MULTI-AGENT ASSET
Deploy agents at scale, while staying in control
Become AI-native and expand your internal team or your product with agentic capabilities: on one proven harness that keeps deployment, security, and observability under a single set of controls.
TRUSTED BY CLIMATE INNOVATORS
THE BUSINESS PROBLEM
Where agent projects stall once real operations are involved
Inconsistent outcomes
Agents that impress in a demo start making costly mistakes the moment they meet real operational data and live customers. Consistency, observability, guardrails, and evaluations are key.
Data is siloed
Structured and unstructured sources never talk to each other. Providing the right data & context to your workflows is critical.
Software best practices
Operationalizing agents at scale requires fallbacks, security measures, and the testing and release discipline of any production system.
THE SOLUTION
Production-grade Agentic AI to become AI-native
Every agent you build runs on the same four layers: the interface your users see, the harness that keeps agents under control, the ontology that holds your domain knowledge, and the infrastructure you own.
Experience layer — generative UI
The interface adapts to the question instead of forcing people through a fixed screen — and it sits wherever the process already runs, embedded in your existing product or as a new one.
TWO DEPLOYMENT SHAPES
Customer-facing interface
Customers ask real questions and get structured, sourced answers back in seconds.
Internal workflows
Automate the operational work your team repeats wired into the systems you already run on.
It runs on the stack and the application you already have.
Agents slot into your existing systems and your existing product.
Agent layer — the harness
One proven harness to build many different agents quickly, and stay in control of all of them.
WHAT COMES WITH IT
Controls, not per agent
Deployment, security and observability are part of the harness — the tenth agent is governed the same way as the first.
Continuous feedback
Built-in evaluation and monitoring, with feedback collected and fed back in, so accuracy keeps improving after launch.
It runs on the stack and the application you already have.
Agents slot into your existing systems and your existing product.
Data layer — ontology and semantics
Your domain knowledge encoded into a structured ontology that is yours to keep, extend and reuse.
WHY THIS LAYER DECIDES IT
Where most agent projects go wrong
Skip this layer and every new use case starts from zero — nothing you learn on one agent carries to the next.
Built once, reused everywhere
One semantic layer serves every agent on top of it, so the second use case is faster than the first.
It runs on the stack and the application you already have.
Agents slot into your existing systems and your existing product.
Infrastructure layer — trust and ownership
Model-agnostic by design, audit-ready, and built so your team can take the whole thing over.
WHAT YOU GET
No vendor lock-in
Swap models, providers or infrastructure without rebuilding what sits on top of them.
Enterprise-grade trust
GDPR and ISO 27001 aligned by design, with a full audit trail on every agent action.
EU hosting, optional
Available when your compliance setup calls for it — a choice, not a constraint.
Built for full handover
Your team owns the code, the infrastructure and the knowledge. No dependency on us to keep it running.
It runs on the stack and the application you already have.
Agents slot into your existing systems and your existing product.
WHY IT WORKS
Four layers, four reasons it holds up
Adapts to the question
Generative UI shapes the answer around what was asked, instead of routing people through a fixed screen they have to work around.
Many agents, one set of controls
Deployment, security and observability come with the harness — so the tenth agent is governed the same way as the first.
Your ontology is your asset
Domain knowledge is encoded once into a semantic layer you keep, extend and reuse, so the next use case does not start from zero.
No lock-in, full handover
Swap models, providers or infrastructure without rebuilding, and your team owns the code and the knowledge when we leave.
From initial prototype to scalable production systems
100+ companies trusted us to build their data & AI layer since 2018
Shypple
Turning manual document-heavy logistics workflows into scalable, automated AI operations for a fast-growing freight platform.
- Mobility & Transport
- LLM Systems
Next Sense
Co-designing a multi-agent AI system with sustainability experts to scale building performance analysis without compromising quality.
- Buildings & Industry
- LLM Systems
Beyond Weather
AI infrastructure to commercialize complex weather forecasting models from research to production environment.
- Climate Intelligence
SnappCar
New ML Ops setup delivers 50% faster queries and significant cost reduction for peer-to-peer car-sharing platform.
- Mobility & Transport
- ML Systems
Groendus
Build an AI forecasting system for Groendus that outperforms the benchmark for energy consumption.
- Energy & Electrification
- Data Systems
CtrlChain
Built a scalable data, analytics, and ML platform with an FTL spot pricing model in under 12 months.
- Mobility & Transport
- ML Systems
Next Sense
Co-designing a multi-agent AI system with sustainability experts to scale building performance analysis without compromising quality.
- Buildings & Industry
- LLM Systems
Shypple
Turning manual document-heavy logistics workflows into scalable, automated AI operations for a fast-growing freight platform.
- Mobility & Transport
- LLM Systems
CtrlChain
Built a scalable data, analytics, and ML platform with an FTL spot pricing model in under 12 months.
- Mobility & Transport
- ML Systems
Sendcloud
Aligning sales, marketing, and customer success around a single ML-driven metric to unlock sustainable growth at scale.
- Mobility & Transport
- ML Systems
Beyond Weather
AI infrastructure to commercialize complex weather forecasting models from research to production environment.
- Climate Intelligence
Crisp
Implement a new forecasting system for Crisp that improves prediction accuracy (WAPE) by >20%.
- Other
- ML Systems
QUESTIONS
Frequently asked questions
How quickly can we have an agent in production?
Most engagements reach a real production deployment in six to ten weeks. The first two weeks are scoping and ontology work; after that we ship into a live environment early, so evaluation runs against real traffic instead of assumptions.
How do you stop agents from giving wrong answers?
[PLACEHOLDER — answer copy pending]
What happens when we want to change model providers?
[PLACEHOLDER — answer copy pending]
Where does our data live, and who can see it?
[PLACEHOLDER — answer copy pending]
What do we own when the engagement ends?
[PLACEHOLDER — answer copy pending]