Not another Zapier flow. Systems that read, decide, write back, and talk — across nine different kinds of business.
Every example below explains itself — no industry background required. Each one is built to handle the messy exception, not just the happy path, which is exactly the part a template tool can't do.
Nine flows, four categories, nine different industries
Each card starts by explaining the actual business problem in plain language, then shows exactly what makes the automation hard enough to need an engineer.
Document generationLegal / any contract-heavy business
Contract redline comparator
The problem: when a business signs a vendor or client contract, someone has to compare it line by line against the company's own standard version to catch changes the other side snuck in — like who's liable if something goes wrong. Lawyers call this "redlining." Doing it by eye on a 40-page contract is slow and easy to get wrong.
This compares an incoming contract against the approved playbook, catches buried changes — a shortened liability cap, a sneaky auto-renewal clause — and produces a marked-up PDF a lawyer can review in minutes instead of an hour.
01
Contract read in
Arrives as a scanned fax, a Word doc, or a clean PDF — the system converts all three into usable text (a technology called OCR, which reads text out of an image).
02
Clauses matched by meaning
Doesn't just search for exact phrases — recognizes that two differently-worded clauses are addressing the same thing.
03
Deviations flagged
Anything that differs from the approved version is marked and ranked by how much risk it adds.
04
Redline PDF generated
Side-by-side markup ready for a lawyer to review, not a document to reread from scratch.
Worked exampleA vendor's contract quietly changes the liability cap from "fees paid in the last 12 months" to "total fees ever paid" — a huge difference years into the relationship. A plain keyword search wouldn't catch it, because neither version uses the word "cap." This catches it because it understands what the clause does, not just the words it uses.
Document generationConstruction / general contracting
Subcontractor compliance packet builder
The problem: before letting a subcontractor (electrician, plumber, etc.) work on a job site, a general contractor has to prove every one of them is insured and licensed, using a document called a Certificate of Insurance (COI). If one sub's COI has quietly expired, an inspector can shut the whole job down.
This tracks insurance and license expiration dates for every subcontractor on every job, and builds the compliance packet automatically instead of someone chasing down PDFs the week of inspection.
01
Certificate received
Extracts coverage amount and expiration date regardless of which insurance broker's template it's on — there's no standard format.
02
Matched to this job's rules
Checks against the specific coverage minimum for that job — bigger projects often require higher limits.
03
Gaps flagged early
Coverage expiring before a scheduled inspection is caught weeks out, not discovered on-site.
04
Packet assembled
One PDF with every sub's current documentation, ready to hand to the inspector.
Worked exampleA job has 14 subcontractors. One electrician's certificate is on file but expires 4 days before the final inspection currently scheduled. The system catches that gap three weeks out — giving the contractor time to get a renewal, instead of finding out the morning of and delaying the whole closeout.
Document generationManufacturing / food & chemical production
Certificate of Analysis auto-builder
The problem: when a manufacturer ships a batch of product (food ingredients, chemicals, supplements), many customers require a Certificate of Analysis (CoA) — a document proving that specific batch passed lab testing. It has to match the real results for that exact batch, not a generic form.
This pulls raw lab results straight from the testing equipment, checks them against each customer's required limits, and generates the certificate the moment a batch clears quality control — and refuses to generate one for a batch that doesn't.
01
Lab results imported
Pulls directly from the testing equipment's own export — no one retyping numbers off a printout.
02
Checked against limits
Every result compared against the exact limits that specific customer's contract requires.
03
Pass/fail decided per batch
Anything out of spec halts the certificate and routes to a human — it never auto-generates paperwork for a bad batch.
04
Certificate formatted per customer
Different customers require different layouts and units — it builds the version each one actually needs.
Worked exampleA batch passes 7 of 8 required tests, but one contaminant reading is borderline over a customer's limit. Instead of generating the certificate anyway, the system holds the batch and routes it to the quality manager — because the one thing worse than a slow paperwork process is a fast one that ships a bad batch with a clean-looking certificate.
VoiceNeuro-ophthalmology & ophthalmology surgery clinic
Live AI intake & doctor-matching line
The problem: a clinic with several specialists gets calls from patients who have no idea which doctor they actually need. Front-desk staff have to ask the right follow-up questions to route correctly — get it wrong, and the patient books the wrong specialist and has to start over, wasting a slot both of them needed.
This is live and playable — speak or type as if you're calling. It listens for symptoms described in plain, non-clinical language, asks one clarifying question when it's genuinely unsure, and matches the caller to the correct doctor on staff — not just the next open slot.
Playable now
Dr. Marshneuro-ophthalmology — double vision, unexplained or fluctuating vision loss, optic nerve concerns, vision changes tied to headaches or a neurological history.
Dr. Colecataract & refractive surgery — cloudy or blurry vision that's developed gradually, interest in LASIK or vision-correction surgery.
Dr. Nathanoculoplastic & orbital surgery — eyelid drooping, swelling or bulging around the eye, orbital concerns.
IF unclearthe agent asks one more specific question instead of guessing — it will never book the wrong specialist just to avoid a pause in the conversation.
Idle — press "Start call" below
Start call
Press "Start call" and just talk — try "My vision doubles when I read at night." Speaks English or Russian (just start talking in either). No need to press anything between turns, and you can interrupt the agent mid-sentence like a real call. Requires mic access — works best in Chrome or Edge.
Worked exampleA caller says "my vision kind of splits into two when I'm reading in the evening" — never using the phrase "double vision." A keyword-matching system would miss that entirely. This one recognizes the symptom pattern anyway and books with Dr. Marsh, the neuro-ophthalmologist — matching on what the caller means, not the exact words they used.
VoiceLogistics / last-mile delivery
Delivery exception call agent
The problem: when a delivery is going to be late, damaged, or can't be completed as planned, the standard response is a one-way "your delivery is delayed" text. The customer can't respond to it, and the same failed delivery often just repeats the next day.
When a delivery hits a snag, this calls the customer immediately, explains what happened in plain terms, and works out what they want next — instead of a text they can't talk back to.
Triggered by delivery exceptions, not on a schedule
IFcustomer wants to reschedule → a new delivery window is booked on the call and confirmed before it ends.
IFthe issue is damage → photos are requested by text follow-up and a claim opens automatically — no separate phone call needed.
IFthe customer doesn't answer → a text goes out with the same information and a self-service reschedule link.
IFthe driver flags the address as inaccessible → the agent calls ahead of the next attempt to confirm gate codes, instead of the same failed attempt repeating tomorrow.
Worked exampleA delivery truck can't get through a gate code that changed recently. Instead of marking it "failed — will retry" and hitting the same wall tomorrow, the agent calls the customer within minutes, gets the new code, and updates tomorrow's route before the truck even leaves the depot.
System connectionsReal estate brokerage
Transaction coordination sync
The problem: a single home sale touches three systems that don't naturally talk to each other — the public listing (MLS), the software agents use to track paperwork and deadlines, and the brokerage's system for calculating commissions. If a closing date changes in one and not the others, someone misses a deadline or gets paid wrong.
This keeps all three systems in sync — and has real rules for which one "wins" when two of them disagree, instead of one silently overwriting the other.
Runs continuously in the background
IFclosing date changes in the transaction software → the public listing status and commission projections update automatically within minutes.
IFa listing goes "under contract" before a transaction file exists yet → one is auto-created and flagged for the agent to fill in, instead of quietly falling through the cracks.
IFa financing deadline is 48 hours out with no update logged → the agent gets a direct alert. A missed financing deadline is one of the most common reasons deals fall apart, and it's usually just nobody watching the calendar.
IFtwo systems show conflicting closing dates → the most recently and directly edited one wins, and the mismatch is logged for a human to confirm — never silently overwritten.
Worked exampleA buyer's loan approval slips a week, pushing the closing date. The agent updates it in the transaction software. Without this system, the public listing would still show the old date and the commission report would calculate off the wrong month. Here, both update within minutes — and the financing deadline tracker re-flags itself too, instead of losing track of a date that just moved.
System connectionsE-commerce / retail
Inventory & order reconciliation
The problem: an online store tracks the same products in three separate places — the storefront (like Shopify), the warehouse or shipping company's system, and the accounting software. Each one often uses a slightly different code or name for the exact same item, so matching them up can silently fail.
This matches products across systems using approximate ("fuzzy") logic instead of requiring an exact code match, so inventory counts and financials actually agree instead of quietly drifting apart over time.
Runs continuously as orders and stock change
IFproduct codes match exactly → linked instantly, no review needed.
IFcodes don't match but name, price, and supplier all line up closely → linked automatically, with a note showing why it was considered a match.
IFonly a partial match exists (similar name, different price) → held for a quick human confirmation instead of guessing.
IFa product exists on the storefront but never appears in the warehouse system → flagged as a likely "ghost" listing — something that can sell online with nothing available to actually ship.
Worked exampleThe storefront lists a product as "Ceramic Mug – Blue"; the warehouse system has it filed as "MUG-CER-BL-08OZ." No shared code, but the name, price, and supplier match closely enough to link with high confidence. Meanwhile, a discontinued item still live on the storefront but missing from the warehouse gets caught before it sells out from under a customer with nothing to ship.
Decision logicFinancial services / lending
Loan pipeline stall predictor
The problem: a loan has to move through several steps before it can close — gathering documents, underwriting review, appraisal, final sign-off — and each step depends on different people. A loan can quietly stall for days before anyone notices it's behind pace to close on time.
This compares how fast each loan file is actually moving against how fast it needs to move to close on schedule, and flags the ones falling behind while there's still time to fix it — not the day before closing.
Runs daily against the active pipeline
Modelcompares how many days a file has sat in its current step against the historical average for files that closed on time.
Flag ruleif a file is 40% past that average and the target closing date is under 10 days out, it's flagged as at-risk.
On flagthe loan officer gets a specific reason — "waiting on appraisal, 6 days overdue" — not a generic "this loan is late."
Worked exampleA loan is scheduled to close in 8 days but has sat in "awaiting appraisal" for 9 days — well past the 5-day average for files that closed on time. The system flags it today and names the exact bottleneck, instead of the loan officer finding out when the buyer calls asking why closing isn't happening.
Decision logicFleet / field service & equipment
Equipment failure risk router
The problem: a company running a fleet of trucks or heavy equipment has to decide when to send something in for maintenance. Fixing everything on a fixed schedule wastes money on equipment that's fine; waiting for something to actually break is far more expensive in emergency repairs and downtime.
This scores which vehicles or equipment need maintenance now versus what can safely wait, using real usage and repair history — not a flat "every 90 days" rule that either wastes money or misses real problems.
Runs weekly against the fleet
Weightshours or miles since last service 35% · number of prior repairs on this exact unit 30% · how hard it's been used recently 20% · known manufacturer issue for this model 15%.
Low scorestays on the normal maintenance schedule, no action needed.
High scorerouted to proactive maintenance — scheduled before a breakdown, not after.
Worked exampleA delivery van has 40% more mileage than average for its service interval, plus two prior repairs on the same part in the last year. It scores high risk and gets pulled in for inspection proactively — instead of waiting for the fixed 90-day mark, by which point it may have already broken down mid-route.
These are working prototypes built to demonstrate real automation and voice-agent capability. None represent an actual client or business currently in production — no real medical, legal, or financial advice is given, and any resemblance to a real business, clinic, or doctor is coincidental.