From idea to AI that works in production: on time, transparent, and proven from the start
Zooz-tech is a Microsoft Partner that develops AI solutions and complex business systems for organizations. Our delivery model is built around one question: what has to happen for the system to work in your organization, on time, and create value you can measure.
That is why we are ready to start with an AI proof of concept (POC): a working version on your data that shows the change the technology can bring to your organization, before you commit to the full project.
Five things every project is measured by
Measurable business value
Every project starts from a success metric agreed with the client, such as handling time, manual work saved or response rate, not from a feature list.
A demo at kickoff
Already at kickoff we show a demo of the solution on your process, and the client approves it from a business standpoint before we move on. The direction is agreed from day one.
A system in production, not just a demo
The goal is a system people work in every day, connected to SAP, Priority ERP and Dynamics 365, within your organization's permissions and security.
Schedules and transparency
Milestones, demos and acceptance criteria are visible to everyone involved, with an up-to-date status at every stage of the project.
One team, end to end
The same team specifies, develops, implements and supports, led by the Head of Delivery. No handoffs between vendors.
Seven stages, from discovery to continuous improvement
This is what a client work plan looks like. Every stage has a clear deliverable, and every stage includes a practice that is not yet standard in the market.
Discovery and opportunity mapping
A workshop with process owners, users and IT. We map processes, systems and data, and rank the opportunities by business value and complexity. Already at kickoff we show a first demo of the solution, and the client approves it from a business standpoint.
POC on real data
We build a working version of the selected process on the organization's own data and systems, with a scope and end date set in advance. At the end we present it to management and users, and decide together whether to continue.
Requirements, design and an interactive prototype
A specification document alongside a prototype you can click through and try. When needed, we build a simulator of the process and test the solution before building it.
Development in sprints, with demos
Short sprints, and at the end of each one we show the client a working system. Feedback goes into the next sprint, and scope changes are decided together, along with their impact on time and cost.
Testing and acceptance
User acceptance testing (UAT) against the criteria written in the specification. For AI agents, we also run a set of test questions with expected answers on every version, and check content safety and permissions.
Phased go-live
We go live first with an initial group of users, fix what comes up in the field, and only then expand. Every release can be rolled back to the previous version, and includes training for users and for the people who will maintain the system.
Stabilization, monitoring and continuous improvement
A stabilization period with the same team that built the system. We track usage, errors, answer quality and costs, and feed what we learn back into the next sprints.
New practices in the work plan
Methods most projects in the market do not use yet, and that we already apply today:
A POC on your data
Instead of a slide deck, one process that runs on the organization's data and systems, and a success metric that is actually measured.
A simulator before development
We build a model of the process and test the solution before building it, like the Lego Lab for Dr. Pach VeTzeva (a car body and paint repair chain): change a step and immediately see the impact on the metrics.
A Generative UI prototype
A live prototype already at the design stage, so decisions are made on something you can see and try.
AI-assisted development, with quality gates
Coding agents speed up development, and every change goes through tests, security scans and human review. From vibe coding to production.
Evals: AI quality you can measure
A set of test questions with expected answers that runs on every version, so you know the agent has not quietly gotten worse.
One MCP layer for all agents
A controlled connection to SAP, Priority ERP and Dynamics 365 that serves several agents, instead of a separate integration for each one. Core systems integration.
AgentOps
Monitoring agents in production: answer quality, resource consumption and costs, and conversations the agent could not answer.
Ready to prove it before you commit
We are willing to run a POC to show the client the change the technology can bring to the organization. Not a slide deck and not a generic demo: one of your processes, running on your data.
What the POC includes
- One process, with clear business value
- The organization's real data and systems, under its own permissions
- A success metric agreed before we start
- A scope and end date set in advance
- A live demo for management and users
- A joint decision: continue, change direction or stop
What you keep, even if you decide to stop
- A well-founded understanding of your data quality and what is missing
- A well-founded estimate of cost, benefit and schedule for the full project
- A recommended architecture and risks identified early
What the process gives your organization
Less risk
You decide on the investment after you have seen the solution work on your data.
No surprises
Demos at every stage and written acceptance criteria, so go-live does not reveal anything new.
Faster value
Start with one process, prove the value, and expand from there.
Management and users on board
A POC and demos that show real change bring the organization on board early.
Measured quality
Tests, Evals and automated quality gates on every version.
A system that keeps improving
Monitoring and ongoing support after go-live, with the same team.
Knowledge stays in the organization
Documentation, training and clear ownership of everything that is built.
FAQ: AI proof of concept (POC) and AI project management
What is a POC, and how is it different from a pilot?
A POC (proof of concept) tests whether the solution works on the organization's data and systems, and what it changes in the process. A pilot already runs a version in production with real users, on a limited scale. At Zooz-tech, a successful POC leads to the specification stage, and the pilot is the phased go-live to the first group of users.
How long does a POC take?
It depends on the process and the data. The POC is short and focused on one process, and the scope and end date are set in advance, before we start.
What happens if the POC does not succeed?
Even then there is a result: an understanding of your data quality, of what is missing, and of the real cost and benefit. Sometimes you change direction, and sometimes you decide this is not the right time, which is a better decision than a project that fails halfway.
How do you keep to schedules?
Milestones defined in advance, a client demo at the end of every sprint, written acceptance criteria, and scope changes decided together along with their impact on the schedule. A Head of Delivery leads every project and shares a regular status update.
Who needs to take part on the client side?
The process owner, who defines what counts as success; user representatives, who try out the demos; and an IT or information security representative for connections, permissions and environments.
What happens after go-live?
A stabilization period with the same team, monitoring of usage, errors, quality and costs, and an improvement plan that feeds back into the sprints. You can keep ongoing support with us or hand it over to your own team.
Learn more: How a project stays on schedule · Clients & projects · AI workshops for organizations · All solutions
Is there a process you would like to see working?
Tell us about it, and we will propose a focused POC: what we build, on which data, what counts as success and when we present it.