$1.5T in enterprise AI spending this year. Almost all of it goes to chat interfaces and API wrappers that demo well and break in production. 30% of GenAI projects are abandoned after proof-of-concept — Gartner. The gap between a working demo and a deployed system is where we operate.
Not a roadmap. A deployed product line. Every vertical ships revenue through the same templated infrastructure.
Three revenue engines. Near-zero marginal cost on every deployment. No single point of failure.
Templated deployment pipeline — configure, ship, bill. Near-zero marginal cost per new client because the infrastructure is built. 100+ deployed to date. SaaS-like gross margins without the multi-year build cycle. Retainer and per-deployment pricing.
Swap fees on $20M+ combined volume across both protocols. ML algorithms optimize routing in real-time. Revenue scales directly with volume and market activity. Zero marginal cost — protocols run autonomously.
Per-query pricing 10x cheaper than Google Custom Search API. Developer subscriptions. Enterprise contracts. Fastest path to scalable, predictable SaaS revenue in the portfolio.
~$500K first-year run rate. 15% month-over-month growth. Five live products across search, DeFi, and enterprise AI. $20M+ in protocol volume. 100+ deployments shipped via templated pipeline with near-zero marginal cost. No VC funding. Every metric is real and verifiable.
Every product — search, DeFi, enterprise AI, marketing — runs on the same agent architecture. Templated, deployable, autonomous. Configure the domain, ship the system.
Ingests 100K+ data points daily. Processes news, market feeds, on-chain data. Delivers structured intelligence signals in under 30 seconds. Powers autonomous trading decisions and market intelligence products.
Translates signals into action. Executes trades on Hyperliquid via TIDE. Routes swaps through Hyperswap. Manages risk parameters autonomously.
Client-facing AI that runs autonomously — marketing, lead generation, stakeholder communication. VenymCMO handles multi-platform engagement 24/7. The same agent architecture deployed for client operations.
Orchestrates all agents. Manages 100+ deployed systems simultaneously. Delegates tasks, handles conflicts, runs scheduled operations. The reason a small team operates at this scale.
Each product targets a distinct market. The same agent architecture deploys across all of them. Expansion isn't diversification — it's the deployment pipeline applied to new verticals.
Enterprise AI spending — Gartner 2025. SearchHive targets the search API layer. Productized deployments target the solutions layer.
Commercial drone market by 2030. The physical intelligence layer applies directly — navigation, sensing, decision-making.
Industrial robotics market. The same agent architecture scales from software to hardware — same deployment pipeline, new execution surface.
Search. Finance. Enterprise AI. Marketing. Next: physical systems. The same deployment pipeline applies to every domain. Drone navigation. Robot decision-making. Autonomous operations. Configure the domain, ship the system.
Technical founder plus engineering and marketing teams. Architecture, deployment, revenue — all built in-house. The templated pipeline means the team scales faster than headcount.
~$500K run rate. 15% MoM growth. No outside funding. The pipeline is built and compounding. This round funds the next phase — scaling the engineering team and beginning the physical intelligence build.