Hybrid AI
Large models where intelligence matters. Efficient models where speed, privacy, and cost matter.
Digicane AI routing pattern
Simple requests can stay on an edge model. Medium workloads use private/local SLMs. Complex work escalates to an enterprise LLM and agent connected to your systems.
An engineering pattern — not a black-box product claim
We design and build this routing with your constraints. We do not advertise a proprietary Digicane LFM or claim one model class replaces another.
Hybrid routing
Digicane AI routing pattern
Customer request → Digicane-designed router → the right model tier → AI agent → business systems.
01 · Simple
Edge / efficient model
02 · Medium
Private / local SLM
03 · Complex
Enterprise LLM → AI Agent → systems
Multi-model AI
LLMs and efficient models solve different jobs
Digicane is model-agnostic. We combine LLMs, SLMs, and efficient foundation models (LFMs) — we do not claim LFM replaces LLM.
Deep reasoning
Large Language Models (LLMs)
- Complex agents
- Enterprise RAG
- Analytics & synthesis
- Multi-step copilots
- · Maximum reasoning quality
- · Cloud or private VPC
- · Higher compute cost
- · Best for hard problems
Private / local
Small Language Models (SLMs)
- On-prem copilots
- Regulated automation
- Local assistants
- Cost-sensitive chat
- · Strong enough for many workflows
- · Easier private hosting
- · Lower latency at scale
- · Good mid-tier fit
Edge / offline
Efficient models / LFMs
- On-device AI
- Mobile & IoT
- Offline intelligence
- Camera / field inference
- · Low latency
- · Lower inference cost
- · Privacy at the device
- · Constrained compute
Want the full comparison? LLM vs LFM · Hybrid AI · Edge AI
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Related capabilities
FAQs
Does Digicane replace LLMs with LFMs?+
No. Digicane is model-agnostic. LLMs remain the primary layer for complex enterprise agents and RAG. Efficient models and LFMs are used where edge, offline, latency, or cost constraints dominate.
Do you sell a proprietary Digicane LFM?+
No. We select and optimize foundation models — including efficient/edge models — based on accuracy, latency, cost, privacy, hardware, and workload. We do not claim a Digicane-owned LFM.
When should we use an efficient or edge model?+
When the workload must run on-device or offline, needs very low latency, or must stay local for privacy or bandwidth — for example cameras, mobile apps, vehicles, or remote industrial sites.
Design a hybrid AI path
Map simple, medium, and complex workloads with Digicane engineers.