Approach

How Digicane builds enterprise AI

Procurement-friendly delivery: model-agnostic, residency-aware, evaluation-backed, human-overridable.

Model-agnostic delivery
We orchestrate within your approved vendor list — Azure OpenAI, Anthropic, Gemini, or open-weight — without locking Digicane IP to one bill.
When API vs open-weight
APIs win for speed and quality on many tasks; open-weight or private hosts win when residency, cost at scale, or air-gap rules dominate.
India hosting options
Retrieval and app tiers can stay in India when contracts require localization — paired with access control and retention design.
Evaluation loops
Pilots ship with groundedness/refusal checks and override metrics before you widen automation.
Human-in-the-loop
Sensitive writes pause for approval. Audit IDs travel with every agent action.

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

Approach FAQs

Will Digicane force a single LLM vendor?+

No. We are model-agnostic and align to your procurement list.

Can we change models later?+

Yes — orchestration is designed so prompts, tools, and evals can retarget with controlled change management.

Do you support on-prem components?+

Where justified, retrieval and serving can sit in private networks; we scope during Assessment.

How do LLMs relate to efficient models or LFMs?+

LLMs remain the primary layer for complex agents and RAG. Efficient models and LFMs are used for edge, offline, and cost-sensitive workloads. See LLM vs LFM for the full comparison.