How FLYBICS Runs on AI Agents

The Challenge

FLYBICS is a solo-founder AI consulting and technology company. The challenge: operate like a full-service firm (sales, marketing, delivery, ops) with one person and a sub-$100/month budget.

Traditional approach: hire freelancers or agencies for each function ($5,000–15,000+/month). AI-native approach: deploy AI agents for every function, with the founder as the director and approval gate.

The Solution: A 6-Agent Ecosystem

Agent 1: Lead Research & Scoring

Trigger: Weekly batch from prospect lists / Apollo.io exports

Workflow: Scrapes company website → Gemini Flash summarizes company profile → AI agent scores leads (1–10) on automation pain signals, tech stack, size, decision-maker availability → Score ≥7 → enriched contact created in HubSpot with research notes

Result: 30–50 researched, scored leads/week with zero manual research time.

Agent 2: Personalized Outreach

Trigger: HubSpot contact enters "Enriched" stage with score ≥7

Workflow: Gemini drafts 3-touch sequence grounded in research notes (one specific observed fact, ≤120 words, soft CTA) → Founder approves/rejects in one click (n8n approval node) → Approved → sent via Instantly/Apollo sequences → Replies webhook back → Groq classifies (interested/question/not now/unsubscribe) → Drafts suggested response for founder

Result: Personalized sequences at scale; reply rates 3–5%; founder time per outreach: <5 min/day.

Agent 3: Content Engine

Trigger: Weekly voice note or bullet list from founder

Workflow: Whisper → Gemini transcribes → Gemini Pro drafts SEO blog post in FLYBICS voice → Founder edits in Google Doc → Agent generates: 3 LinkedIn posts, 3 X posts, 1 carousel → scheduled via Buffer → Monthly: pulls Search Console data, proposes next month's topics

Result: 4 pillar posts/month, 24 social posts, without a content team.

Agent 4: Website Support & Intake

Trigger: Chatwoot widget conversation on flybics.com

Workflow: RAG query on Supabase vector store (service docs, FAQ, case studies) → Gemini answers with citations; offers Calendly link on buying intent → Low confidence → assigns to founder with full conversation summary → All conversations logged to HubSpot; qualified → auto-deal creation

Result: 60%+ of conversations auto-resolved; 24/7 coverage without a support person.

Agent 5: Dev & QA Pipeline

Trigger: GitHub PR

Workflow: Playwright suite runs; failures sent to n8n → LLM classifies each failure (bug vs flaky locator) → Locator fixes → auto-PR; real bugs → GitHub issue with label → Second LLM reviews PR diff for security/quality

Result: Deploy-to-client lead time <2 weeks per agent build.

Agent 6: Ops Reporting

Trigger: Monday 07:00

Workflow: Pulls HubSpot pipeline, outreach stats, Buffer performance, Search Console, GitHub activity → Gemini writes one-page brief: wins, risks, three priorities → Delivered by email + Notion log

Result: Full business visibility in <5 minutes/week.

The Architecture

Layer 4: Delivery        CrewAI · LangGraph (client agent builds)
Layer 3: Functions       Apollo · HubSpot · Buffer · Chatwoot · GitHub · Calendly
Layer 2: Orchestration   n8n (self-hosted) — all agents live here
Layer 1: Intelligence    Gemini · Groq · Cerebras · OpenRouter (all free tiers)

The Stack

Every tool in the stack is either free tier or open-source. Total monthly cost: ~$30.

• n8n (self-hosted)$0
• Gemini AI Studio$0
• Groq$0
• Cerebras$0
• OpenRouter$0
• Claude Code Pro$20
• HubSpot Free$0
• Apollo.io Free$0
• Buffer Free$0
• Chatwoot (self-hosted)$0
• Supabase Free$0
• Total$30/month

Results (First 90 Days)

Key Lessons Learned

1. Human-in-the-loop is non-negotiable. The founder approval gate on outreach and content ensures quality. Agents draft; humans decide.
2. Stack free tiers deliberately. Each provider has independent rate limits. Stacking 4 LLM providers multiplies free capacity without multiplying cost.
3. Secondary domain for cold email. Never cold-email from your primary domain. Warm up 2–3 weeks before sending. Stay under 50/day/inbox.
4. Context is everything. The outreach agent's quality depends entirely on the research notes it receives. Invest in the Lead Research Agent first.
5. Measure everything. The Ops Reporting Agent makes invisible bottlenecks visible. If you can't measure it, you can't agent it.

Applicability to Your Business

This model works for any business where:

• Knowledge work is a significant cost centre
• Customer interactions are repetitive but require personalisation
• You have data (or can gather it) to feed AI workflows
• A human-in-the-loop approval model is acceptable

The same architecture that runs FLYBICS can be deployed for your organization — customized to your workflows, data sources, and compliance requirements. Typical build timeline: 1–2 weeks per agent.

Frequently Asked Questions

How much does it cost to run a business on AI agents?

FLYBICS operates its entire six-agent stack for about $30/month by combining free-tier LLM providers (Gemini, Groq, Cerebras, OpenRouter), self-hosted open-source tools (n8n, Chatwoot, Supabase), and free CRM/outreach tiers. The only paid line item is Claude Code Pro at $20/month.

What are the six AI agents FLYBICS uses?

1) Lead Research & Scoring, 2) Personalized Outreach, 3) Content Engine, 4) Website Support & Intake, 5) Dev & QA Pipeline, and 6) Ops Reporting. Each is triggered by a specific event and hands off to the founder for approval on high-stakes actions.

Can this AI agent model work for other businesses?

Yes. The model fits any business where knowledge work is a significant cost centre, customer interactions are repetitive but need personalisation, data is available to feed workflows, and a human-in-the-loop approval model is acceptable. Typical build timeline is 1–2 weeks per agent.

Is a human still involved when a business runs on AI agents?

Yes. Human-in-the-loop is non-negotiable at FLYBICS. Agents draft outreach, content, and code, but the founder approves or rejects high-stakes actions in one click. Agents propose; humans decide.