Workload-fit review
Identify the visible demand patterns and operating pressure.
Design Partner assessment
A controlled route from workload pressure to confidential, evidence-led evaluation.
Begin with non-sensitive workload characteristics. Detailed technical information is shared only after an appropriate confidentiality agreement is in place.
The engagement boundary
High-level first. Confidential when technical.
The public route does not request internal algorithms, protected configurations, source code, raw decision records or sensitive workload data.
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Assessment offer
Define the workload, boundary and evidence standard before a pilot decision.
The assessment establishes whether there is a credible opportunity to test. A native baseline means the strongest stable comparison using the existing serving environment without S2. No savings percentage or customer capacity multiplier is inferred from a short enquiry.
Baseline capacity assessment
Establish the current service boundary before comparison.
Deployment-boundary review
Agree the model, serving environment, workload and tenant boundaries.
Validation-plan preparation
Define the quality, service and evidence requirements before testing.
Evidence and findings summary
Report supported results, limitations and the recommended next decision.
Engagement sequence
- Initial enquiry
- Non-confidential workload-fit discussion
- Confidentiality agreement
- Technical assessment
- Pilot decision
- Evidence-led deployment recommendation
Suitable assessment candidate
- Operates an AI inference workload
- Can identify its model and serving platform
- Has observable capacity, latency or queue pressure
- Can provide non-sensitive workload characteristics
- Will evaluate under agreed quality and service requirements
What the assessment is intended to produce
- A documented workload-fit conclusion
- A description of the observable operating constraint
- A native baseline position
- Agreed quality and service requirements
- A proposed validation plan
- Identified limitations and exclusions
- A go, no-go or further-evidence recommendation
- An evidence basis for a pilot decision
A go, no-go or further-evidence conclusion is valid. The assessment does not guarantee a pilot or favourable result.
What to prepare
- The model and serving platform
- Observable capacity, latency or queue pressure
- Non-sensitive demand characteristics
- Current quality and service requirements
- The definition of an acceptable outcome
- Whether additional GPU capacity is under consideration
- Any operating boundary that must not be crossed
Keep the first email non-confidential. Do not send confidential workload data, source code, credentials, raw prompts, protected configurations or detailed internal records.
Who you are engaging
ProgGen Zeroth AI Limited.
Detailed technical information is shared only through an appropriate confidential engagement.
- Legal name
- PROGGEN ZEROTH AI LIMITED
- Company number
- 17374280
- Company type
- Private company limited by shares
- Jurisdiction
- England and Wales
- Incorporated
- 31 July 2026
Prepare the first conversation
Start non-confidential. Progress with control.
The website prepares a draft to info@proggen.co.uk. Nothing is sent automatically or stored by the site. ProgGen reviews the non-confidential request and moves technical detail into an appropriate confidential engagement only when further evaluation is justified.
First discussion covers
- Serving environment
- Visible capacity pressure
- Evaluation requirements