toward routing logic, drift monitoring, versioning, and fallback protocols. SLM adoption will hinge not on parameter counts alone, but on whether enterprises can verify that a specific model performs predictably under well-defined, audited conditions.