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AI Features& Integration

Useful AI, inside your product.

For teams adding an assistant, knowledge search, or a focused AI workflow to their web application.

Add AI where it makes your product more useful. I build assistants, retrieval-based search, and OpenAI integrations that work with your application and its data.

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TripBooka — AI Integration projectExplore TripBooka
TripBooka / Selected workAmminadab Elias
  • OpenAI API
  • RAG
  • Vector search
  • Node.js
  • Next.js
  • TypeScript
(What I can help with)

What’s included /

01.AI Assistants

Integrate conversational features into your existing product with the OpenAI API. Connect the assistant to relevant application context and design a clear experience for follow-up questions, streaming responses, and failures.

02.RAG & Search

Prepare your content for retrieval using embeddings and vector search, then use relevant results to inform responses. Include source references and an appropriate fallback when the available information cannot support an answer.

03.AI Workflows

Use AI to extract information, summarize content, or turn a request into structured application data. Validate the output before it reaches your database or triggers another step, with human review where the workflow needs it.

04.Testing & Integration

Check representative questions and edge cases, refine retrieval and prompts, and account for response time and API usage. Build in error handling and useful logging so the feature can be improved after launch.

(The work behind the service)

Selected projects /

TripBooka project presentation
(01)   AI integration & full-stack development

TripBooka

TripBooka connects travel requests with relevant supplier information and helps agents prepare structured proposals.

  • Scout retrieval uses embeddings and vector search to find relevant supplier information.
  • OpenAI-powered workflows turn travel requests into structured data with validation and fallback handling.
  • Confidence scoring and usage tracking help inspect the quality and behaviour of the integration.
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(How we work together)

From brief
to handoff /

(01)

Choose the useful task

Define what the feature should help a user do, the data it can access, and what a good response looks like. Keep the first use case focused.

(02)

Connect & evaluate

Build retrieval or tool calls around your application. Try representative inputs, check the output, and refine prompts and data handling.

(03)

Integrate & refine

Connect the feature to the interface, account for failures and API usage, and document how to monitor and improve it after release.

(A few useful answers)

Before we start /

Can the assistant use my own content?

Yes, where your content can be accessed and prepared for retrieval. We first review the source material, access rules, and how it changes, then choose a retrieval approach suited to the task.

Can you add AI without rebuilding my app?

Often, yes. I review the existing frontend, backend, and data flow to find a practical integration point. The goal is to add a useful feature within your current product.

How do you handle unreliable answers?

I use representative test questions, check retrieved context, validate structured output, and add an appropriate fallback when the available information is insufficient. Workflows can include human review when a person needs to make the final decision.

(Have something in mind?)

Let’s build /

Tell me the task you want AI to help with, the application it belongs in, and the information it can use. A few real example inputs are a useful place to start.

Book a Call Hire on Upwork
eamminadab@gmail.com
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