Airia — Free Mini Course

Your Team Is Using 6 Different AI Tools. Nobody Knows What Anyone Is Doing.

Marketing uses ChatGPT. Engineering uses a different model. Sales has its own thing. And somehow, every team is writing the same prompts from scratch, getting inconsistent results, and storing nothing.

• ▸Here's what AI adoption actually looks like at most companies right now:
• ▸🗂️ Prompts live in random Notion docs, Google Sheets, and people's heads
• ▸🔀 Different teams use different AI models — and get wildly different results
• ▸🚫 No audit trail — if something goes wrong, you have no idea what prompt caused it
• ▸🔒 Security teams are terrified — sensitive data going into tools no one approved
• ▸💸 You're paying for 4 AI subscriptions that 40% of your team has never opened
• ▸This isn't a small business problem. It's the default state for every team scaling AI without infrastructure. The fix isn't more tools — it's orchestration.
• ▸Orchestration just means: one place to coordinate everything. Like a control tower for your AI.

• ▸Airia is an AI orchestration platform — a single place to build, deploy, and manage AI workflows across your entire team.
• ▸Think of it like this: instead of everyone using their own AI tools in their own way, Airia acts as the conductor. It coordinates which AI model does what, when, and how — with consistent rules across the board.
• ▸🧩 Connect multiple AI models (GPT-4, Claude, Gemini, and more) in one place
• ▸⚙️ Build workflows — sequences of automated steps — without writing code
• ▸🔐 Keep everything secure and auditable — know exactly what's happening and why
• ▸👥 Manage what each team member can access and do
• ▸14-day free trial. No IT department required.

• ▸Airia is built around a simple 4-step loop. Here's how teams actually use it:
• ▸1️⃣ Connect — Link your AI models (GPT-4, Claude, whatever you use) and your existing tools (Slack, Salesforce, your database) to Airia via APIs. An API is just a digital handshake that lets two tools talk to each other.
• ▸2️⃣ Build — Create workflows: sequences of steps that tell AI what to do, in what order, with what data. No code required.
• ▸3️⃣ Deploy — Publish your workflow so your team can use it — consistently, every time, with the same guardrails.
• ▸4️⃣ Monitor — Watch what's happening in real time. See usage, catch errors, improve over time.
• ▸Each step removes a bottleneck. Together, they turn AI from a toy into infrastructure.

• ▸A workflow is just a recipe: if this happens, do that. Airia lets you build them visually.
• ▸Example: a content summarization workflow.
• ▸📥 Input: someone pastes a long article
• ▸🤖 Step 1: Airia sends it to your chosen AI model with your pre-written summarization prompt
• ▸✏️ Step 2: The AI returns a summary
• ▸📤 Output: the summary gets posted to Slack or saved to your CMS (content management system — where you publish content)
• ▸That entire chain runs automatically, every time, with zero manual steps.
• ▸You can build this in Airia's drag-and-drop editor. No engineering degree needed. The key is: you define the logic once, and the machine runs it forever.

• ▸Not all AI models are equal. GPT-4 is great at writing. Claude is exceptional at long documents and reasoning. Gemini has strengths with data. Airia lets you use all of them — and route tasks to the right model automatically.
• ▸🧠 Multi-model routing: send different task types to different AI models based on rules you set
• ▸🔄 Fallback logic: if one model is slow or unavailable, automatically switch to a backup
• ▸📊 Cost controls: set limits so you're not accidentally burning through your AI budget
• ▸💡 Model-agnostic: switch models anytime without rebuilding your workflows
• ▸This is the biggest advantage over single-model setups. You're not locked in. As AI improves, you just swap the engine — the workflow stays the same.

• ▸Compliance means following the rules — legal, regulatory, and internal — around how data is handled. For AI, this is where most teams are dangerously exposed.
• ▸Airia builds the guardrails in so you don't have to:
• ▸🔒 Data doesn't leave your approved environment — sensitive info stays where it's supposed to
• ▸📋 Full audit log: every AI call is logged — who ran it, what prompt was used, what came back
• ▸🛡️ Role-based access controls: different people get different permissions (more on this next slide)
• ▸✅ SOC 2 and enterprise-grade security standards built in
• ▸For regulated industries (finance, healthcare, legal) this isn't optional — it's table stakes. But honestly, any team handling customer data should care about this.
• ▸You now know more about AI security than 90% of teams currently using AI. That's worth something.

• ▸As your AI stack grows, not everyone should have access to everything. Airia gives you role-based permissions — a fancy phrase for 'different people get different keys.'
• ▸👑 Admin: can build and publish workflows, manage integrations, view all logs
• ▸🛠️ Builder: can create and edit workflows, but can't change security settings
• ▸👤 User: can run approved workflows but can't modify them
• ▸👀 Viewer: read-only access — great for stakeholders who need visibility
• ▸Why does this matter? Two reasons:
• ▸1. You prevent well-meaning mistakes — a new hire can't accidentally break your production workflow
• ▸2. You meet compliance requirements — many regulations require you to prove who had access to what
• ▸Setting this up takes 10 minutes. Skipping it takes 10 hours to fix when something goes wrong.

• ▸Most teams run AI workflows and have no idea if they're performing. Airia fixes that with a built-in analytics layer.
• ▸📈 Usage metrics: which workflows are being used, by who, how often
• ▸⚡ Latency tracking: how fast each AI step is running (latency = time it takes to get a response)
• ▸💰 Cost per run: how much each workflow costs in API calls — so you can optimize spend
• ▸❌ Error rates: where workflows are failing and why
• ▸🔍 Output quality signals: track whether the AI outputs match your expected format or pass your quality rules
• ▸This is where AI goes from experiment to asset. When you can measure it, you can improve it. When you can improve it, you can scale it.
• ▸Pro tip: set up a weekly review of your top 3 workflows. 30 minutes of monitoring = hours saved.

• ▸The #1 mistake teams make when deploying AI: building everything from scratch without a template.
• ▸Here's what that looks like in practice:
• ▸❌ Writing new prompts for every use case instead of reusing proven ones
• ▸❌ Building workflows in isolation — one team doesn't know what another built
• ▸❌ Not versioning your workflows — so when something breaks, you can't roll back
• ▸Versioning means saving a snapshot of your workflow so you can go back to an older version if needed. Like 'undo' but for your entire AI system.
• ▸✅ The fix: use Airia's prompt library to share and reuse prompts across your team
• ▸✅ Keep a changelog — a simple log of what changed and when
• ▸✅ Use Airia's version history to roll back instantly if a workflow breaks
• ▸Treat your AI workflows like software. Document them. Version them. Test before you deploy.

• ▸Here's what a mid-size SaaS company looks like after 30 days on Airia:
• ▸🌅 Morning: Support team runs an Airia workflow that summarizes all overnight tickets and flags urgent ones — takes 2 minutes instead of 45
• ▸📝 Midday: Marketing uses a shared content workflow to generate 10 product description variations using Claude, then routes top picks to Slack for approval
• ▸📊 Afternoon: Sales runs competitor analysis through a multi-step workflow: scrape inputs → summarize with GPT-4 → format into a battlecard (a cheat sheet for sales calls)
• ▸🔒 End of day: The admin reviews the audit log — 47 AI runs, all within policy, zero security flags
• ▸Total time saved: ~4 hours. Total AI cost: tracked, controlled, under budget.
• ▸This isn't enterprise fantasy. This is what any serious team of 5+ can set up in a week.

• ▸✅ 1. Connect your first AI model — pick one (GPT-4 or Claude) and link it to Airia. This takes about 10 minutes and unlocks everything else.
• ▸✅ 2. Build your highest-ROI workflow first — pick the one task your team does manually with AI most often and automate it. Start simple.
• ▸✅ 3. Set up team roles — invite your team and assign permissions before anyone starts building. Trust me on this one.
• ▸✅ 4. Turn on the audit log — don't wait until something goes wrong. Enable logging from day one so you have a paper trail.
• ▸✅ 5. Run your first workflow and review the analytics — see what it cost, how fast it ran, and where you'd improve it. This single review session will change how you think about AI.