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Evaluation helps your team decide whether the Subconscious Inference System is the right customer-hosted path before implementation starts. The goal is to align on:
  • Business value: cost, productivity, and expected adoption.
  • Technical fit: cloud, Kubernetes, GPU, networking, and coding-agent requirements.
  • Security review: data boundary, vendor assessment, and compliance fit.
  • Proof points: what must be validated before deployment.

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Discovery

Discovery starts with your engineering workflows and coding-agent usage. Key topics:
  • Engineering team size and expected adoption.
  • Current coding-agent usage and preferred tools.
  • Target model quality, latency, and throughput expectations.
  • Workload patterns and anticipated token volume.
  • Existing cloud, Kubernetes, GPU, and networking constraints.
  • Security, compliance, data residency, and vendor review requirements.
  • Success criteria and evaluation timeline.
Output: a clear evaluation path: what must be validated, who needs to participate, and which deployment model is most likely to fit.

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ROI exercise

The ROI exercise compares customer-hosted Subconscious against frontier hosted APIs, generic self-hosted GPU options, and unmanaged open-model serving. Useful inputs:
  • Expected number of engineers and pilot users.
  • Agent usage patterns and expected daily or monthly token volume.
  • Target model families and quality expectations.
  • Current hosted API spend or internal GPU cost assumptions.
  • Latency and throughput targets for interactive coding workflows.
  • Required control over data, networking, infrastructure, and deployment cadence.
Output: a shared view of expected savings, GPU needs, reliability goals, and what the pilot or deployment must prove.

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Security review

Security review usually runs alongside commercial and technical evaluation. Key question: Does the customer-hosted model fit your data, IP, security, and compliance requirements? Subconscious can support review with:
  • Security architecture overview.
  • Data-flow and data-retention summary.
  • API Gateway, Inference Runtime, and Distribution Platform boundary.
  • Access control and support access model.
  • Vulnerability management and patching process.
  • Release evidence, SBOMs, vulnerability reports, or related supply-chain materials where available.
  • Shared responsibility guidance.
For the detailed security position, see Compliance. For deployment mechanics, see Methods and Distribution Platform.

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Optional model comparison trial

Some customers evaluate model quality before committing to a full customer-hosted deployment. Typical shape:
  1. Select 1-4 engineers or a small pilot group.
  2. Choose representative coding-agent workflows.
  3. Point local coding agents at open models hosted on-demand.
  4. Compare model quality, compatibility, latency, and workflow fit.
  5. Capture gaps or configuration requirements before deployment planning.
Security note: Cloud hosted trials are separate from customer-hosted production deployments and should be evaluated under their own data-handling assumptions.

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Optional load-test trial

A load-test trial validates whether Subconscious can serve the target engineering capacity with acceptable latency, throughput, and reliability. Typical shape:
  1. Agree on traffic assumptions or benchmark tasks.
  2. Provision an appropriate GPU environment.
  3. Run throughput and latency tests on representative workloads.
  4. Compare results against success criteria from discovery.
  5. Decide whether to proceed to deployment planning.
Success criteria: against target engineering capacity, there is acceptable latency for coding-agent workflows and stable throughput under expected load.

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Contracts & onboarding planning

If we agree there is a fit, we move forward with contract negotiation and onboarding planning. After that, customers move into Onboarding.