Use case
Financial services
High-stakes text workflows need low latency, clear audit trails, and boundaries that match your data policy - not a one-size public API.
The problem
Operations and risk teams drown in alerts, clauses, and policy documents. Generic cloud models create friction with procurement, residency rules, and explainability expectations - while doing nothing to guarantee your prompts stay inside approved systems.
You need repeatable automation for narrow tasks (classification, extraction, triage) without betting the bank on a single frontier model for every line of text.
Where an SLM fits vs. a larger private LLM
Compact SLMs excel at high-volume, well-scoped patterns: screening queues, metadata tagging, and clause spotting against playbooks you define. They keep per-token cost and latency predictable when traffic spikes.
Larger private LLMs add value for rarer, messier cases - long agreements with unusual structure, or steps that need broader reasoning - still inside your network when deployed privately. Hybrid routing makes that split explicit instead of accidental.
- Default repetitive work to an SLM; escalate only when policies or confidence scores say so.
- Log inputs, outputs, and model versions per route for audit-friendly operations.
How SLM-Works helps
We deliver the model and infra stack - not legal or compliance sign-off for your jurisdiction. Typical engagement surfaces:
- Custom SLM development →
Domain-tuned small models for your documents and policies.
- SLM infrastructure →
Run inference on-prem or on contracted GPU with clear operational ownership.
- Hybrid routing →
Route between SLM and private LLM tiers under explicit rules.
- Private LLM deployment →
When breadth of reasoning matters alongside your boundary requirements.
- All services →
Overview of the full SLM-Works offering.
Related insights
- On-prem SLM inference vs rented GPU cloud: how to choose
The decision is not ideological—it is a bundle of networking, procurement, incident response, and unit economics that changes with your traffic shape.
- SLM vs LLM in the enterprise: a practical decision framework
Use a scorecard—not slogans—to decide when a specialized small model should own a workflow versus when a larger private LLM must stay in the loop.
See how this maps to your stack and governance