Choose a workflow
Define users, inputs, permitted actions and a measurable business outcome.
Ultrabot by MLAIA · Hebrew & English AI assistants
Ask it.
It acts.AI assistants for your site, your team and your app.
Chatbots, knowledge assistants and automations built on your data, in Hebrew and English. By text, by voice, or with a face. Try ours below.
Live demo
Skip past the embedded demoOpen the demo in a new tab The demo answers out loud. Your browser will ask for the microphone.
Pick who answers
A cartoon, a 3D character, a lifelike presenter, or your own face.
What an AI assistant does.
Answer
From your documents, with sources.
Act
In your CRM and tools, with approval.
Speak
By chat, on the phone, or with a face.
What we build
Where your
assistant lives.
Three kinds of assistant, built the same way: on your data, measured before launch, with people on the decisions.
Website chatbots
Answers your customers from your site and documents, with sources, captures leads into your CRM and hands off to a person.
Website chatbotsKnowledge assistants (RAG)
Answers your staff from policies, procedures and contracts, with the source and your permissions, in your cloud or on-prem.
Knowledge assistantsIn-app assistants
An assistant inside your web or mobile app that answers from product data and acts through your API, by text or voice.
In-app assistantsUnder every assistant
The layers we add when an assistant needs to act, talk or run in your cloud. Each page explains how we build it and what we measure.
Voice agents
Streaming speech-to-text, an agent with tools and natural voices, tuned for delay, interruption and Hebrew, on the web or the phone.
Voice agentsAI avatars
Talking faces lip-synced to the agent's voice, rendered in the browser or app, or streamed as photoreal video.
AI avatarsAI agents on AWS
Amazon Bedrock models and guardrails, AgentCore, Lambda and Step Functions, deployed in your account with least-privilege tools.
AWS Bedrock agentsLangGraph agents
Agents as explicit graphs: typed state, tool calling with limits, approval steps, retries and evaluation of every step.
LangGraph agentsn8n automation
Workflows that connect forms, email, CRM and documents, with AI steps for classification and extraction, and people on the decisions.
n8n automationWhat we have built
A Hebrew and English voice assistant, with an avatar that speaks.
Our recent work includes a Hebrew and English voice assistant with a lip-synced avatar for TailorPath, a rehabilitation and care platform, live with real users, running Claude on Amazon Bedrock with Amazon Transcribe. Patient data (PHI) is anonymised before it reaches the model, an AWS BAA is signed, and it is built to HIPAA requirements.
Before that, we built a website chatbot that answered in Hebrew or English and collected customer inquiries.
The clinical work behind it, at MLAIA Data Science.
We describe other client projects in general terms. Details are shared in conversation, subject to confidentiality.
A practical delivery path
Prove the value. Then integrate.
Build a baseline
Use representative examples to assess retrieval, prompts and available models.
Connect & evaluate
Integrate the required systems and test errors, access controls and handoffs.
Operate & improve
Monitor answer quality, actions, latency and cost as data and models evolve.
Insights
The thinking behind the work.
Talk to a person
Tell us what
you’re working on.
Share the problem, the data you have and what success would look like. We’ll discuss a practical next step.
Please don’t include confidential datasets, credentials or patient information.