Amira AI voice systems
At Amira, I handled projects end-to-end for many different clients, from the first conversation through building, integrating, testing and delivering the system. I built voice assistants and deeply integrated WhatsApp chatbots around each team's operations. Only a few of those engagements were proofs of concept.
From the client conversation to the working system
Each project had its own information, tools and ways of working. I prepared knowledge from documents, organized databases, connected business systems and designed how the assistant should respond, take action or hand a conversation to a person. I carried those decisions through implementation and testing, working through errors with the team.
Across the voice projects, I worked with English and Arabic configurations, context prepared before a call, tools used during it and structured outputs afterwards. In WhatsApp, conversations were connected to property information, CRM records, staff notifications and follow-up workflows. The channel changed; the need to make the conversation and the underlying operations agree stayed central.
One example: insurance support
For a large insurance company, I worked on a voice-assistant project involving member records, policy information and provider details. I normalized member data, extracted and cleaned knowledge from documents, and organized facility information so the assistant could retrieve relevant facts during a call.
This project made the distinction between a policy answer and a provider search particularly important. The assistant needed the relevant member context for one, and facility data together with the caller's stated location for the other.
| The caller needs | The system needs |
|---|---|
| An answer about their policy | The relevant member and policy context |
| A nearby provider | Facility information and the caller's stated location |
Within this insurance project, location also mattered to selecting the right destination for a caller. A phone number was not a reliable statement of where someone was now. The assistant needed to ask, then use the answer to choose an appropriate facility or handoff.
Ask where they are.Different clients, different integrations
Other engagements called for different combinations of conversation design, knowledge, tools and follow-up. I adapted those pieces to each client rather than treating one successful configuration as a finished solution for everyone. The deeply integrated WhatsApp work for a property company is covered in its own case study.
What I value in this work is seeing a project through as a whole: understanding the need, making the information usable, building the connections and getting the interaction right. The words a customer hears or reads, the records the assistant uses and the information a colleague receives afterwards all have to agree.