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ARCby FenxLabsCollective Intelligence

Platform

  • How it works
  • Custom routing and governance
  • Use cases
  • Example architectures

Deployment

  • Hosted Platform
  • Managed Service
  • Self-hosted Licence
  • For ISVs
  • Open the portal

Services

  • Assessment and planning
  • Architecture and integration
  • Model Adaptation
  • EU AI Act readiness
  • Managed Service operations

Resources

  • Cost modeller
  • Compare approaches
  • Pricing
  • FAQ
  • Transparency
  • Get started
  • Discord community

Legal

  • Privacy notice
  • Terms and conditions
  • Sub-processors
  • Accessibility

For individuals

  • FenxChat

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Herengracht 320, 1016 CE Amsterdam, Netherlands

+31 85 060 5273contact@fenxlabs.ai

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Assessment and planning

Choose the model mix before you commit

Candidate models tested against the work your people, applications and agents will send, then turned into an infrastructure, placement and cost plan you can defend before you commit.

Discuss your requirementsRun your own numbers

Most engagements open with a paid proof of concept, deliberately small.
One bounded workload and one measurable outcome, used to test the approach before the scope grows. Scoped to your requirements and quoted per engagement.

You can check the assumptions behind every decision

Six decisions, written down, with the assumption each one rests on beside it.

  • Recommended model mix by workload
  • Proposed provider, private or self-hosted placement
  • Volume, throughput and infrastructure assumptions
  • Security and data-boundary requirements
  • Cost model and commercial options
  • Hosted, managed or self-hosted deployment recommendation
Choose where ARC runsRun your own numbers

Assessment and planning does not require ARC. The recommended deployment is part of the decision, not a condition of the engagement.

Evidence from your own workload

A public ranking measures a model against someone else's tasks. Candidates here are compared on yours, across provider-hosted and open-weight options, on quality, cost, performance and deployment fit.

  • Candidate model selection
  • Workload-specific evaluation
  • Quality testing
  • Cost and performance analysis
  • Provider comparison
  • Open-weight versus proprietary model assessment
  • Deployment and operating analysis
If nothing in the field closes the gap, that is Model Adaptation, and it starts from the same evidence this evaluation produces.
Adapt a model around your data

We size the compute and negotiate the rate

Where the plan calls for dedicated compute, that requirement is sized before anything is bought.

Sizing

Sized against the volume, throughput and infrastructure assumptions the plan already wrote down. The number traces back to the evaluation.

Negotiation

FenxLabs negotiates the provider arrangement on your behalf. Providers publish very different rates for the same hardware, and they negotiate differently again.

Build the plan around your workload

Thirty minutes with an engineer. Your numbers. A straight answer.

Discuss your requirementsHow we connect it to your stack