AI Integration for Web Products
Give an existing product one useful AI capability, with a clear way to test the result.
A chatbot demo is easy to show, but production needs predictable cost, access controls, useful answers and a fallback when a model fails. The feature must work inside the product your customers already use.
You get an AI feature tied to a concrete task and acceptance criteria. I compare a deterministic implementation with a model-based approach before introducing recurring model cost or data exposure.
Deliverables
- Use-case and data suitability assessment
- Model API integration with validated inputs and outputs
- Document extraction, classification or assisted search
- Evaluation examples and failure-case checks
- Usage limits, timeouts and human review steps
- Integration documentation and operating notes
Not included
- Training a foundation model from scratch
- Guaranteed factual accuracy for every generated answer
- Autonomous high-stakes decisions without a human review process
Define a useful answer and a safe failure
For a document assistant, classification tool or search feature, we first define what a correct result looks like. Representative examples expose missing information, ambiguous inputs and cases where the feature should refuse or ask for review. A convincing demo is only the start of that evaluation.
Integrate with the product’s boundaries
I check data access, provider suitability, latency and operating cost before choosing the implementation. The feature can include source references, structured output validation and an approval step. Monitoring and limits make it possible to see when quality or cost changes.
Questions before we start
Can you add an AI assistant to an existing product?
Yes, if the product exposes appropriate integration points. We define the assistant’s allowed tasks and data access, then start with a bounded feature and test examples.
When is retrieval-augmented generation useful?
RAG can help when answers should use a specific document collection. It still needs permission checks, source handling and evaluation; retrieval does not make every generated answer correct.
How are model costs and private data handled?
We select a provider and data policy for the use case, minimize what is sent and set usage limits. Model charges and retention requirements are reviewed before deployment; sensitive material is not assumed to be safe to upload.
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Start with scope
Let’s scope your project
Describe the task, show non-sensitive examples of the input and expected output, and explain who will review the result. I will assess whether AI is useful and how to validate a first version.