LLM Integration Services
Adding a model to a product is easy. Adding it so it survives provider outages, price changes and your next great idea — that is the engineering.
Every provider — OpenAI, Anthropic, Google, the open-weight options — has good models and a reason you might need to leave them: pricing shifts, rate limits, a capability another vendor suddenly does better. A well-built integration treats the model as a swappable component behind your own interface: provider-agnostic calls, graceful fallbacks, cached responses, and cost telemetry from day one.
This practice integrates LLMs into existing products — WordPress plugins, Shopify apps, Node.js and PHP applications, internal tools — as features your users touch: generation, summarisation, classification, extraction, search. The integration ships with prompt versioning, so behaviour changes are deliberate rather than accidental, and with a switch to disable any AI feature without taking the product down.
The honest limits: I will tell you when a feature does not need a model. Some "AI features" are a lookup table wearing a costume, and shipping them as model calls adds cost and failure modes for nothing. The audit that starts every integration engagement says which parts of your wishlist are real model work and which are ordinary engineering — that answer saves more money than the build costs.
What the engagement covers
- Model-agnostic integration layer — swap or add providers without touching product code
- Fallback chains: automatic provider failover so an outage degrades gracefully, not fatally
- Prompt versioning and regression checks — behaviour changes are releases, not surprises
- Cost controls: caching, routing cheap tasks to cheap models, per-feature budgets and alerts
- Feature kill-switches — any AI feature can be disabled without redeploying the product
- Integration into your existing stack — WordPress, Shopify, Node.js, PHP, or internal tools
- Documentation and handover — your team owns the layer, not a black box
Honest limits
What this is deliberately not.
Not this: Wrapping a raw model key in a page and calling it a feature — the wrapper is where the value is
Not this: Training or hosting your own foundation model — API and open-weight integration is the honest scope here
Not this: AI features that exist for the badge — if the demo impresses but users never touch it again, that is a no
Not this: Unversioned prompt changes in production — if it affects behaviour, it ships through the same discipline as code
Questions · LLM integration
Asked before building.
Per feature, not per company: the model is chosen by task, latency need, cost envelope and data sensitivity — and the architecture keeps the choice reversible. Open-weight self-hosting earns its keep only when privacy or volume demands it; otherwise it is operational burden for bragging rights. The integration doc records why each model was picked, per feature.
Fallback chains route to the next provider automatically, and per-feature budgets flag cost drift before the invoice does. A provider change becomes a configuration update, not an emergency rewrite — that resilience is the core deliverable of this service.
Yes — that is the standard engagement: a capability added to a product you already run, on the stack it already uses. The feature ships behind a flag, disabled by default, and your team can turn it on per audience as it proves itself.
Cache what repeats, route what is simple to cheap models, budget per feature, and alert on drift. Most runaway bills come from unbounded generation — no output limits, no caching, no per-user caps. All three are set in the build, and the cost dashboard shows actuals against plan from week one.
Related: all AI services · automation services · the vetting guide.
Also in this section
AI Chatbot Development · AI Agent Development · RAG Development · LLM Fine-Tuning · AI Consulting & Audit · AI Search Visibility
Scoping something in this space?
Written scope within two business days — deliverables, milestones, timeline, terms, price at the bottom. Compare it against anyone.