Turn your AI prototype into a production-ready product
Your prototype works in a demo. Production is different: real users, real load, real systems. We design, build, and ship the production-ready product in weeks, on a performance-based contract.
Commercial model
Built around your outcome.
Performance-based contract
We stay accountable to the agreed production outcome.
Pay against agreed delivery
Scope is agreed before we start. Not a vague retainer.
60-day bug guarantee
Bug fixes after launch. Money back if we miss the agreed scope.
You own the code
Code, infrastructure, and data sit on your side.
No lock-in
No Zheat platform. You can run it yourselves after handover.
The problem we solve
Your prototype works. Production is the problem.
A demo that works is not a system that holds for real users. These are the blockers we see between prototype and production.
The demo isn't ready for real users
It works in a controlled walkthrough. It does not yet handle real load, messy inputs, or people using it every day.
Answers and uptime are not reliable enough
Hallucinations, silent failures, and no monitoring. Production needs error handling, observability, and a system you can trust.
AI costs become unpredictable
Token spend grows with usage. Without cost controls, a successful prototype becomes an expensive production problem.
Infrastructure and deployment are missing
No production environment, no CI, no scaling plan. The prototype still lives on a laptop, a notebook, or a staging hack.
It isn't connected to real systems
The model is isolated from your CRM, data, auth, and workflows. Production means integrations that hold.
Security and ownership are unclear
Sensitive data, access control, and who owns the stack still need to be designed for production.
If this is your bottleneck, get it mapped.
In 30 minutes, we'll identify what's blocking production and outline the next steps.
From prototype to production
A clear production journey
Four steps from the existing prototype to a system you can put in front of real users.
Assess
Understand the existing prototype, architecture, constraints, and what is blocking production.
Architect
Define the production architecture and technical roadmap: stack, scaling, security, and cost.
Build
Implement the infrastructure, integrations, product features, and AI systems required for production.
Ship
Deploy, monitor, optimize, and hand over a production-ready system. We stay on for drift, quality, and model updates.
See how we'd take yours to production.
In 30 minutes, we'll identify what's blocking production and outline the next steps.
Commercial model
Not a retainer. A defined production outcome.
We take on delivery risk with you. The work is scoped before it starts, and we stay accountable after launch.
You are not buying hours. You are buying a production-ready product, with a contract built around that outcome.
Scope agreed before we start
Clear deliverables and timeline before we start building.
A defined production outcome
The project has a target: a system real users can use, not an open-ended exploration.
Tied to agreed delivery
The engagement is performance-based. Payment follows the commercial model agreed for that scope.
Not a vague retainer
If one of these blockers is true, you need the product built, then kept running. That is a performance-based contract, not a retainer.
Accountable after launch
Bug fixes included for 60 days. Money-back guarantee if we miss the agreed scope.
60-day guarantee: bug fixes after launch, money back if we miss the agreed scope. After launch we stay on a performance-based contract. You own the code and the infrastructure.
Results
Real products. Real users.
Outcomes from shipped work, not projections.
1,000+
Daily users
Alfred.ia at BPI France, from a small POC to daily employee use.
10M+
Users reached
Open Garden and FireChat, products the team helped build.
~2 weeks
Typical delivery
From kickoff to a production-ready app.
Case studies
From prototype to production
Select a project. Each one shows the production problem, what we built, and the outcome we can stand behind.
BPI France
Alfred.ia
French Public Investment Bank founded in 2012. Finances and supports businesses (SMEs, mid caps) in their growth.
- Challenge
- An internal AI chatbot with only a handful of users. The prototype was not yet a stable product employees could rely on every day.
- What Zheat built
- A secure internal multi-model AI chatbot (GPT-4o, Claude 3.5, Pixtral) with web search, deep research, IT documentation, translation, code generation, financial analysis, and a prompt library.
- Result
- Used daily by more than 1,000 BPI France employees.
Want this kind of production outcome?
In 30 minutes, we'll identify what's blocking production and outline the next steps.
Clients
Trusted by teams building products used in the real world
From startups to public institutions, on products that have to hold for real users.
Why Zheat
Why this is safer than hiring or another agency
Senior engineers, production-focused delivery, and a commercial model that keeps us accountable after launch.
Senior engineering
Software engineers who put AI in production, not a bench of juniors wrapped in agency process.
Production-focused delivery
The demo is easy. We design for real users, real load, and real systems.
Performance-based engagement
A defined production outcome, not an open-ended retainer.
No lock-in
Your code, your infra, your data. No platform lock-in.
You own the product
You get the code, the AWS CDK definitions, and a handover.
Post-launch accountability
60-day guarantee: bug fixes after launch, money back if we miss the agreed scope.
Team
Senior software engineers who put AI in production.
We put AI in production and stay on when quality slips: drift, hallucination, stale models.
Who this is for
Built for teams stuck between prototype and production.
If you already have something that works in a demo, and you cannot yet put it in front of real users, this is the work.
You already have
- An AI prototype
- A working POC
- Early users
- A validated business case
But you're blocked by
- Production engineering
- Infrastructure
- Scaling
- AI reliability
- Integrations
- Security
- Lack of internal capacity
What production actually requires
We ship with
Testimonials
What clients say
Feedback from teams we've shipped with.
Used daily by 1,000+ employees
“I had the pleasure of working with Yohan (Zheat) on the Alfred AI product at Bpifrance. When I arrived as Scrum Master, the chatbot had only a handful of users. I saw firsthand how Yohan, who initiated the project with the Product Owner, drove it technically into a stable solution now used daily by more than 1,000 employees. Yohan (Zheat) has a rare talent for turning an innovative idea into a high-performing production product. I strongly recommend Zheat for any ambitious AI project.”
Shipped an LLM pipeline the team could own
“I worked with Yohan at Bricks on an LLM-agent document extraction pipeline, in a context that was still loosely defined. Despite light scoping, Yohan moved autonomously and delivered a solid result. He did not stay inside the brief we gave him: he challenged the need, helped think it through, and proposed reduced-scope tests so we could evaluate with data before going further. The solution that came out of that was genuinely fitted to our need. Beyond the deliverable, it was the accompaniment that made the difference. Yohan took the time to explain his choices, hand over what he had put in place, and leave us able to take the subject back. The handover was careful and useful. I recommend him for LLM engineering and document extraction work, especially when you need someone who can propose a relevant solution, ship it with little context, and make sure it transfers.”
Shaped early customer-facing AI in production
“Radjiv worked with us for many years and became a highly valued member of the team. His ability to operate independently while delivering at speed is one of his greatest strengths as a software engineer. He thrives in fast-moving environments with evolving requirements, where pragmatism, efficiency, and rapid iteration are valued. Broadly scoped projects where he can take ownership and keep momentum are where he does his best work. In his later years with us at Ynomia, Radjiv also played an important role in several of our early AI initiatives. He helped establish an internal MCP to support our development workflows and contributed to shaping some of the first customer-facing AI features in our platform. Given the pace at which the AI space is evolving, I'm confident he'll continue to add immense value in this area.”
Hit tight deadlines and shipped more than requested
“Radjiv joined our team to lead the front end design and development of several high visibility and important projects which had extremely tight deadlines. The remit of the projects was for Radjiv to both design and deliver the projects he was responsible for. Radjiv not only met the deadlines with the functionality originally requested, he exceeded them by delivering more feature rich applications - much to the delight of the sponsors. I couldn't recommend Radjiv highly enough - he is a fantastic asset, engineer and team mate. I sincerely hope to work with him again in future.”
Recommended for complex, multi-country delivery
“Radjiv joined my team (Wireless / Methods & Tools Support) on October 2012 as a software designer in charge of developing web-based applications for tests management. While Yohan joined another team in a different unit of the Nokia submarine network. Due to excellent interpersonal skills and abilities to evolve in an international and multicultural environment (serving 3 regions, 15 countries), Zheat demonstrated a strong acumen to fit with team's spirit and to understand business needs he had to design features for. They are dedicated, customer-oriented and able to balance between IT constraints and internal customers' requirements. Radjiv and Yohan are hard-workers, tenacious and able to search for the most efficient solutions even when starting from an unknown context. In a short time, they constructed an excellent networking within the Wireless business unit and this made themselve able to gather business needs as well as to translate them into specifications understandable by technical staff. Thanks to its very good professional start, both were proposed to stay with Nokia for 2013 and beyond. I keep on meeting with Radjiv on a regular basis and remain interested by its potential, entrepreneurial mindset and ability to create value with SW applications and new usage around mobility. I am convinced he owns all assets to supply IT and telecom companies with the best services and this is why I warmly recommend them for a new project.”
FAQ
Questions that usually block the decision
Direct answers. If you still need more, the production assessment is the next step.
Free production assessment
Ready to put your AI into production?
Get a free production assessment and understand what it will take to turn your prototype into a production-ready product.