Services · On-Prem & Intelligence Sovereignty
Your data shouldn't live on someone else's server.
What this means for your business
Your AI runs on a box in your building — not in a data center somewhere. You stop paying per-query cloud fees. Your sensitive documents, financial records, and internal processes never leave your property. The AI gets smarter every month as we update it. You own the hardware outright.
What it is
A physical AI system on your premises. Full stop.
This is not hosting. It is not hardware resale. It is Krastor's architecture and intelligence layer running on hardware the client owns, on the client's property, with no data ever leaving the building.
The monthly cloud API bill goes to zero. The compliance exposure goes to zero. The AI gets better every month as open-weight models improve, on the same hardware, no migration.
Local inference layer
Open-weight model stack
Retrieval layer over your data
Autonomous workflows
Observability
Secure remote access
Air-gapped deployments
Local RAG over private data
On-site fine-tuning
Hardware sizing & procurement guidance
Backup, failover & disaster recovery
Hybrid cloud/on-prem routing
Security Hardening & Risk Baseline
Where we deploy
Three deployment scales. One architecture.
Hardware is always client-purchased direct to the vendor. You own the asset. Krastor's fee is architecture, build, and maintenance only. These are not packages; they are calibration points for different operating scales.
Entry: Proof of Concept
SMB Production: NVIDIA DGX Spark
Regulated Enterprise
Edge & Embedded Intelligence
Cognitum: AI agents that live where your data is born.
For operations that need intelligence at the asset — not just at the server — Krastor deploys on Cognitum hardware. These devices run self-learning AI agents locally with no cloud dependency, at the edge, in real time. The Seed captures and processes data where it's generated. The Appliance is the sovereign network core it all reports to.
The CFO argument
The math works faster than most CFOs expect.
At 10,000 queries per day and 500 tokens per query, a reasonable volume for a 20-person team, cloud API costs run between $450 and $2,250 per month depending on the model. On-prem inference runs about $50 per month in electricity.
The DGX Spark hardware at ~$4,699 pays for itself in 3 to 12 months depending on current cloud spend. After that, every query is free, forever, regardless of how much the model improves.
There is no migration cost when better open-weight models ship. We point the system at the new model and the upgrade is live. You don't pay for the improvement; you just benefit from it.
The ownership model
You own your intelligence. You don't rent it.
Krastor charges for architecture, build, and maintenance. The hardware is yours. The data is yours. The AI is yours. The only thing that keeps you paying us is that we keep making it better, which is the incentive we want.
Architecture and build
We design the intelligence layer, deploy the models, wire the retrieval and agent systems, and get everything running. That's the engagement.
Maintenance and evolution
Models improve constantly. We handle the upgrades, monitor performance, and evolve the system as the technology moves. You don't manage any of it.
No lock-in
You own every component. If you want to bring it in-house, the system is yours to hand off. We don't hold anything hostage.
Model-agnostic by design
Any Krastor workflow already pointing at a cloud model re-points to the local server with zero rewrites. One config change and the cloud bill stops.
In practice
Four industries where cloud AI is a liability.
These are typical scenarios in verticals where compliance, competitive exposure, or data-residency law makes cloud AI the wrong answer.
A resort operator builds their full financial model and pricing logic inside a cloud-hosted chatbot. A competitor with cloud access to the same API can extract the methodology. The on-prem answer: financial AI, concierge, and predictive ordering all running locally, cloud dependency gone.
Attorney-client privilege makes cloud AI a liability, not an asset. A private legal-research layer with case-law retrieval and a full audit log of every query means associates get the answers while the firm keeps the privilege.
Examiners want a complete audit trail of AI-assisted decisions. An on-prem deployment with tamper-evident logs closes that conversation. Compliance posture improves; cloud AI spend goes to zero.
State data-residency requirements make cloud AI non-compliant. POS intelligence, inventory forecasting, and customer analytics all run locally: the regulator is satisfied, the system runs faster.
Questions
Straight answers.
Isn't on-prem AI worse than the big cloud models?
Open-weight models (Llama, Mistral, Qwen) are now strong enough for most business tasks: document Q&A, retrieval, summarization, classification, drafting. And we upgrade them free as better ones ship. The gap to frontier cloud models is narrowing every quarter, on the same hardware you already bought.
Do we have to buy the hardware?
Yes, and that's the point. You purchase it directly from the vendor; we never mark up hardware. You own the asset outright from day one. Our fee is architecture, build, and ongoing maintenance. There's no monthly lease, no subscription, no rent.
What if compliance is the blocker?
That's exactly the use case. Data never leaves your premises, query logs are yours, and the architecture is designed from the start around your specific regulatory framework: SOC 2, HIPAA, SR 11-7, state data residency. We've built for all of them.
Engagement starts here
Worried about where your data lives?
Book a diagnostic. We'll map what's exposed, what it's costing, and what a private, on-prem intelligence layer would look like for your operation.
Not limited to what's listed. Every engagement starts by assessing what your business actually needs, and we build whatever it requires.