In-house data engine
Low-delay data transfer with minimal overhead. Performance targets are agreed for each project.
Solution family
R&D & Applied AI
Research, prototype and validate new AI and connected-system capabilities.
What this solves
- Standard web protocols add delays that some real-time systems cannot accept.
- Busy services slow down unpredictably under heavy load.
- Relying only on external cloud AI adds cost, delay and data privacy risk.
- AI models that work in a lab often fail on the limited hardware of real workplaces.
Capabilities
What this family covers in practice.
Stable performance under load
Careful memory and resource management to keep response times steady when load is high.
Modern encryption
Encryption that protects data in transit while keeping critical paths fast.
On-premise AI
AI models run on your own workstations and devices, so private data can stay inside your premises.
Multi-agent research
Research into how AI Agents divide work, create tools and coordinate safely with each other.
Benchmarking and verification
Automated tests that measure speed, capacity and accuracy before rollout.
Example workflows
Example workflows we configure first.
Performance engineering
- Design compact data formats for fast transfer.
- Reduce unnecessary copying and processing in the data path.
- Add encryption to protect data in transit.
- Test under simulated heavy traffic to confirm stable behaviour.
Preparing AI to run on-site
- Compress and convert the model so it runs on smaller hardware.
- Tune it for the processors available on site.
- Measure accuracy against your benchmarks and report any change.
- Deploy the model locally so it works without sending data to the cloud.
From prototype to production
- Agree measurable targets for speed, resources and capacity.
- Run a pilot alongside your existing systems.
- Measure the results against the agreed targets.
- Move verified modules into production software, with documentation.
Integration & governance
- Data engines follow open standards where possible, so they can work with other systems.
- Encryption uses established, widely reviewed algorithms and libraries.
- On-premise AI models and customer data stay within your environment.
- Experimental features are stress-tested, and their limits are documented before use.
Frequently asked questions
What does EYC's data engine help with?
It keeps data transfer lean, so systems stay responsive under load. Performance is measured during your pilot.
How do you keep performance steady under heavy load?
By controlling how memory and resources are used, and by testing under realistic load before rollout.
Why run AI on-premise instead of using cloud AI services?
Running models on-premise avoids per-request fees from external AI services and keeps confidential data inside your environment. The right choice depends on your workload, and we can help compare.
How does R&D work become production software?
Prototypes must meet agreed targets for speed, capacity and error rates in a pilot before they are moved into production.