Use case
Professional services
Knowledge workers spend hours on repetitive text tasks. Private SLMs take the volume off the desk so your team focuses on the work that actually needs judgment.
The problem
Consultancies, agencies, and back-office teams process large volumes of structured and unstructured documents: proposals, reports, invoices, client emails, and internal memos. Generic cloud AI raises data-handling concerns and adds per-query cost at scale.
Teams need automation that is fast, consistent, and operates within the boundary their clients and compliance teams expect.
Where an SLM fits vs. a larger private LLM
Most office automation tasks are well-scoped: extract fields from a form, classify an email, summarise a meeting note, convert a table to a report. A purpose-built SLM handles these faster and cheaper than a general LLM.
A private LLM adds value when tasks require broader reasoning, multi-step synthesis across long documents, or flexible instruction following that a smaller model cannot reliably cover.
- Invoice and purchase-order extraction with structured JSON output.
- Email triage and intent classification for client-facing queues.
- Report generation from structured inputs without manual formatting.
- Data entry automation from semi-structured sources like PDFs and forms.
How SLM-Works helps
We scope the automation, build the model, and integrate it with the tools your team already uses - on your infrastructure, not a shared API.
- Custom SLM development →
Task-specific models trained on your document types.
- SLM infrastructure →
Private serving for office automation workloads.
- Hybrid routing →
Route simple tasks to SLMs, complex steps to a private LLM.
- Agent orchestration →
Multi-step workflows for end-to-end document automation.
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