Productized Service
Sovereign AI Architecture Review.
A fixed-scope, vendor-neutral review of your private AI plans: model selection, hardware sizing, RAG design, deployment topology, control evidence, and compliance posture.
What You Get
Fixed scope. Fixed price. Written output.
No open-ended consulting engagement. One session, one document, one action plan — priced so a single avoided hardware or vendor mistake pays for it many times over.
90-minute working session
A structured video call walking through your use case, data sensitivity, infrastructure, and constraints — not a sales call.
Written findings document
Delivered within 5 business days: recommended model stack and sovereignty tier, hardware sizing, runtime topology, RAG design notes, and prioritised risks.
Compliance posture notes
How your design maps to EU AI Act obligations, GDPR/DPDP data-residency expectations, and audit-logging requirements.
30-day action plan
A concrete next-30-days sequence your team can execute, plus 2 weeks of async email follow-up for clarification questions.
Pricing
Fixed quote by email
You know the exact scope and cost before committing to anything
Turnaround
5 business days
From session to findings document
Payment
No online payment
Everything starts with an email conversation — requesting costs nothing
Who You Work With
Reviewed by the person who builds these systems
Rana Kumar
15+ years in data engineering and AI infrastructure
Hands-on architect behind SovAIHub's open-source air-gap AI kits, private RAG reference implementations, and the sovereign model registry — working across enterprise data platforms, Kubernetes/OpenShift AI deployment, local LLM runtimes, and AI governance. The findings document you receive is written by the same person who runs the session, not handed to a junior team.
Good Fit
When this review pays for itself
- You are planning a private RAG or document-AI system and want the architecture validated before you build
- You need to choose between self-hosted open-weight models and cloud-tenant managed models
- You must size GPU hardware for a budget request and want an independent second opinion
- You operate in a regulated industry and need the deployment to survive an audit
- You are preparing an air-gapped or restricted-network AI deployment
Process
Four steps, no surprises
1. Book
Send the request form. You get a short intake questionnaire and a scheduling link.
2. Intake
You describe the use case, data classes, current stack, and constraints. Takes ~20 minutes.
3. Session
90-minute deep-dive call. We work through architecture, models, hardware, and compliance.
4. Findings
Written findings document with recommendations and a 30-day plan, within 5 business days.
Beyond the Review
Need hands-on implementation help?
If the findings call for it, implementation support is available as a separate custom engagement — private RAG builds, OpenShift/Kubernetes AI deployment, air-gapped artifact supply chains, and governance tooling.
Scoped individually, with a fixed proposal based on the findings document.
Review customers get a scoped proposal based on the findings document — no discovery fee, no re-explaining your context.