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, and they deploy where your data already lives: 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
Connects your agents to everything your organisation already knows, with citations and access control. Every answer can be traced back to its source.
See RenBase- RenLayerThe control tower
Sees everything your agents do, keeps them under control and brings the cost 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.
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.
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. The first week defines the map, the second puts an agent in production.
The way we work is an AI Lab inside your company, a mixed team of your people and ours that builds the first agents with you in the room and hands you the engine. 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. When the lab wraps up, the company knows how to build them and knows which ones pay.
Contact
If you want agents in production inside your own perimeter, and still yours the day we leave, let's talk.
Talk to us