Specialist tasks
10
analysis + generation
Route every coding request through a specialist local model stack.
SLM Router turns a single chat surface into a controlled multi-workflow system: Analyst classification, task-specialist execution, live streaming, optional translation, and local cost discipline without cloud dependencies. The result is a developer loop optimized for speed, privacy, and per-task precision.
01 Use your own localhost Ollama per user
02 Analyst-first classification
03 Streaming with continuation
04 Per-task model mapping
Cloud API cost
$0
local inference via Ollama
Data residency
Local
requests stay local by default
Routing approach
Task-scoped
analyst classification + specialist execution
Why this SLM approach works
For developer workflows in this app, routing to smaller specialist models gives better control, lower cost, and faster turnaround than sending every request to one large generalist model.
SLM Router benefits
- Task-scoped prompts reduce noise from unrelated instructions.
- Smaller local models provide faster iteration for coding loops.
- Per-task model mapping lets you optimize quality vs speed explicitly.
- No cloud lock-in for core workflows and predictable operating cost.
Generic LLM tradeoffs
- Single generalist model handles every task with less specialization.
- Higher prompt/context overhead for routine developer operations.
- Cost grows with usage and larger context windows.
- Less transparent routing logic and weaker per-task control.
Platform architecture
Structured into three planes so routing, execution, and UX evolve independently.
Routing Intelligence
Control Plane
Analyst pre-analysis detects language, intent, diff context, and framework signals before model routing.
Task Specialists
Execution Plane
Each task runs on a dedicated specialist model profile with streaming responses and token-aware continuation.
Operator UX
Experience Plane
Per-task conversation history, live progress visibility, optional translation, and instant cost comparisons.
Operational workflow
End-to-end lifecycle for each request, from raw input to final enriched output.
01
Input + context
Paste code, error traces, diffs, or attach files. The request is normalized into a structured payload.
02
Analyst classification
A lightweight Analyst infers language and workload traits to reduce misrouting and improve output quality.
03
Specialist execution
The selected specialist model handles only its domain task, instead of using a generic one-size model.
04
Streaming + continuation
Responses stream in real time and can auto-continue for long outputs so sessions do not truncate mid-answer.
05
Post-processing
Optionally translate prose output and inspect local-vs-cloud cost estimates for each interaction.
Task catalog
Live model labels reflect your current local configuration.