Consulting / Routing

AI model routing for every request.

We help teams route workloads using task, quality, latency, cost, availability, and policy requirements. A stable application interface supports multiple providers and controlled migration.

Talk to an AI architect See our approach
The routing decision Representative scope

Intent + Risk + SLO + Budget

A router turns workload context into an explicit model decision, then records the outcome for continuous improvement.

Quality threshold Latency target Policy boundary Unit economics
01Multi-model architecture 02Policy-aware decisions 03Linked to Project ARBR

Why this layer matters

Static model selection becomes an expensive product constraint.

Model selection balances task quality, latency, policy, availability, and cost. Routing records each decision so teams can review and improve those trade-offs.

01

Uneven workloads

Extraction, drafting, classification, reasoning, and high-risk decisions need different capabilities.

02

Hidden economics

Token cost alone misses retries, latency, failure handling, and the cost of a wrong answer.

03

Provider coupling

Product logic built around one model makes evaluation, fallback, and migration unnecessarily difficult.

What we deliver

One engagement, three connected workstreams.

01

Workload taxonomy

Segment requests by intent, complexity, risk, context size, and service-level expectation.

  • Traffic and use-case profile
  • Risk tiers
  • Routing signal inventory
02

Policy & benchmark

Define candidate models and measure them against task-specific quality, latency, and cost thresholds.

  • Golden task set
  • Model scorecards
  • Fallback and escalation policy
03

Integration & learning

Put the routing layer behind a stable contract and capture decision, response, and outcome telemetry.

  • Provider abstraction
  • Shadow and canary routing
  • Routing evaluation dashboard

The engagement

Each phase answers a production question.

The initial scope is narrow. Each phase produces working software and a reviewable deliverable for the next decision.

1

Profile

Understand traffic

Sample real requests and identify the meaningful decision boundaries.

2

Benchmark

Test candidates

Compare candidate models against representative tasks and agreed thresholds.

3

Route

Ship policies

The initial release uses explicit rules, fallback behaviour, and full decision traces.

4

Learn

Improve safely

Use outcomes and drift monitoring to refine policies and detect regressions.

What you leave with

Deployed software, test results, and an operations runbook.

The engagement includes implementation documentation and a defined handover.

01

Routing policy

A documented and testable decision system across model, risk, performance, and cost.

02

Provider abstraction

A stable application interface that supports comparison, fallback, and controlled migration.

03

Decision telemetry

Records showing why a model was selected and whether the result met the required threshold.

Typical building blocks

Model gatewaysSemantic routingPolicy enginesTracingCanary releasesCost telemetry
Read the model selection guide ↗

Open-source initiative

Explore Project ARBR

Project ARBR is Gyde's open-source initiative for adaptive request-based routing. It has its own website, product identity, and technical roadmap. Gyde provides the consulting and implementation services described on this page.

ARBR Visit projectarbr.org ↗

Questions

Before we begin.

How is this consulting service related to Project ARBR?

This page describes Gyde's routing consulting. Project ARBR is a Gyde-led open-source initiative with its own website, product identity, and documentation.

Do we need many providers before routing is useful?

No. Routing can begin with model tiers or versions from one provider, then expand when the benchmark and operating case justify it.

Can routing reduce quality?

Poorly evaluated routing can reduce quality. Every route therefore needs task-specific thresholds, fallback behaviour, and ongoing regression tests alongside cost controls.

Bring a defined business constraint

Bring us the workflow that is stuck.

We will define a focused engagement using representative data, real permissions, and measurable success criteria.

Talk to an AI architect