LLM Orchestration & Routing

Overview

What we deliver

We design orchestration layers that route prompts across multiple LLMs based on task type, cost, latency, and quality requirements.

We build LLM orchestration and routing systems that send each request to the model best suited for the job. Instead of locking your application into a single provider, we set up a routing layer that evaluates task complexity, context length, latency targets, and budget rules, then directs traffic to the right model. Our work covers provider abstraction, fallback logic, retries, and graceful degradation when a model is rate limited or offline. We integrate with OpenAI, Anthropic, Google, and open source models hosted on your infrastructure, and we add caching and batching where it makes sense. The result is an AI stack that stays fast, controls cost, and avoids vendor lock in. We document the routing rules and hand over a system your engineers can operate and extend.

Fit Check

Built for teams like yours

Who it's for

AI product teams
SaaS platforms with LLM features
Enterprises piloting multiple models
Customer support automation teams
Internal AI platform groups

Pain points we solve

Single provider lock in
Unpredictable LLM bills
Slow response times on complex queries
Outages from one provider taking down features
No fallback when models fail

What's included

Capabilities

Everything we cover in this engagement.

Routing rule design
Provider abstraction layer
Fallback and retry logic
Latency and cost benchmarking
Caching strategy
Model selection policies
Rate limit handling
Integration with existing APIs

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Audit

Review current LLM usage and traffic patterns.

02

Design

Define routing rules and provider mix.

03

Build

Implement the orchestration layer and adapters.

04

Test

Run load tests and validate fallback paths.

05

Deploy

Roll out to production with monitoring.

What you get

Deliverables & outcomes

What you get

Routing architecture document
Orchestration service code
Provider adapter library
Fallback playbook
Performance benchmark report
Operations runbook

Outcomes you can expect

Lower average cost per request
Reduced provider dependency
Faster median response times
Higher uptime for AI features
Clear operational visibility

Timeline

4 to 8 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

LangChain, LiteLLM, OpenAI, Anthropic, AWS Bedrock

KPIs we track

Cost per request, P50 latency, P95 latency, fallback rate, error rate

Client stories

What clients say

"

Our old site was a Frankenstein of three previous agencies. We gave them a hard launch date tied to a trade show and they actually hit it. 47 templates, full product catalog migration, no broken redirects on go-live day. Our previous vendor missed the same deadline twice. This time my phone stayed quiet on launch morning.

Marcus L.

"

Our SDRs were spending two hours a day copying lead data between Salesforce, Outreach, and a Google Sheet nobody owned. They mapped the whole flow, stitched it together in n8n, and added a dedupe step we did not even know we needed. Got 38 hours a week back across the team. The SDRs were the ones who pushed to expand it further.

Rebecca F.

Proof