AI and ML software development.
Senior engineers who have released AI systems that keep working under real traffic, not just in a demo: evaluation, safety checks, and the final steps that make a model into a product.
AI software built to keep working in production, not just in a demo.
Evaluation sets, not guesswork
Real test cases scored on every change, so quality is measured, not assumed.
Safety checks by default
Checks on what the system can say or do, built in from the first version.
RAG and retrieval
Answers based on your own data, so the model has facts to work from.
Agents with checkpoints
Multi-step systems that verify each step instead of trusting a long series of steps without checks.
Model flexibility
Architecture that does not depend on one provider, so a price change or deprecation is not a rebuild.
Cost at scale
Systems designed with per-request cost in mind before they reach real volume.
AI and ML products fail in a specific, predictable way. A demo works because someone picked the inputs. A production feature has to work when a real user types something nobody planned for, and it has to keep working after the model provider changes something on their end. Companies that build AI and ML software full-time, across a range of clients, learn to solve that problem. Companies that build one AI feature as a side project usually do not, because they only see the problem once, on their own product, after it is already live.
Why AI and ML software needs a different kind of team
A normal software bug is deterministic: the same input produces the same wrong output every time, so it is straightforward to reproduce and fix. A model is probabilistic. The same input can produce a slightly different output on two different days, and a prompt that worked well last month can get worse after a model update the provider did not announce. That difference changes how the whole system has to be built, tested, and watched.
Teams that specialize in AI and ML software treat this as the default condition, not an unusual case. They build an evaluation set before they release, so a change in the model or the prompt can be scored against real cases instead of a personal feeling. They build monitoring that watches for output quality getting worse over time, not just uptime. They design a fallback for when the model gives a bad answer, so one wrong response does not damage the whole product's reputation. Our AI development work is built around exactly this discipline.
What we build
RAG and enterprise search. Systems that base a model's answers on your own data, so it responds with facts your team can verify instead of a plausible-sounding guess. See RAG for enterprise search for how we approach retrieval quality specifically.
AI agents. Systems that take multi-step action, not just answer a question. Agents fail in ways a single model call does not, since one wrong step can lead to several more, so we build in checks between steps rather than trusting a long series of steps to self-correct. Our agentic AI framework post covers the architecture pattern we use.
Evaluation and safety checks. The part of an AI system most teams skip, and the part that decides whether it keeps working once real users arrive. We wrote up our full approach in AI evaluation and guardrails for production.
LLM integration into existing products. Adding a model-backed feature to software that already has real users and real data, without breaking what already works. See LLM integration for existing products.
Model selection and RAG vs. fine-tuning. Picking the right approach for a given problem and budget, instead of defaulting to whichever technique is popular right now. We cover the actual tradeoffs in RAG vs. fine-tuning for product teams.
Proof, not a pitch
The engagement patterns below are the forms this work usually takes. An AI agent in production is the one teams underestimate most: the model is the easy part, and the hard part is the interface that shows a user when the system is confident and when it is guessing, so a person can tell the difference before acting on the answer. A data platform engagement is the other common form, where the labeling and quality work that production models depend on needs tooling of its own before the model is worth trusting. Both are built the same way: the specification first, then an eval suite written from it, then the code that has to pass it.
How we assess an AI idea before we build it
Given how often AI projects fail on a misunderstood problem rather than a technical limitation, we spend real time at the start asking what would have to be true for the idea to work, what data actually exists to support it, and what happens the first time the model is confidently wrong. That framing work is not a formality. It is usually what separates an AI feature that gets released and keeps working from one that works well in one demo and is then dropped without any announcement. If you are earlier in that process, see how to evaluate an AI feature idea.
For the full build process, see our guide on building AI products, taking AI agents from prototype to production for evaluating and safely running agents, and taking AI-generated code to production for what it takes to make AI-written code safe to release.
Common questions
What makes AI and ML software development different from regular software development?
AI and ML software is probabilistic rather than deterministic, so the same input can produce a different output on two different days, and a prompt that worked last month can get worse after an unannounced model update. That difference means evaluation sets, monitoring for quality changes, and safety checks have to be part of the build from the first day, not added after launch once the gap between a demo and real traffic becomes visible.
Do you build with a specific AI model or provider?
Reveneau matches the model to the problem and the budget rather than defaulting to whichever technique is popular right now, and architects the system so switching providers later is not a rebuild. Depending on one vendor is a risk we design against from the start, since a price change or a deprecation on the provider's end should not force a rewrite of the whole feature.
How do you know an AI feature is actually ready to release?
A feature is ready when it has an evaluation set of real cases it is scored against, a defined fallback for when it gives a bad answer, and monitoring that would catch quality getting worse after launch. Without those three in place, what exists is a demo that worked because someone picked the inputs, not a product that has to work when a real user types something nobody planned for.
Can you add AI to a product we already have?
Yes, and most of our AI and ML work is exactly this: adding a model-backed feature to software that already has real users and real data, without breaking what already works. LLM integration into an existing product carries its own risk profile, since the new feature has to earn trust inside a system people already depend on, so it gets the same evaluation and safety-check discipline as a new build.
What is the biggest reason AI features fail after launch?
The team misunderstood the problem before writing any code, not a technical limitation in the model. That is why Reveneau spends real time at the start asking what would have to be true for the idea to work, what data actually exists to support it, and what happens the first time the model is confidently wrong, before committing engineering time to the build itself.
How do agents differ from a single AI feature?
AI agents take multi-step action instead of answering a single question, and that changes how they can fail. One wrong step in a series can lead to several wrong steps before anyone notices, so Reveneau builds checks between steps rather than trusting a long series of steps to self-correct on its own, which keeps a small mistake from becoming a large one later in the sequence.
Do you build RAG systems, or only fine-tune existing models?
Reveneau builds both and picks the approach that fits the problem and the budget instead of defaulting to whichever technique is popular right now. RAG bases a model's answers on your own data so it responds with facts your team can verify instead of a plausible-sounding guess, which is usually the right fit when the underlying data changes often or accuracy against a specific source matters most.
What happens to cost as an AI feature scales to real volume?
Cost gets planned at the start rather than discovered after launch. Reveneau builds systems with per-request cost in mind before they reach real traffic, since a feature that is affordable at demo volume can become expensive fast once real users are calling the model on every action, and a redesign after the fact is far more disruptive than planning for scale from the start.
Build with us, wherever you are
Fintech software development.
Software where the numbers have to be right the first time: real-time ledgers, transaction accuracy, and financial data a customer's own accountant can trust.
Healthcare software development.
Systems that put the right patient data in front of a care team fast enough to change what they do next, not weeks later in a static report.
Real estate software development.
Proptech where the software makes a decision about someone's housing: valuation models, tenant screening, and listing platforms that have to be explainable to the person they affect.
What we build
AI development company for products that launch, work, and last.
As an AI development company, we build agents, RAG, and ML systems that turn promising ideas into products people can rely on, with the evaluation and safety checks real usage demands.
Full product development, from strategy to launch.
One senior product team takes your build from discovery and design through engineering and a confident release, accountable the whole way.
Staff augmentation services that accelerate your team.
Staff augmentation done right: senior engineers, designers, and product people who join your team and release work from the first week, improving how you build rather than just adding people.
A product design agency for software that feels simple.
A product design agency taking you from product vision and brand principles through to polished, high-fidelity design systems that make complex products feel simple.
A custom software development company senior teams trust.
A custom software development company that designs, builds, and releases production software with senior teams, from a single feature to a full platform.
Forward deployed engineers who stay until the system is running.
Senior engineers who work inside your environment, on your real data and your real approval path, and who are accountable for the system running in production. A named production date and a named handover date, both agreed before we start.