Generic Foundation Models
Learn from public internet data
General knowledge
Prompt-dependent
Higher infrastructure costs
Difficult to audit
Can hallucinate beyond business rules
Purpose-Trained Small LanguageModels for Enterprise AIPurpose-Trained SmallLanguage Models forEnterprise AI
Generic foundation models know everything, but not your business. Shunya builds Custom Small Language Modelstrained exclusively on your workflows, terminology, and enterprise knowledge to deliver higher accuracy, lowerlatency, and more predictable AI in production.
10x smaller, 3x faster inference than general-purpose LLMs
10x
Parameters per use-case model
3x
Faster Inference
94%
Accuracy Retention
Zero
Data Leakage
Why generalist models fail regulated workflowsWhy Generalist Models FailRegulated Workflows
Generic AI Is For Everyone. Yours Should BeBuilt for Your Business.Generic AI Is For Everyone.Yours Should BeBuilt for Your Business.
Shunya Custom SLMs
Trained on enterprise knowledge
Purpose-built for specific workflows
Grounded in business logic
Lower latency
Lower infrastructure costs
Predictable enterprise behavior
What the model does
What Makes a Custom Small Language Model Different?
Every Custom Small Language Model is trained around your organization'sLanguage, workflows, and decision-making processes. The resultis faster, more accurate, and more predictable AI builtspecifically for your business.
Capability · 01 / 04
Enterprise Vocabulary
Learn your products, customers, policies, industry terminology, and internal language to improve understanding from the first interaction.
Efficiency benchmarks
Built for Production, Not Just Proof of Concept
Purpose-trained narrow models don't just cost less to run, they cost less to train, and pack more users onto the same hardware.
Metric
Shunya Custom SLM
Public / Big-Tech Reference
Gain
Training compute per production model
~40 GPU hours
10,000+ GPU hours
~250× more efficient
Inference density, single GPU (L4)
240+ concurrent users
~15 concurrent users, industry typical
~16× density
Inference cost, equivalent workload
Baseline
Cloud-only big-tech reference
~20× lower
Deployment speed, contract to production
72 hours
Weeks to months, typical enterprise AI
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How an engagement starts