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Built by TerAgentic

We build what we advise.

TerAgentic develops its own ventures to explore and validate modern AI, agentic, cloud, inference, automation, and product-development approaches in real environments. These are internal, founder-led projects — not client engagements.

Applied innovation

Each venture is documented the same way a client engagement would be: the problem, the opportunity, the technical implementation, the outcome so far, and what comes next. No customer counts, revenue, or adoption metrics are claimed.

Applied technology venture

AnglerDog AI Fishing Finder

AnglerDog is an internally built venture exploring intelligent outdoor decision support — combining seasonal fishing knowledge, location-specific guidance, weather and moon phase context, inference, recommendation design, membership, and modern web and cloud architecture.

Why this is here

AnglerDog is useful here because it is a real system, not a slide. The patterns it exercises — contextual recommendations, inference with confidence, integration with external data, and a workable monetization path — are the same patterns businesses ask about.

Business problem

Fishing audiences need local, timely guidance, but most niche content sites are static, hard to personalize, and difficult to monetize beyond ads.

AI opportunity

AI can convert seasonal knowledge, lake patterns, weather context, and member preferences into practical fishing recommendations and content workflows.

Technical implementation

A Vercel-hosted recommendation app connects to Squarespace for public pages and membership flows, with structured fishing knowledge, weather and moon phase inputs, affiliate product links, and AI-assisted content generation.

Outcome

A working example of a content brand becoming a recommendation platform, running in production rather than in a prototype.

Future roadmap

Expand water-body coverage, add member profiles, improve recommendation confidence, connect product feeds, and test premium trip-planning tools.

What we’d measure

  • Recommendation acceptance rate
  • Member retention and repeat visits
  • Affiliate click-through and conversion
  • Time to produce seasonal content

Try a sample recommendation

Illustrative only — it shows the recommendation pattern, not live conditions.

Applied community initiative

Communion Neighbors Digital Ministry Pilot

Communion Neighbors is an internally built initiative exploring how AI and modern digital systems can support community formation, connection, communication, and human-centered workflows — without putting software between people.

Why this is here

Communion Neighbors runs like most small teams: a real mission, few hands, and no time for busywork. It is where we test assistive automation that reduces administrative load while leaving judgment and relationships with people.

Business problem

Community and ministry projects often have strong stories and clear missions, but limited systems for onboarding, follow-up, events, and group-starting momentum.

AI opportunity

AI-assisted messaging, content repurposing, and workflow routing can help leaders turn interest into local action without adding heavy operational overhead.

Technical implementation

Squarespace hosts the public story and landing pages, while Vercel can support member resources, intake flows, content repurposing tools, and lightweight automation for events and outreach.

Outcome

A practical pattern for assistive community technology that connects storytelling, onboarding, and local action.

Future roadmap

Add group starter workflows, event follow-up, AI-assisted message drafting, member resources, and a simple portal for local leaders.

What we’d measure

  • Onboarding completion rate
  • Event follow-up response rate
  • Host and organizer time saved
  • Opt-in and consent rates

Start with a conversation

Not sure what AI should mean for your business?

You don’t need a finished specification. That is often where the conversation should begin. Bring the problem, bottleneck, idea, or opportunity, and we’ll work out where the return is — and whether TerAgentic is the right fit.