Private AIinfrastructure.
On-premise AI workstations for firms that can't put their data in the cloud. Engineering, defense electronics, regulated, NDA-heavy work. Owned hardware, owned configuration, owned data — the OpenAI alternative for buyers who need their IP to stay under their roof.
The honest framing
Most AI infrastructureisn't built for buyers like you.
The default AI deployment story is cloud. Send your prompts to OpenAI. Send your documents to Claude. Send your customer data through a third-party vector store. That works for businesses where the data isn't the moat.
For engineering firms with proprietary designs. For contractors handling regulated information. For any business where a customer's IP, a partner's NDA, or a program's classification posture means the data cannot leave your environment — the cloud-first AI playbook doesn't apply.
That's what this product is for.
Sovereignty
Four things that changewhen the data stays put.
Data never leaves your environment
All routine inference happens on the workstation. Training data, customer records, design files, IP — none of it goes to a cloud vendor for the local work. Frontier-model calls (Claude, OpenAI) are configured per-workflow with explicit egress rules, not by default.
Compliance posture, not compliance theater
Built for environments where ITAR / CMMC / HIPAA / SOC 2 / NDA constraints are real. The architecture supports the audit story (data flow diagrams, egress logs, access controls) — not just the marketing claim.
Multi-box deployments scale linearly
One box per role, multiple roles per program, multiple programs per firm. We rack-mount, network, and configure them as one logical fleet — same Intelligence Hub OS across all units, shared knowledge corpus, role-specific agents.
Owned hardware, owned configuration
You own the workstations. You own the configuration. You own the model weights on each box. We own the ongoing relationship — maintenance, role evolution, model updates, engineering bench you can call when something needs to change.
Multi-box deployments
One box per role.One fleet, one OS.
Most engineering firms need more than one box. An engineering cockpit on the design lead's desk. A test-bench assistant in the lab. A reverse-engineering sidekick at the parts bench. We rack-mount, network, and configure them as one logical fleet — same Intelligence Hub OS across all units, shared knowledge corpus, role-specific agents.
Two to twenty boxes is the typical deployment range. We don't ship single-box engagements at this scale; we ship the rack and the configuration as a package.
Role configurations
Six roles we configure forengineering + regulated firms.
Engineering Cockpit
Design notes, drawings, BOM data, vendor cut-sheets, redlines — indexed and recalled per project. Drafts ECNs, surfaces prior decisions, flags interface conflicts before they ship.
Reverse Engineering Sidekick
Photograph or scan a part. Get probable schematic, candidate datasheets, equivalent components, and cross-references against your existing catalog. Engineer reviews and refines.
Test Bench Assistant
Reads instrument output, correlates measurements with the test plan, drafts the test report, flags anomalies against historical baselines. Test engineer reviews and signs off.
New-Hire Buddy
Trained on your firm's institutional knowledge — past projects, process documents, tribal-knowledge interviews. Cuts onboarding ramp from quarters to weeks.
Marketing Department
Capability statements, white papers, conference abstracts, SBIR/STTR narrative drafts. The same content discipline Blue Octopus runs internally, configured for technical-marketing teams.
Chief of Staff
Cross-cuts every other role. Surfaces what needs attention this week, what's stale, what's blocking who. Single pane of glass across the firm's active programs.
These are the role configurations we ship for engineering and regulated firms today. New role configurations get developed per engagement — if the role doesn't exist on this list yet, we build it.
The same product, framed for you
Same hardware. Same OS.Different audience.
The engineering box on the previous page is the same workstation we configure for mid-market commercial trades — inside sales, submittal coordination, change orders. Different role configurations, different sales motion, same hardware + Intelligence Hub stack underneath.
We separate these landings because the buyer language and the compliance story diverge enough that one page can't serve both audiences honestly. The product is identical; the framing is different.
Who it's for
Engineering firms with proprietary designs. Defense electronics. Regulated industries (medical device, financial, aerospace, energy). NDA-heavy contractors. Manufacturing firms with IP that can't leave the building. Anyone whose default reaction to “send it to ChatGPT” is a hard no — for good reasons.
Who it's not for
Firms whose data isn't actually sensitive (the cloud-first path is cheaper and easier — use it). Anyone shopping for the cheapest AI infrastructure (this is meaningful capex). Buyers who want a one-time install with no ongoing relationship (the OS evolves; the engagement does too). Firms that haven't done the data-substrate work yet (we'll start with that).
Ready to keep your IPunder your roof?
Discovery call first — your existing infrastructure, your compliance posture, the role configurations that match your organization, the deployment scale. We propose the engagement; you see the scope and the number before you commit.