Services
Development and deployment of highly efficient, domain-specific language models optimized for edge hardware with significantly lower latency, drastically reduced compute costs, and enhanced privacy. Sub-services include on-device SLM integration for IoT, low-latency Phi-3.5 edge deployment, mobile-first small language model application development for retail, and offline NLP for remote industrial sites.
Direct integration of small language models into mobile, IoT, and embedded devices, focusing on hardware-aware optimization for specific chipsets (e.g., Qualcomm Snapdragon, Apple Neural Engine) to enable fully offline, low-latency NLP without cloud dependency.
Custom training and distillation of domain-specific language models (e.g., for medical, legal, or industrial use) specifically for edge hardware constraints, prioritizing model size, inference speed, and accuracy over general capabilities.
Specialized service applying techniques like pruning, knowledge distillation, and INT8/FP16 quantization to shrink pre-trained SLMs for deployment on resource-constrained edge devices, balancing performance with memory and power limits.
Architecture and deployment of SLM systems for environments with intermittent or no connectivity (e.g., remote industrial sites, maritime, defense), including robust local inference, secure data caching, and sync strategies.
Engineering of ultra-low-latency (<100ms) inference pipelines for SLMs at the edge, critical for interactive applications like voice assistants, real-time translation, and live customer service in retail or automotive.
Securing SLM deployments on edge devices against physical tampering, model extraction, and adversarial attacks, implementing secure boot, encrypted model storage, and runtime integrity checks.
Deployment of SLMs on industrial gateways and PLCs to process sensor logs, maintenance manuals, and operator voice commands locally, enabling predictive maintenance and procedural guidance without cloud latency.
Integration of SLMs with Multi-access Edge Computing (MEC) architectures in 5G/6G networks, positioning intelligence at the network edge to serve ultra-low-latency use cases for smart cities and connected vehicles.
End-to-end service for managing SLMs on distributed edge fleets, including version control, over-the-air (OTA) updates, performance monitoring, and rollback strategies at scale.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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