Your company's intelligence should be yours.
Renaiss deploys the infrastructure that lets an organisation run AI in production on its own knowledge, under its own rules, inside its own perimeter.
Mission
We started Renaiss because we believe AI is going to rewrite how every company works: how decisions get made, how a customer is served, how a contract is reviewed. That part is no longer up for debate and a good share of that work is going to be done by agents.
What is still up for debate is who will own that intelligence. Today most companies are renting it. The model belongs to someone else. The context lives outside. Nobody can explain why the system answered the way it did. For a pilot that is enough. For anything that touches a customer, a contract or a patient, it is not. If you can't trace where each answer came from, you can't fix the one that goes wrong either.
We believe a company's competitive advantage will no longer come from the model, which is on its way to being a commodity, but from two things nobody can copy: what only that company knows and its ability to show how it uses it. Our mission is to build the engine that turns both into agents of its own, running inside its perimeter, owned rather than rented.
Approach
An agent inside a real company runs into three problems no model solves on its own:
- Without context, an agent is generic. It knows a great deal about the world and nothing about your business: your products, your policies, your ten years of documentation, your customers.
- Without governance, an agent is not defensible. If you cannot see what it did, why it did it and what it cost, it is not a production system: it is an experiment with access to your data.
- Without sovereignty, an agent is not an asset. It is a subscription that can change its price, its data policy or its owner.
That is why we build an engine and not applications. All three pieces are proprietary technology, born out of our own research. They deploy where your data already lives, be it on-premise, cloud or hybrid. The agents that come out of it are yours and they keep running once we are gone.
When governance sits in the architecture, meeting the AI Act, GDPR or a SOC 2 comes down to documenting what the system already does.
For us sovereignty is an architectural decision, and it gets made before the first line of code.
Technology
- RenBaseThe company's memory
The governed corpus of everything your organisation already knows, versioned and access-controlled, that nobody uses until a person approves it. Metrics and business rules live there as named objects, not as passages you have to find by resemblance. Every answer traces back to its source. Where the corpus falls short, the agent abstains.
See RenBase- RenLayerThe control tower
A transparent proxy in front of OpenAI, Anthropic, Bedrock and Vertex that asks you to touch no code at all. It surfaces the AI already in use that nobody authorised and decides on every call whether to allow, flag or deny. It leaves an immutable audit trail, classifies each agent against the EU AI Act and brings the token bill down. It is the difference between a demo and something a bank is willing to run in production.
See RenLayer- RenFoundryThe agentic engine
Not one more agent platform. It is a coding agent, trained to build agents in whatever stack you choose and to wire them into RenBase for context and RenLayer for control. It works inside your environment, and the code it writes stays with you.
RenBase gives the context RenFoundry builds RenLayer controls.
Your perimeter
- Your knowledgeDocuments, policies, history and the systems your people already work in.
- RenBaseTurns it into a governed corpus that no agent uses until a person approves it. Every answer cites its source, its version and who signed it off.
- RenFoundryBuilds the agent in here, in the stack you choose, leaving the code where it built it.
- RenLayerApplies the policy before the call leaves and records what it did, why and what it cost. Any decision can be reopened months later, with the audit chain verified.
- The answerComes out with its source and the trail behind it, which is what someone will have to defend. Where the corpus does not support it, there is no answer.
OutsideThe only piece that may sit outside is the model, which is replaceable anyway. Where your policy does not allow it, that goes inside too.
Use cases
From reviewing a sample to auditing 100% of the work.
An energy company automated the validation of its construction certifications. Six people were getting to 40% of more than 100,000 certifications a year. Today every one of them is reviewed, against the company's own engineering criteria.
- 100,000+
- Certifications/year
- 100%
- Coverage (was 40%)
- 2.5×
- Volume processed
- 6
- People freed up
The ones who went first
Industry leaders that moved before the rest of their sector did.
AI Lab
Not a vendor. A lab we set up inside your company.
A mixed team of your people and ours that builds the first agents with you in the room. We do not train only the engineers. We also train the people who decide where an agent is worth putting and what to do with the time it frees up.
What you bring
- A real process, with volume and with an owner.
- Access to the documentation and the systems where the criteria actually live.
- Two or three of your own people. Whoever does the work today and whoever will have to maintain it afterwards.
What we bring
- RenBase, RenFoundry and RenLayer deployed in your environment.
- Engineers who have put this into production before, sitting with yours.
- The judgement to throw out the cases that do not deserve an agent.
What you keep
- The agents running and the code that builds them.
- The engine inside your perimeter, with us out of the way.
- People of your own who can build the next agent and tell which ones are worth it.
The first weeks
- Week 1
- The mapWe walk the candidate processes and keep the ones that meet all three conditions. What comes out is a list ranked by what it is worth and what it costs, plus a few reasoned refusals.
- Week 2
- First agent in productionOne narrow case, real volume, inside the workflow of the people doing it by hand today. Not a demo on a screen of its own.
- Month 2
- The engine in your handsRenBase on your corpus, RenLayer measuring every decision and what it cost, RenFoundry inside your environment. Your engineers build the second agent and we review it.
- On closing
- The handoverWhat stays is the code, the engine and the person who knows which agent to build next. If we carry on it is because you chose to, not because it stops without us.
Team
Renaiss comes out of a research and engineering team that has spent years on AI systems that have to work: in production, on real data, under regulation.
Research and delivery sit in the same place. Whoever designs the system is the one who deploys it and the one who sits down with your team afterwards.
Contact
If you want agents in production inside your own perimeter, and still yours the day we leave, let's talk.
Talk to us