You have an application. Now you need AI that fits it.
LLM API integration for existing products
Add language-model features to an existing web or mobile product: structured extraction, summarization, assistants, and tool use, with the application logic needed to make them dependable.
The situation
Calling a model is one step. A product also needs authentication, reliable input handling, validation, billing boundaries, cancellation, and a useful experience when the model or network fails. I build that surrounding application layer as part of the integration.
This is a focused engagement for an existing application. Examples include converting unstructured text into validated records, summarizing a document, helping a user draft a response, or allowing an assistant to prepare an action for approval. We can evaluate providers such as OpenAI or Anthropic against the task instead of tying the product's value to a single model name.
How I approach it
Choose one outcome and a baseline
We define a useful task and compare the proposed AI feature with the current workflow. Example inputs and a concrete acceptance check make it possible to tell whether the integration improves anything.
Put boundaries in application code
Credentials remain on the server. The application authenticates the user, validates inputs and outputs, and decides which tools or records the request may reach. Model-generated instructions do not bypass those checks.
Build for imperfect networks and responses
The integration needs timeouts, cancellation, understandable failures, and bounded retries. For structured tasks, a syntactically valid response still has to satisfy your business rules before it is stored or acted on.
Make operating costs visible
Usage measurement, per-user limits, and representative evaluations help you decide whether a model change is an improvement. You receive the integration source and configuration rather than a dependency on my account.
What gets delivered
- Server-side model integration and secure configuration
- Streaming or structured-output experience as appropriate
- Validation, timeouts, cancellation, and error handling
- Per-user access and usage limits
- Evaluation examples and integration documentation
What it costs to get wrong
- A provider API key is shipped to a browser or mobile client.
- Retries create duplicate actions or unbounded usage.
- The application accepts a model response as valid without checking its structure or meaning.
Why you can believe this
This portfolio contains a server-side AI endpoint with request guardrails. My ai-os case study demonstrates enforced budgets and independent completion checks, while my app collection demonstrates experience building the product around a feature.
Questions buyers ask
- Can you integrate OpenAI or Claude into my app?
- Yes. The provider and model should be selected against the feature's requirements, data constraints, cost, and response time. The scope includes the secure server integration and the user-facing behavior, not just a prompt.
- Do we need to rebuild the whole product?
- Usually not for a focused feature. I review the existing stack and identify a narrow integration boundary. If the current authentication or backend cannot safely support the feature, I explain the prerequisite work before implementation.
- Can you replace an existing model provider?
- Yes. First we capture representative inputs and acceptance checks, then compare behavior with the replacement. A compatible request shape does not guarantee equivalent results, so evaluation is part of a provider migration.
- Who owns the API accounts and source code?
- Production credentials should belong to you, and the integration is delivered in your codebase or a repository you control. Access, hosting, and handoff are agreed during scoping.
Related writing
- Building AI-Powered Apps in 2026: A Practical GuideA hands-on guide to integrating LLMs, embeddings, and AI features into production web applications using modern tooling.
- Claude vs GPT: Choosing the Right LLM for Your ProjectA practical comparison of Anthropic's Claude and OpenAI's GPT models based on real-world project experience across dozens of implementations.
- How to Hire an AI Developer: A Practical Buyer’s ChecklistWhat to ask before hiring an AI developer, how to scope a useful pilot, what deliverables to expect, and how to judge whether the system actually works.
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