Assessment & recommendation
We understand the need, the systems and the constraints, and you get a written recommendation: what to do, in what order and why. Including the alternatives considered.
DECIDE BEFORE YOU BUILD
AI Consultant in Israel for Businesses and Organizations
The right decision saves half a project.
Focused advisory for organizations, founders and teams: where AI truly adds value, which architecture and infrastructure fit, what to build and what to buy, and how to move forward without guessing. Experience across development, automation, DevOps and AI architecture, in one conversation.
Talk about AI, Architecture & DevOps ConsultingTHE RIGHT FIT
This is a fit if…
- Leadership that needs a clear direction on AI before committing budget.
- Teams facing an expensive technology decision: a tool, a vendor, an architecture or infrastructure.
- Founders who want a second opinion on a plan, a quote or an existing product.
- Organizations that need an architect on call without hiring a full-time role.
A CLEAR DELIVERABLE
What you get along the way.
A practical roadmap
Steps, milestones and success measures your team or a vendor can execute. What happens in the first 30, 60 and 90 days.
Ongoing decision support
Regular availability for architecture questions, tool selection and reviewing proposals and deliverables, so the next decisions are made with confidence.
HOW WE MOVE FORWARDWe map the path.
We map the path.
Then we move.
- 01
An intro call to define the question that needs an answer
- 02
A focused review of the current state, data and constraints
- 03
A written recommendation with alternatives, risks and priorities
- 04
Into execution: with your team, with a vendor, or with me
THE FULL PICTURE
A good AI consultant doesn't start with the latest tool. They start with where AI can save time, improve decisions or unlock something new in your business, and where it would only add complexity. AI consulting for businesses means turning curiosity and market pressure into a plan: what to test first, which data it will rely on, how it connects to the systems you already run, and how you will know it works. This page covers what an AI consultant actually does, what AI adoption looks like inside an organization, and how to choose the right person to guide you.
What does an AI consultant actually do?
An AI consultant connects three worlds that rarely speak the same language: the business need, the data and systems already in place, and what language models and AI tools can realistically do today. Leadership sees an opportunity, the technical team sees risk, and vendors see a sale. The consultant's job is to see the whole picture and say plainly what is worth doing, what is premature and what is not worth doing at all.
In practice, AI consulting means mapping processes and pain points, checking data quality and access, weighing off-the-shelf products against custom development, and designing the architecture - for example RAG, which lets a model answer from your organization's own documents, or agentic workflows that carry out multi-step tasks. It also covers security, permissions and human oversight. Good technology consulting ends with a clear decision and an executable plan, not an inspirational slide deck.
It also helps to know what AI consulting is not. It isn't a generic chatbot training, buying licenses for every employee, or a promise that every process will become automatic. Sometimes the right recommendation is to wait, to fix the data first, or to solve the problem without AI at all.
To keep the conversation practical, it helps to know the kinds of solutions that come up most often in AI consulting for businesses:
- Internal knowledge assistants - a system that answers employee or customer questions from your documents, policies and data, with a link to the source.
- Document and data processing - extracting data from forms, contracts, emails or requests, classifying it and routing it to the right system.
- Agents and workflows - multi-step processes where the system takes actions in other systems, with human approval at the sensitive points.
- Automating existing processes - connecting AI capabilities to the tools teams already use, instead of adding yet another system.
- AI in software development - structured use of coding agents and AI tools in engineering teams, with quality control.
When does an organization need AI consulting?
Not every business needs an AI consultant, and not always on day one. What the situations where consulting really helps have in common is a decision with financial and operational consequences, and not enough certainty to make it alone. That is exactly where a few hours of focused advice can save a lot of work in the wrong direction. These are the most common signs it is time for an outside view:
- Employees already use AI tools on their own, and there is no clear policy on what data can go into them.
- There is a long list of AI ideas and no structured way to decide where to start.
- A pilot looked great in the demo but never made it into day-to-day use.
- You have proposals from several vendors and struggle to compare them or understand what each one actually includes.
- Leadership is asking for an AI strategy, and the technical team is too busy to build one alongside its regular work.
- You plan to connect sensitive customer or company data to an LLM-based system and want to be sure it is done right.
The three layers of AI strategy consulting
Serious AI consulting works on three layers at once. Skipping one of them is a common reason projects stall halfway. Many consultants are strong in only one: a strategy consultant can build a plan without always knowing what will be hard to implement, and a developer can build without always asking whether it is worth building. Someone with a background in development, automation and DevOps can translate a strategic decision into technical requirements and point out early where the project is likely to get stuck.
- Strategic - where AI serves business goals. Identify the processes with the most potential, weigh cost against benefit, decide what to build versus buy, and set priorities leadership can stand behind.
- Operational - how the solution fits into daily work. Who uses it, what changes in the process, who reviews the output, what the AI usage policy is and how teams are trained. Without this layer, even an excellent solution sits on the shelf.
- Technical - how it is built and run. Architecture, model and vendor selection, integration with systems and data sources, permissions, monitoring, usage costs and deployment to the cloud or on-premises. This is where you find out whether the solution survives past the demo.
What does AI adoption in an organization look like?
Every organization is different, but a good adoption process is built so each stage earns the next one. Scope and pace depend on the size of the organization, the complexity of the data and the level of risk. Each stage ends with a decision point: continue, change direction or stop. That way, investment only grows when it is justified.
Define the question and the goals
Start by agreeing on what you want to achieve and what success looks like. A focused question, such as cutting ticket handling time or making internal knowledge easy to find, beats a vague goal like bringing AI into the company.
Map processes, data and systems
Review the relevant processes, data sources, the systems that need to connect, and the constraints: security, privacy, budget and team capacity.
Choose and prioritize use cases
Rank opportunities by value, feasibility and risk, and pick one or two to start with. Each gets a success metric you can actually measure.
Design the solution: build or buy
Compare off-the-shelf tools, platforms you already own and custom development, and design an architecture that fits your infrastructure and the team that will maintain it.
Run a controlled pilot
Build or configure a limited version, run it with real data and real users, and measure it against the agreed metrics. A good pilot is designed to teach you something, not just to impress.
Scale, embed and keep improving
If the pilot hits its goals, expand gradually: monitoring, training, working procedures and clear internal ownership. If it doesn't, understand why and decide whether to fix it or move to the next opportunity.
What you get from AI consulting and how success is measured
The output of AI consulting should be concrete and useful even if you never work with the consultant again. Success is measured in the business process, not in the model: handling time, error rate, manual work removed, user satisfaction or actual adoption. Measure the current baseline before you start, or you will never know whether anything improved.
Depending on scope, expect deliverables like these:
- A ranked opportunity map: which processes suit AI, in what order and why.
- A written technology recommendation with the alternatives considered, the risks and an estimate of each option's running costs.
- An architecture design detailed enough to hand to a development team or a vendor.
- Responsible-use principles: which data may go to which tools, who approves and how it is documented.
- A pilot definition: scope, users, data and success metrics.
How to choose an AI consultant, and mistakes to avoid
The AI consulting market filled up fast, and it can be hard to tell people who have shipped production systems from people who mostly know the tools. Certificates and courses are no substitute for having built, deployed and maintained a real system. Also listen to how the consultant talks: anyone promising results before understanding your processes and data is probably selling a solution, not giving advice. Questions worth asking in the first conversation:
- Have you personally built an AI system that real users rely on, and what was hard about it?
- Do you have partnerships with, or commissions from, vendors you might recommend?
- How will we verify the system's answers are correct, and what happens when it is wrong?
- What happens to our data, and where is it processed?
- What do we get at the end, and what will our team be able to continue on its own?
Common mistakes in AI adoption
Most stalled AI projects don't fail because of the model. They fail because of decisions made before a single line of code was written. These are the mistakes that come up most often:
- Starting from the tool instead of the problem - picking a platform first and looking for a use case later.
- Running a pilot with no defined success metric, then finding out at the end that nobody can say whether it worked.
- Assuming the data is ready when it is actually scattered across systems, duplicated or out of date.
- Ignoring the ongoing cost of model usage, which grows with the number of users.
- Leaving security and permissions for the end, and discovering that users can see information they shouldn't.
- Handing every decision to a single vendor who also sells the solution.
Why work with me
I'm Bechor Simhaev, an AI Solutions Architect. I design and build end-to-end AI solutions - RAG pipelines, agentic and multi-agent workflows, and LLM-based organizational platforms - and take them from concept to production.
I got here through automation development, technically leading automation development in C# with CI/CD, and working as a DevOps engineer with Kubernetes (including AKS and OpenShift), Terraform, Docker, Azure, Azure DevOps and GitHub. For you, that means every recommendation is checked against how it will be deployed, monitored and maintained, not only how it looks in a demo.
I taught as an assistant lecturer in a software engineering course at Ariel University, so I'm used to explaining complex technology clearly to both leadership and engineering teams. I work independently and vendor-neutral, with no commissions from vendors, so the recommendation rests only on what is right for you.
WHO YOU WORK WITH
Who works with you.
Bechor Simhaev · AI solutions architect & DevOps
I take an AI idea and turn it into a system running in your organization: RAG over your knowledge, agents and agentic workflows, and LLM-based platforms. From discovery to production.
The difference: I don’t stop at the demo. Hands-on experience in DevOps, Azure, Kubernetes and on-prem clusters in closed environments with EKS Anywhere and OpenShift means the solution is built from day one to run in your environment, with its security and permissions. And I’m independent, with no commissions and no preferred tool, so the recommendation rests only on what is right for you.
GOOD QUESTIONS
Before we start.
Technology consulting: when to bring in an advisor and what to ask forHow is consulting different from a development project?
In consulting, the deliverable is a decision and a plan, not code. You can stop at the recommendation, hand it to your team or a vendor, or continue into joint execution.
Can we start with a single conversation?
Yes. You can start with a focused advisory session on one question and decide afterwards whether a broader scope is needed.
Do you recommend specific vendors?
Recommendations are based on your needs, not on vendor partnerships. I take no commissions or commitments from vendors, and if there is a conflict of interest, I will say so up front.
How much does AI consulting cost?
It depends on scope. A single focused question may take one or two sessions, while mapping several departments and building a roadmap takes longer. Other cost drivers are the number of systems and data sources, how sensitive the data is, and whether you also want support during implementation. After the intro call you get a proposal with a defined scope.
How long does AI adoption take?
Focused consulting can wrap up within a few weeks, and a first pilot is usually built within a few more. Broad adoption is gradual, because it changes how people work, not just the technology they use.
Is AI consulting worth it for a small business?
Yes, at a different scale. For a small business, consulting usually focuses on a handful of repetitive processes and choosing the right existing tools, without custom development. Often one focused session is enough to avoid unnecessary purchases and set a clear way of working.
How is AI consulting different for a large enterprise?
Enterprises have more stakeholders, more systems and stricter requirements around security, procurement and compliance. So the engagement also covers usage policy, data governance and coordination across departments, not just choosing a solution.
What happens to our data when we use language models?
That is one of the first things we look at. The recommendation defines which data may be sent to which services, under what terms, where it is processed and who can access it. When needed, the solution can run inside your own cloud environment with per-user permissions.
Do we need clean, organized data before we start?
Not necessarily. Part of the work is finding out what data exists, where it lives and how good it is. Sometimes one data source needs fixing before you build on it; sometimes you can start with what you have and improve along the way.
Should we start with a pilot or a POC?
Almost always. A pilot or POC on a single use case, with real users and a defined success metric, teaches more than any requirements document. It also lets you stop or change direction before a large investment.
We already work with an AI vendor. Why bring in an outside consultant?
A vendor-neutral consultant reviews the proposal, architecture and deliverables purely from your side of the table. That makes it easier to spot gaps, hidden costs or excessive lock-in. The goal is not to replace the vendor but to make sure you get what you actually need.
How do you work with our in-house team?
Your team is central to the process: they know the systems and the workflows, and they are the ones who will carry the work forward. The engagement includes joint sessions, documented decisions and knowledge transfer, so the capability stays in-house.
Which areas are best to start with AI?
Areas heavy on text, documents and repeated requests are natural starting points: customer service, internal knowledge, operations, sales and software development. The common thread is repetitive work that relies on retrieving information and where you can check whether the result is right.
Does AI consulting also cover infrastructure and cloud?
Yes, where relevant. An AI solution has to run somewhere, connect to your systems, be monitored and meet security requirements. My DevOps and cloud background lets me plan that part during consulting instead of discovering it mid-project.
How do you know an AI system is giving correct answers?
You define a test set up front: real questions or tasks with expected answers, and you run the system against it before launch and after every change. You also plan when a person approves the output and how users report mistakes.
LET’S MAKE IT REAL
Let’s find the right step for you.
Have an idea, a need or a question that hasn’t settled yet? Let’s work out together what’s right to do.
Let’s talk A direct conversation. A clear direction. A step that fits you.