We map where AI pays off
We go through the organisation’s processes, teams and data and rank the use cases by expected value, complexity and risk. You get a focused list of what to do first and what to leave for later.
AI Adoption for Organisations
AI that works in the organisation, not just in the deck.
For management, IT and innovation leads, mainly at Israeli companies.
MLAIA helps organisations bring AI into everyday work, from strategy to working use. We map where AI pays off, choose the tools, write the usage policy, train the teams and take what proved itself in a pilot into production. We are a team that builds and runs AI in production, so every recommendation is checked against whether it can be built and maintained. We are builders, not slide-deck consultants.
Built around your organisation
- Starting point
- People already use AI, without shared rules
- The challenge
- Choosing tools, meeting privacy and security rules, getting to real use
- Our focus
- A pilot with success measures, then production
Who does the work
A team that builds AI in productionWe build prediction models, medical AI, audio processing and AI agents. The same team gives the advice, runs the training and does the build.
Led by Dr. Yochai EdlitzPh.D., Weizmann Institute of Science. Leads MLAIA and its projects.
In Hebrew, with your teamsWorkshops, policy and documentation in Hebrew or English, built around the tools and processes you already have.
01 What we do
From the map to production.
Start with one service or go the whole way. Each one ends with something your team can keep working with.
We choose the tools
Microsoft Copilot, ChatGPT Enterprise, Claude, or agents built in-house. We work out what fits each team, by the kind of work, the systems already in place, security requirements and cost.
An AI usage and data policy
We write the usage policy with you: which tools are approved, what information may and may not go into them, and who approves new uses. It is aligned with Amendment 13 of Israel’s Privacy Protection Law and with the company’s information-security rules.
We build people’s AI skills
Hands-on training by role: sales, customer service, finance, HR, engineering and management. Each team practises on tasks from its own work, in the tools the organisation has approved.
We build AI skills and workflows for teams
We build reusable Claude or Copilot skills and agents, tied to the team’s own tools and data. A task that comes up again and again is done the same way every time, not by whichever prompt someone remembers.
Pilots with success measures, then production
Before anything is rolled out, we run a defined pilot on a specific team and process, with success measures agreed up front. What works goes into production with permissions, monitoring and clear ownership. What doesn’t is stopped.
Cost savings
The aim is to find the work and spend that AI can take over, and cut it. We map repetitive manual tasks and outside services that could be replaced, and measure the before and after in the pilot, so the decision rests on your own data.
Not sure where to start?
A short intro call is enough to see where you stand today and what first step we would take.
Book an intro call →02 Why MLAIA
Builders, not slide-deck consultants.
An AI strategy is only worth something once someone builds it. With us, the same team maps, recommends and builds.
Advice tested against production
We build and run AI systems. When we recommend a tool, an architecture or a process, we know what it takes to work day to day: permissions, integration, monitoring and maintenance.
No hand-off between vendors
Policy, training, the pilot and the build are done by one team. Nobody has to explain the organisation again when the build starts.
Assistants on your company’s data
When a team needs an assistant that answers from the company’s documents, procedures and internal knowledge, we build it at Ultrabot, where we build AI assistants and agents.
Company knowledge assistants at UltrabotWhen an off-the-shelf tool isn’t enough
Some problems need a dedicated model built on your data. That is what our machine-learning practices are for.
Prediction & causal analysis → Medical AI → Audio & acoustics →
03 How an engagement runs
From the map to working use.
Five steps, each with a clear output. You can stop after any of them.
Map
Conversations with management and team leads, and a review of processes and data. Output: a ranked list of use cases.
Choose
Matching tools to teams, including a security, permissions and licensing-cost review. Output: a reasoned recommendation per team.
Policy and training
A usage and data policy approved on your side, and training by role. Output: a policy document, and teams starting to work by it.
Pilot
A defined team and process, success measures set in advance and a before-and-after measurement. Output: a decision to continue, change or stop.
Production
What worked is scaled: integration, permissions, monitoring and internal ownership. Output: a workflow your team owns.
04 Questions buyers ask
Straight answers.
Our people already use ChatGPT or Copilot. What else is needed?
Usually three things: a clear policy, training that fits each role, and workflows built around the tool instead of ad-hoc use. We start from what you already have.
Which tool do you recommend?
It depends on the team. The recommendation follows the kind of work, the systems already in use, security requirements and cost, and sometimes the answer is a mix of tools.
Will the policy you write meet Amendment 13 of the Privacy Protection Law?
We write the policy to align with the requirements of Amendment 13 and with your information-security rules. We are not a law firm, so the final wording should be approved by your legal counsel and whoever owns information security in the organisation.
Does our data leave the organisation?
Only as far as the policy allows. For each tool we check where data is processed and stored and which organisation-level settings it offers, and we build inside the environments you have approved.
What do you need from us to start?
A contact in management, a list of the tools already in use, and time for short conversations with a few team leads. A first intro call is enough to decide whether and how to start.
AI Adoption for Organisations
Book an intro call about AI in your organisation.
Tell us which tools you already use, which teams want to start and what is holding you back today. We’ll tell you what first step we would take.
Please don’t include confidential documents, credentials or personal information about employees or customers.
yochai@mlaia.com
WhatsApp: +972 52 484 6282