“Do I have everything I need?”
Missing amount? Ambiguous vendor? Half-readable scan? The work stops here — a garbled invoice never reaches your accounting system.
If not, it stops and says whyAI that reads your invoices, follows your rules, and asks your team before it acts.
Zyro runs inside your own cloud and does the paperwork your team is buried under — reading, checking, matching, drafting. Anything that matters waits for a person to say yes.
30 minutes, no slides — we walk through one of your real workflows.
Runs in your cloud
Works with any AI model
You own it, forever
Zyro is askingjust now · invoice workflow
Post invoice #8842 for $4,180 to your accounting system? Everything matches the purchase order.
Approved — posted once, and written into the permanent record.
This is the whole idea. The AI does the work; a person makes the call. Try the buttons.
When something goes wrong at 2am, you shouldn't have to guess. With Zyro you can replay exactly what the AI did, why it did it — and prove nothing happened twice.
That is the guarantee Zyro is built around.What it does
We set it up in your cloud.
Then we hand you the keys.
Your data never leaves your company. The whole system runs inside your own cloud. Sensitive work can be kept on AI models running entirely in-house — enforced by the system, not by a promise.
It follows your rulebook — every single time. Before anything touches your systems, four checks run automatically. The AI can't talk its way past them, because they aren't AI — they're fixed rules your team wrote.
People stay in charge of what matters. You choose the line. Above it, work pauses for a person to approve, edit, or decline. Below it, routine work flows through automatically — always on the record.
Everything is written into a permanent record. Every decision and every action is saved as it happens. If a run is interrupted, it picks up where it left off — an invoice is never paid twice.
Use any AI model — and switch anytime. Anthropic, NVIDIA, open-source models running on your own machines, and more. Change providers with a settings change, not a rebuild.
Quality is a number you can see, not a vibe. Every workflow ships with its own test suite. When anything changes, it's re-scored against your acceptance criteria — so you know it still works before it runs.
How it decides
Before Zyro does anything,
it answers four questions.
Not four AI judgments — four fixed checks, written with your team, that run the same way every time. If any answer is no, nothing happens.
“Am I even allowed to do this?”
Every action is checked against who's allowed to do what in your organization — the same way you'd check a new employee's permissions. The AI never gets to decide this for itself.
Your org chart decides, not the AI“Is this important enough to ask?”
Above the line you set, the work pauses and a person approves, edits, or declines — honored exactly once, with a record of who said yes and when.
You draw the line“Is this within what was pre-approved?”
Spending limits per task, per day, per workflow. The AI can never run up a bill — or an exposure — that you didn't sign off on in advance.
Caps you set in advanceUse cases
Start with work you already recognize.
Pick how work arrives in your team — as documents, as tickets, on a schedule — and see what Zyro does with it and where a person stays in the loop.
Live todayrunning in production On the roadmapbuilt on the same foundation — ask us for a date
The pattern: documents land in your business — invoices, claims, contracts, applications — and someone has to read, check, match, and act. Zyro runs the whole pipeline; a person approves anything that matters.
What you need: a place documents arrive, the system they end up in, and the checklist your team uses today — even if it only lives in someone's head. We'll write it down together.
Invoices read, matched, and posted
Every invoice checked against its purchase order and receipt, then posted — with anything unusual held for a person. Never posted twice, even after a crash.
A person approves: any mismatch, or amounts over your cap
Expense reports checked against policy
Each report checked against your actual policy document. Problems are flagged with the exact rule quoted — not a black-box score.
A person approves: exceptions only — clean reports flow through
Claims sorted and drafted for review
Claims classified, checked for completeness, and drafted for a decision — with every extracted fact traceable to the page it came from.
A person approves: every claim decision
Loan files checked for what's missing
Files checked against your checklist, missing items listed, and routine items prepared for underwriter sign-off.
A person approves: each item cleared
Contracts compared to your playbook
Key terms pulled out and compared to your standard positions. Deviations ranked; nothing summarized without a citation.
A person approves: every suggested change
Identity documents verified
Customer documents verified, discrepancies flagged, periodic re-checks drafted — with a paper trail regulators can actually read.
A person approves: every adverse finding
The pattern: work arrives as a queue — support tickets, IT requests, disputes, HR cases. Zyro sorts, researches, and drafts answers inside the tools you already use. Handing off to a person is a feature, not a failure.
What you need: your existing ticketing system (you don't switch tools), the documents your team answers from, and a decision on what can flow through automatically versus what always needs a person.
Tickets sorted and answered in draft
Every inbound ticket classified, matched with account context, and answered in draft. Your team reviews and sends — response times drop, quality doesn't.
A person approves: sends — or auto-send for the routine ones
Billing tickets routed to the right team
The AI works out what the ticket actually is — a refund request, a dispute, a failed payment. Where each of those goes is your routing table, not the AI's opinion. It cannot send a dispute to the payments team.
Your rules decide: the destination is never the AI's call
Refunds queued for one-click approval
Refund requests checked against order history and policy, then queued for a yes/no. Issued exactly once, always on the record.
A person approves: every refund above the floor you set
Routine requests handled from your runbooks
Standard requests carried out step by step, with anything privileged paused for sign-off. A full record per ticket.
A person approves: privileged operations, always
Complaints routed with the clock ticking
Complaints classified and routed with regulatory deadlines tracked — built for teams where a missed deadline is a reportable event.
A person approves: everything sent outside the company
Employee questions answered from policy
Queries answered from your actual policy documents, quoted rule by rule — with sensitive cases routed straight to people.
A person approves: anything about a named individual
Dispute responses assembled on time
Evidence gathered and response packets assembled to the card networks' requirements, submitted on your sign-off — before the deadline, every time.
A person approves: every submission
The pattern: some work should just happen — nightly, weekly, or when a number crosses a line. Zyro runs it unattended, and the judgment calls become a short morning approval list instead of a fire drill.
What you need: read access to the systems being checked, the schedule or thresholds that should trigger a run, and a named owner for the morning list.
Overnight reconciliation, with answers
Payments vs. ledger, inventory vs. warehouse — matched overnight. Discrepancies don't just get listed; they arrive with a proposed fix attached.
A person approves: every proposed correction
Renewals watched, actions drafted
Contracts, licenses, and certificates tracked — with the action item already drafted before the deadline, not a calendar reminder someone has to act on.
A person approves: renewal actions
Deadlines watched around the clock
Regulatory clocks and internal promises monitored continuously. When something's at risk, the escalation arrives with everything needed to act.
Runs unattended — a person acts on escalations
Customer files re-checked on schedule
Files re-verified on your schedule; only the ones needing attention reach a person, each with the findings already assembled.
A person approves: adverse findings
Reorder drafts for the unusual cases
Stock exceptions spotted and purchase-order drafts prepared — the judgment on weird cases stays with your buyer.
A person approves: every purchase order
Weekly reports that write themselves
Ops packs and board reports assembled from your live data on schedule — with every number traceable back to where it came from.
A person approves: before anything is distributed
The pattern: work where “the AI did it” is not an acceptable answer to an auditor. These run with sensitive data kept entirely inside your own systems, full records of every step, and approvals designed to hold up in a compliance review.
What you need: your compliance team in the room when we define the rules (we design with them, not around them), your data-residency requirement stated up front, and a map of who may approve what.
Claims prepared, patient data stays home
Claims checked and prior-authorization packets assembled — with patient data handled only by AI running inside your own systems, never an outside service.
Patient data: never leaves your systems — enforced, not promised
Medical records summarized, with receipts
Discharge summaries and abstracts drafted with every fact linked back to the chart, ready for a clinician to sign.
A person approves: a clinician signs every summary
Alerts investigated, reports drafted
Suspicious-activity alerts investigated and report narratives drafted — every step recorded for the regulator who will ask.
A person approves: every decision and every filing
Document review at scale, defensibly
Large document sets classified for relevance and privilege, with reviewer-ready explanations and a process log that stands up in court.
A person approves: all privilege calls
Résumés screened, fairly and provably
Candidates screened against your stated criteria only — and the record shows exactly which criteria drove each decision.
A person approves: every shortlist
Filings drafted from your books
Returns and filings drafted with every line traceable to its source, ready for your preparer to review and sign.
A person approves: the preparer signs every filing
Our promises
We tell you what's built
and what isn't.
The use cases above are marked Live today or On the roadmap, and we will not blur the two in a sales call. What is live rests on the four guarantees below — and those we will demonstrate on your own workflow before you pay us anything.
Nothing ever happens twice
Every session is recorded as it runs. If a run crashes mid-way, it resumes from the record instead of starting over — the invoice is never paid twice. We demonstrate this live, on your own workflow, before you commit.
Approvals that survive an audit
Approve, edit-and-approve, or decline — each honored exactly once, with a permanent record of who decided and when. Routine work flows through automatically without giving up the trail.
Any AI model, under your rules
Anthropic, NVIDIA, open-source models on your own machines, and more — all behind one set of rules, with automatic fallbacks, spending limits, and a keep-it-in-house option for sensitive data.
Quality you can measure
Every workflow ships with its own test suite and is re-scored on every change against your acceptance criteria. Quality is a number you watch — not a testimonial you're asked to trust.
For the person signing
We don't know your numbers.
So we haven't invented them.
Most AI proposals arrive with someone else's ROI attached. Here is the model with the inputs left open. Put your own figures in — the arithmetic is yours, not a claim of ours.
Annual model · document processing
Edit any figure. Everything recalculates.
Invoices and expense claims combined.
End to end, including the chasing.
Salary plus employer costs.
The number this whole case turns on. See risk 1.
Faster than today — the problem arrives already named.
Put in whatever figure we quote you.
Cost today
—
per year, staff time
Cost after
—
exceptions + the system
Net
—
per year
Hours returned
—
per year, to your team
This excludes money recovered from duplicate payments caught and out-of-policy claims stopped — because we will not estimate a figure we cannot yet measure at your company. Treat the saving above as the floor, not the case. And note it counts hours, not headcount: returned hours only become cash if you choose to redeploy them.
The three ways this disappoints you
You will find them anyway. It is cheaper for both of us if you find them now.
The automation rate comes in low
Your documents may be messier than the model assumes — scanned, handwritten, awkward. This is the input the whole case rests on, and nobody can honestly promise it in advance.
So: the pilot exists to measure this one number on your real documents, and report it, before you commit to a rollout. If it comes in low, you have spent a pilot — not a budget.
A bad approval gets through
The nightmare: it quietly approves something it should have stopped, and an auditor finds it.
So: approval requires passing every check and falling under a limit you set — and the figures approved are verified against the document itself. An AI handed an invoice with faulty arithmetic will otherwise silently "correct" it and hand you a clean-looking record. That exact failure is designed against.
Your team escalates everything
A reviewer who doesn't trust it checks all of it, and you have bought an expensive second pair of eyes.
So: the system is not permitted to send a clean, in-limit document to a human. An automation rate propped up by escalating everything is a fiction — we would rather it not be available to us either. Beyond that, this is a change-management problem and we will say so rather than pretend it is a software one.
What is not at risk
Your data: it runs in your cloud and does not reach us — enforced by where the software runs, not by a clause.
Your audit trail: it is not a report we generate for you, it is how the system works. It cannot process a document without producing one.
Your control: the limits, the rules, what needs a signature — yours to change, without us.
Engagement & pricing
One written quote.
No costs hiding in a retainer.
Most of the real cost in any AI project is connecting it to where your work actually lives. We quote that up front, in writing, once. When the engagement ends, the system stays — and keeps running.
Working session
We map one workflow end-to-end, design the approval rules with your team, and hand you a fixed estimate in writing. No retainer, no commitment to proceed.
Free · 90 minutes · estimate in writingSet up in your cloud
The platform lands in your own cloud in days; the quoted work connects your systems. You watch every session live from day one.
Fixed price · quoted before we startProve it & hand over
The workflow passes its test suite against your acceptance criteria. Then it's yours — the system, the tests, the records. Support is optional, never required.
You own it · support optionalCommon questions
Straight answers.
How fast can we be up and running?
The core platform lands in your cloud in days. The honest variable is connecting your existing systems — and we quote that explicitly, in writing, before you commit. You'll know the full timeline up front, not discover it later.
What does it cost?
A fixed-price setup, quoted after the free working session — not an open-ended monthly retainer. Once it's deployed, the system is yours; a second workflow on the same platform costs a fraction of the first, because the foundation is already in place.
Do we need to replace our existing tools?
No. Zyro connects to the systems where your work already lives — your ticketing system, your accounting software, your document storage. Your team keeps working where they work today; the AI works there too.
What do we need to have ready before starting?
Three things: the workflow you want automated (even roughly described), access to the systems it touches, and the person who knows how the work is judged today — the policy, the checklist, or the rules of thumb. We turn that into explicit rules together, in the working session.
Does our data ever leave our environment?
No. The platform runs in your cloud, and residency is enforced by the system, not by a promise: work you mark as sensitive is handled only by AI models running inside your own environment. That rule is one the AI cannot override.
What happens when the AI gets something wrong?
Anything above the line you set waits for a person before it happens — approve, edit, or decline. And everything, right or wrong, is on the record: you can replay the exact session, see every decision, and prove what did and didn't happen.
Which AI models do you use? Are we locked in?
No lock-in. Anthropic, NVIDIA, open-source models on your own machines, and other providers all run behind one set of rules, with automatic fallbacks and spending limits. Switching providers is a settings change, not a rebuild.
Get started
Bring us your messiest back-office workflow.
In one 90-minute working session we'll map it end-to-end, design the approval rules with your team, and hand you a fixed estimate in writing. If we're not the right fit, you leave with the map anyway.
We reply within one business day. Usually much faster.
Day 0
You send one paragraphabout the workflow, in your own words
Day 1
Session scheduledwe come to you — 90 minutes, no slides
Day 3
Workflow mappedapproval rules drafted with your team
Day 5
Fixed quote, in writingthe full cost, before you commit to anything
Then
You decideno retainer required to get this far