The only infrastructure purpose-built for the traffic characteristics of AI agents — high-frequency, multi-hop, context-heavy, latency-critical.
Per-Agent · Sensitivity-Tiered · Enterprise-Grade
A lightweight gateway that sits between your AI agents and your LLMs — intercepting every query, caching semantically similar answers, and routing cache misses to the cheapest capable model. Agents run faster, cheaper, and fully sovereign. Zero rearchitecting.
Every LLM-bound agent query is captured at the gateway before it reaches the frontier. No changes to your agent code — one BASE_URL environment variable.
Drop-in proxyThe agent query is converted to a high-dimensional embedding using your preferred ML model, then matched against the per-agent vector index — locally, inside your perimeter.
Local embeddingsSemantically similar prior answers are served in <10ms at ~1/100th the cost of a fresh LLM call. The agent gets its answer. No token is spent. Data never leaves your perimeter.
<10ms · $0.002/MCache misses route to the cheapest capable model — Bedrock, OpenAI, Anthropic, or local — based on query complexity scoring. Agents never know the difference.
67% miss savingsEach pillar addresses a distinct enterprise buyer — four independent urgency triggers, four separate budget conversations, one infrastructure layer purpose-built for agentic workloads.
Forty-two percent blended cost reduction on day one — from agent query caching alone. Success-fee pricing means you pay 10% of measured savings. Zero upfront risk. The CFO doesn't need to believe in AI to close this deal. They just need to read the invoice.
Agent reasoning loops firing 100+ LLM calls at 2–8 seconds each create minutes-long workflows. Vector Vault makes them real-time. This is a product quality decision, not just a performance metric. Zero rearchitecting — one environment variable.
Route agents to Claude today, GPT-4o tomorrow, a fine-tuned local model next year — without touching agent code. Deploy on AWS, Azure, GCP, or on-prem simultaneously. Vendors know they're replaceable. You negotiate from strength, not dependency.
Local vector embeddings mean agent query intent, proprietary decision logic, pricing models, and customer PII never reach external LLM APIs. Every cache hit is a query that didn't leak. Architecture-level compliance — not a contractual promise.
Twenty years of shared history. One prior co-founded exit. The same intercept-cache-serve architecture — now applied to the AI agent token economy.
SVP Sales, STRATACACHE (IoT & retail video PaaS) — built AT&T's billion-dollar white-label channel from greenfield. Closed McDonald's, major banking & QSR enterprise accounts.
CRO, AgilePoint (low-code digital transformation) · CEO, DaNoraAI (AI content) · President, Brandometry (NYSE ARCA ETF co-founder) — career built at the intersection of enterprise software, AI, and capital markets.
Credentials — AWS Solutions Architect · AI/ML · FinOps · MIT MS Innovation · Harvard CS50 AI · MBA Finance Notre Dame.
linkedin.com/in/tonywenzel →SuperLumin Co-Founder — 15 years building semantic proxy cache infrastructure, deployed at Adobe & Luxottica. Acquired by STRATACACHE.
Juniper Networks — Director of Engineering, security cloud ops & CI/CD. Cisco Systems — contributed to $92M VOD acquisition.
Patented architect — multiple patents in secure proxy acceleration, transparent domain interception, and VPN cache.
linkedin.com/in/mdackerman →SuperLumin Co-Founder — SVP Engineering alongside Mark. NitroCast platform delivered 100Gbps+ per cache instance for enterprise service providers.
Juniper Networks — Senior Director of Engineering, routing and connected-security products at scale.
Data sovereignty specialist — designed cache systems where content containment was a hard requirement. Directly translates to VV's IP Security pillar.
linkedin.com/in/brentchristensen1000 →Mark and Brent co-founded SuperLumin Networks in 2007 — a semantic proxy cache for enterprise content delivery, deployed at Adobe and Luxottica and acquired by STRATACACHE. The intercept-cache-serve architecture is the direct technical predecessor to Vector Vault. Same model. New token economy. Market 100× larger.
Vector Vault is pre-revenue and actively raising. If you're building enterprise AI agent workflows, investing in AI infrastructure, or simply curious about what we're seeing in the field — reach out.
Tony Wenzel · Co-Founder & CEO
New York, NY
Connect on LinkedIn
Netflip · Vector Vault
Architecture diagrams, financial projections, valuation bridge, and Series A milestones are available to credentialed visitors.
Sensitivity Tiering: Each agent node carries its own cache policy — data classification level, retention window, permitted LLM routes, and perimeter controls. A CFO agent handling board projections operates under a fundamentally different policy than a customer service agent handling order status. The network manages this — not your dev team.
| Milestone | Month | ARR Target | Capital Deployed |
|---|---|---|---|
| 25 paying pilots · AWS Marketplace listing | M3 | $150K | $680K |
| 3 logos · SOC 2 Type II begins | M6 | $500K | $1.4M |
| SOC 2 certified · First regulated vertical | M9 | $1.5M | $2.8M |
| GDPR / HIPAA posture complete · MCP GA | M12 | $2.5M | $3.9M |
| First CISO-led deals · OEM conversations | M15 | $3.5M | $4.5M |
| Series A trigger · OEM deal target | M18 | $5M | $5.0M |