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SLM-Works

Service

Hybrid AI routing

Pain

Using one model for every request wastes money on easy work and overloads latency-sensitive paths - while using only the smallest model starves tasks that need broader reasoning.

Outcome

We help you design a routing layer that classifies incoming work, sends repetitive or narrow tasks to SLMs, escalates complex or high-stakes steps to larger private LLMs, and records what happened for tuning and compliance.

Differentiator

Policies stay explainable: thresholds, fallbacks, and human review hooks are documented for security and architecture reviews - not hidden inside a black-box router.

Layered SLM and LLM design

SLMs excel at high-volume, well-scoped patterns - classification, extraction, short answers. Larger LLMs add value when tasks need multi-hop reasoning, rare edge cases, or wider world knowledge - still inside your boundary when deployed privately. Hybrid routing makes that split explicit instead of accidental.

Routing model

Flow diagram

The routing figure uses an accessible SVG with a caption below; connector motion respects reduced-motion preferences.

Requests enter classification; policies send most traffic to an SLM for cost and speed, with escalation to a larger private LLM when rules or confidence scores require it. Arrows represent logical flow - your deployment may use a gateway, mesh, or sidecar pattern.

Typical sequencing

Hybrid routing often lands after you have at least one SLM or private LLM serving reliably and observability in place. It does not require waiting for every upstream project to finish - we can scope a thin routing pilot on a single use case.

Multi-step workflows

When routing spans tools, schedules, and long-running agents - not just model choice - see Agent orchestration for the AgentWorks narrative. This page stays focused on SLM versus LLM routing policy.

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Related services

From our insights

Frequently asked questions

Practical answers for technical buyers; validate resale, SLA, and capacity wording with legal and sales before public launch and paid campaigns.

How is this different from agent orchestration?
Hybrid routing focuses on choosing which model serves a request or step. Agent orchestration (AgentWorks) adds multi-step workflows, tools, and schedules on top - see the agent orchestration service page for that narrative.
Does routing add latency?
A lightweight classifier adds milliseconds to low single digits in typical designs; we size the path to your SLOs and can cache classifications for sticky sessions where policy allows.
What happens when the SLM is uncertain?
You define the fallback: escalate to a larger model, return a safe default, or route to human review. Each path is logged so you can tune thresholds without guessing in production.
Can we route to a public API as well as private models?
Technically yes, but many regulated teams prefer to keep traffic inside approved boundaries. We document data flows explicitly; mixing public APIs requires governance sign-off.
How do we know the router is correct?
Offline eval sets plus shadow mode: run candidate routes alongside production without serving their outputs until metrics prove the switch.
Who deploys the router?
Usually your platform team with our reference patterns - sidecar, gateway, or service mesh integration depending on your stack. The statement of work names owners.
Where should we start a PoC?
Pick one high-volume API or workflow with clear success metrics, baseline cost and latency, then add routing rules for a subset of traffic before full rollout.

Design routes your finance and security teams can defend

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