This assessment analyzes your data architecture and operational workflows to determine if they can support a Large Language Model locally.

It provides a strict pass/fail decision before you purchase hardware. 

ASSESSMENT SCOPE

Use-Case Limits: Verify if your specific goals (e.g., staff LLM usage, automated account research, parsing messages, sending emails, etc.) can be executed on local hardware.
Data Architecture: Map your current data silos (CRMs, databases, disparate inboxes, etc.) to determine if extraction and vectorization are viable.
Pipeline Feasibility: Assess whether your data can be structured, chunked, and fed into an embedding model at an affordable cost, without destroying semantic retrieval.

$300.00 USD // 50-Minute Evaluation

The fee is fully credited toward deployment.

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faq

Why is there a fee for an initial evaluation call?

Free calls are designed to sell you a contract, which is why we do not do them.

The $300 fee funds pure engineering time. We evaluate your data structure to prove feasibility on paper before you fund a single line of code or ship a single server.

What exactly happens at the end of the 50 minutes?

You receive a binary outcome. Either your infrastructure is cleared for local AI deployment, or it is found to be non-feasible; typically because it requires significant workflow changes in how you run your business.

Either way, you are provided with a Specification Document outlining exactly what hardware to buy or the specific structural problems you must fix. [ View Example Spec Document (PDF) ]

If we are approved, what comes next?

Because every corporation is unique, Private AI deployments are handled case-by-case. 

If cleared, we’ll define the specific hardware rig (e.g., GPU requirements, localized storage) and your custom data ingestion pipeline (e.g., async daemons, CRM scrapers) required to build your system. 

You’ll be presented options based on your objectives, budget, and timeline. [ View Example Full Engagement Document (PDF) ]

Do I need to provide access to our proprietary data during this call?

No. We only require a high-level understanding of your data formats, volume, and storage locations to run the calculations. We do not ingest or touch your live systems during this diagnostic.

Can't we just use OpenAI or Anthropic instead of building local hardware?

You can, if you are willing to send data through public cloud endpoints. Public AI is perfectly fine for many operations.

We build localized infrastructure for businesses who require absolute data isolation, zero recurring token taxes, immunity from upstream API breakage, or technical mitigation of discovery risks (executed exclusively for legal counsel).