AI · Deep dive 05
Vendor-Neutral Integration
Anthropic, OpenAI, open weights on your own GPUs — we pick the model that fits the job and keep the integration swappable as the frontier shifts.
The scope
Architecture + implementation work to decouple your AI features from any single vendor. A routing layer, shared prompt abstractions, and the eval harness that lets you compare providers on your own data.
Does this sound familiar?
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You're tied to one AI vendor and would rather not be.
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When that vendor has an outage, your product has an outage.
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You'd like to try a cheaper model for the 80% easy cases but rewiring seems expensive.
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A better model came out last month and it'd take you a sprint to even evaluate it.
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Data residency requirements demand a self-hosted option you don't have.
The customer payoff
What changes
What you feel once it’s running.
A routing layer that picks the right model per task, per user, or per tenant.
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Evals that run across vendors — you compare apples to apples.
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Provider outages degrade gracefully instead of taking the feature down.
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Swapping providers is a config change, not a rebuild.
Phases
⏱ 4–8 weeks typicalHow Vendor-Neutral Integration actually runs.
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01
Map
Audit current AI calls, vendor by vendor. Identify where lock-in is accidental vs intentional."
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02
Abstract
Introduce a routing layer (often LiteLLM-style or custom) with shared prompt + tool-use abstractions."
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03
Benchmark
Run your eval suite across candidate providers. Numbers, not vibes."
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04
Deploy
Route traffic per task. Monitor cost + quality per route. Failover paths tested by dropping provider traffic in staging."
The hand-off
You'll have
What lands in your hands — every artefact, nothing hidden.
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Routing layer + provider abstraction in production
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Multi-vendor eval suite
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Cost + quality per provider, per task, dashboard
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Failover paths documented + tested
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Runbook for adding a new provider
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Self-hosted option evaluation (if warranted)
Common questions
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Q·01 LiteLLM / Portkey / custom?
Depends on scale + feature needs. LiteLLM is a great starting point. Portkey for managed + enterprise. Custom when your routing logic needs specific business rules."
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Q·02 Can we save money by routing?
Usually yes — some tasks don't need the flagship model. Easy-classification tasks on cheaper models, nuanced generation on flagship. We quantify before and after."
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Q·03 What about self-hosted models?
Valid for data residency or very high volume. We'll evaluate Llama / Mistral / Qwen on your eval suite and tell you honestly whether the ops cost is worth it."
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Q·04 Does this work with agentic systems?
Yes — the routing layer sits below whatever agent framework you use. Your agent still calls 'the model'; routing decides which model actually runs."
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Q·05 How often should we re-evaluate?
Quarterly for most. Major model releases (every 3–6 months) are the natural trigger to re-run the eval suite and update routing."
Ready to start
Stop betting on one vendor.
Two-day audit of your current AI call patterns, honest map of where lock-in is costing you, clear plan. Let's see what's worth decoupling.
Start a routing engagementThe wider map
Every service page at a glance.
Each link below opens a dedicated page on that specific piece of one of our four service pillars. Jump sideways — different service, same way of working.
Digital Product Strategy
Service overview →Web & Mobile Development
Service overview →Business Automation
Service overview →AI Integration
Service overview →- 01 AI Opportunity Mapping
- 02 AI-Driven Product Features
- 03 AI-Powered Automation
- 04 Evaluations, Guardrails & Observability
- 05 Vendor-Neutral Integration — you’re here