What can actually be automated in a business?

Find, qualify, and reach out to high-fit leads automatically — every week.

Define your ideal customer profile (industry, size, role, geography). An automation searches LinkedIn daily, pulls matching profiles, enriches them with data from Apollo or Clearbit (email, company size, funding status), and lets an LLM rank each one against your ICP. The top-ranked get a personally-written first contact based on their recent LinkedIn activity. Everything lands in your CRM with a score and a recommended next action.

Form submission → enrichment → scoring → the right rep gets a Slack ping with full context.

Someone fills in your contact form or emails you. Within 30 seconds, the automation enriches the lead with company data, classifies their need with an LLM, scores them against your ICP, creates a CRM record, and pings the right rep in Slack or Teams with a summary. Hot leads get answered in minutes, not days — and no leads fall between the cracks.

Give me a list of 200 companies — I'll email each with a personalized opener based on their site and recent news.

For each company in your list, the automation scrapes their website, latest press releases, LinkedIn company updates, and any open data (open job listings, funding rounds). An LLM trained on your tone and positioning writes a personal first line for each recipient that shows we've done the homework — followed by your standard pitch. Result: double the open rates of generic emails, without writing each one by hand.

An AI voice takes orders by phone, integrates with your menu or POS, and confirms by SMS.

Restaurants, salons, auto shops — they all lose money on missed calls outside business hours. A voice agent answers 24/7, listens to the order or booking, validates it against your menu or calendar, drops it into your POS or calendar system, and confirms with the customer by SMS. It knows when to escalate to a human (special requests, complaints) and transfers the call when needed.

Incoming tickets classified, FAQ answers sent directly, complex cases flagged with a summary for a human.

Incoming emails or chat messages go through an LLM that classifies the ticket type. Standard questions (hours, delivery status, return policy) get answered directly with responses grounded in your FAQ and knowledge base — no hallucinations, every answer has source citations. Complex or emotionally charged tickets are escalated straight to a human, with a short summary of what the customer wants and a suggested response. Escalation rules are explicit and transparent.

Inbound invoices parsed automatically, matched against POs, posted — exceptions flagged.

Supplier invoices arrive by email. The automation extracts data with OCR and an LLM (supplier, amount, OCR/reference number, due date, VAT), matches against existing purchase orders or projects, and submits to your accounting system (Fortnox, Visma, Bokio, Xero). If something doesn't match — wrong amount, no PO, duplicate invoice — it's flagged for human review with a clear reason. Typically frees up 5–15 hours/week on the finance team.

When product X drops below threshold → PO to supplier automatically, with the right quantity based on sales velocity.

The automation reads daily from your inventory system (Shopify, WooCommerce, Visma) and sales history. When a product drops below a dynamic threshold (calculated from sales velocity + lead time + safety stock), it creates a PO with the right quantity and emails it to the supplier. Every week you get a summary of what was ordered, what's in transit, and what's at risk of stocking out. No manual stock checks, no "oh we're out".

Every Monday morning, the system emails you a summary of key metrics, comparisons to prior periods, and what to focus on this week.

The automation pulls data from CRM, finance, ad platforms (Google/Meta Ads), web analytics, and optionally a product database. An LLM compares the week to last week and the same week last year, identifies anomalies ("sales up 12%, but your biggest customer didn't engage — check"), and formats a tight report. Sent Monday 07:00 to email or Slack. Five minutes and you have the whole week in your head.

Write once; automation publishes to LinkedIn, X, newsletter, YouTube Short script, and TikTok draft.

You publish an article or record a short video. The automation reads the content, extracts 3–5 key points, and produces channel-specific material: LinkedIn carousel (with image templates), Twitter/X thread, newsletter draft, YouTube Short script, and Instagram caption suggestions. You approve and post — or let the automation schedule directly if you trust the process. Multiplier effect with no extra effort.

Daily automated scraping of competitor pricing, products, job postings, and social media — summarized every Friday.

The automation scrapes selected competitors daily: price changes, new products, job listings (a signal of where they're growing), press releases, and their social media pace. An LLM identifies changes vs. baseline and writes a tight Friday report: "Competitor A cut prices on X by 15%, hired two reps in Stockholm, launched a B2B feature last week." You get strategic awareness without anyone on your side doing manual research.

Inbound resumes scored against the role profile, top 15% booked for interviews automatically, the rest get personal rejections.

When a resume comes in (via email, ATS, LinkedIn), an LLM reads it and scores against your role profile (skills, years of experience, industries, location, salary range). Top candidates get an automatic email with a calendar booking link for an interview. The others get a personally-written, respectful rejection — not a generic form letter. Saves 20+ hours per hire on the HR side, and candidate experience gets better, not worse.

After each purchase or delivery → smart-timed request → happy customers go to Google / Trustpilot, unhappy ones become support tickets.

The automation triggers from delivery or order data. 3–7 days later (timing based on product type) the customer gets a short question: "How was it?". If they respond positively, they're guided to leave a public review on Google, Trustpilot, or Reco.se. If negatively, a support ticket is automatically created with their feedback — and you can reach out before they post anything publicly. Result: more 5-star reviews, fewer angry 1-stars.