DigiEdze AI · Custom LLM & SLM Engineering
Use the Right Model, Own the Right Intelligence
Model choice is an architecture decision with cost, latency, sovereignty and lock-in consequences. We design a model strategy, often a portfolio, routed by task rather than a single vendor bet.
- model selection
- open-weight LLMs
- SLMs
- private models
- specialised domain models
Reference architecture
- 06Task classification
- 05Sensitivity & residency policy
- 04Model router
- 03Small models · Open-weight models · Hosted frontier models
- 02Adapter and prompt registry
- 01Cost and quality telemetry
The Challenge
Impact for Business When Gone Wrong
One frontier model is used for every task, including the ones a small model would do better and cheaper.
A single vendor dependency becomes a pricing and continuity risk with no migration path.
Sensitive workloads are served by endpoints that cannot meet the data boundary they require.
What We Build
Custom LLM and SLM Capability Grid
Model Selection Framework
Candidates evaluated on your tasks, data, latency, and cost targets.
Open-weight strategy
Where open-weight models give equivalent quality with ownership, portability and a fixed cost base.
Small language models
Compact models for classification, extraction and routing — the high-volume work that quietly dominates inference bills.
Routing layer
Task complexity and sensitivity decide the model, with fallbacks and cost ceilings enforced at the router.
Portability by design
An abstraction that makes swapping a model a configuration change rather than a rewrite.
Private model hosting
Serving inside your perimeter where the data boundary requires it.
How it works
Technology Wheel in Implementation Cycle
Our LLM and SLM engineering layer is where model choice gets operationalized through evaluation and deployments that serve every need of what enterprise AI is aimed at.
- Llama, Falcon and comparable open-weight families
- Sarvam and Indic-capable models
- Quantisation toolchains
- Inference servers
- Model gateways
Practical Approach
Industries We Tailor LLMs and SLMs
Government
Open-weight models served in-country, with no dependency on a foreign inference endpoint.
Retail & CPG
A small model handles catalogue enrichment volume; a frontier model handles the exceptions.
Technology
Routing by complexity keeps quality on hard tasks without paying frontier rates on easy ones.
Outcomes
What We Measure
Blended with intelligence, in every LLM and SLM engineering engagement, we ensure that the following outcomes are fulfilled.
- Blended cost per thousand requests
- Quality parity on held-out tasks
- P95 latency
- Vendor concentration reduction
Questions
Custom LLM & SLM Engineering, Answered Directly
Are open-weight models good enough for enterprise work?
For the majority of enterprise tasks — extraction, classification, summarisation, retrieval-grounded answering and routine tool use — yes. Reserve frontier models for genuinely hard reasoning, and prove the boundary with evaluation rather than assumption.
What is a model router?
A service that inspects each request's task type, sensitivity and complexity, then dispatches it to the appropriate model under cost and residency policy — with fallback if the primary is unavailable. It is the single highest-leverage component for AI unit economics.
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Design Your Model Strategy
Let’s have a direct conversation with an architect who can build this for you. Address your challenge with the LLM and SLM systems we build for your business.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT