SummAIze provides AI automation services for businesses that want their workflows and back-office work to run on their own: the tools you already use connected, the rules enforced, the repetitive steps handed to an AI agent, and a person approving what matters.
Automation is the main thing we do. When a workflow needs a screen we build the app or platform around it, but the app is one output of the system, not the starting point.
Business process automation with AI is not one tool. It is a trigger, a set of rules, an AI step where reading or judgment is needed, an approval where money or private data moves, and a record of what happened.
Teams that run on email, chat and spreadsheets and have outgrown them. Companies with a back office that re-types the same data between systems. Founders who want the operations of a new product to run without hiring for them first. AI automation for small business and for a larger back office follows the same shape; only the systems differ.
Fewer hand-offs by chat and spreadsheet. The same question answered once, by the system. Money and private data that move only after a check. The weekly report already there on Monday. And you can see what ran, when, and what is still waiting.
An order arrives, a message lands, a payment clears, a date passes.
Capacity, policy, who may do what, and what counts as done.
Reads the records, drafts the reply or the document, fills the fields, flags what it is unsure about.
A person confirms before money moves or private data leaves.
Push, email or WhatsApp to the right people, the record updated, the report ready.
The jobs that repeat every week, described as they happen. Each one is a flow we have built in our own products or scoped for a client's back office.
Orders, bookings and requests that arrive by email, form or WhatsApp get read, checked against stock, capacity or policy, recorded in the system you already use, and confirmed back to the customer. Missing details are asked for, not guessed.
Seats, slots and stock held while a customer pays and released when the window passes, a waitlist that gets a timed offer when something frees up, and reminders that go out before the date, not after it.
Incoming payments matched to invoices and records, refunds and payouts prepared in batches with the evidence attached, and nothing sent until a person approves the batch.
The numbers you ask for every week pulled from the system itself: queues, digests, finance views, and an alert when something has been waiting too long for someone.
Invoices, IDs, forms and contracts read by a model, checked against rules and filed as structured data, with the original kept and the uncertain cases flagged for a person.
Replies drafted from the customer's own records, reminders that go out on time, and the conversation handed to a person with the context attached when the question is new.
Holds that release on time, offers that lapse, data swept when its retention ends, and syncs that run every few minutes without anyone starting them.
The step that used to wait in a chat thread becomes a queue: the right person sees what needs a decision, approves or returns it, and the flow continues with the decision logged.
One workflow first. It runs beside the old process until the numbers say it holds, then the next manual step goes.
We sit with the people doing the work and write down how it actually flows: tools, hand-offs, exceptions, where it waits. The first workflow is chosen here, usually the one with the most re-typing.
Which steps a machine may take alone, which need a person, and what gets logged. Agreed in writing before any build.
The new flow runs beside the old one on real work, with the same tests and logs as the rest of a product. Nothing is switched off until you trust it.
Only with your own numbers, from your own logs: cycle time, errors, hand-offs, work waiting on someone. Agreed before the build, reported by the system.
Switch over, remove the next step still done by hand, add the next workflow. Alerts when something fails, and a person who knows the system answering you.
Most of the value in automation lives between systems, so we start from the tools your team already knows. If a tool has an API, a webhook, an export or an inbox, we can connect to it. The jobs, queues and agents run on Supabase or Railway, with a page that shows what ran and what is waiting.
We do not ask you to replace your tools to automate them. When a tool cannot be reached live, we say so in the scope and work from scheduled exports and imports rather than promising a connection that does not exist.
A named person confirms before money moves, private data leaves, or a customer is contacted about something new. The approval is a step in the flow, not a habit someone has to remember.
Every automated action is logged with what it read, what it did and who approved it. Failed steps retry safely, and the steps that move money are reversible.
Prompts are tested against a set of real cases before they run and again after every change, so an AI step keeps behaving the way it did on the day you signed it off.
The jobs and the model see only the records they need. Enforcement is on the server, with row-level security, not in the prompt.
We learned this work by building and running our own products. Everything below runs today in products we maintain.
A scheduled job runs every minute in Barari's database: unpaid reservations release their seats and manual payment claims past their window expire, with the buyer told.
When a seat frees up in Barari, the next person on the waitlist gets a timed offer; unanswered offers lapse and the seat moves on.
A scan at the trailhead records attendance, and Barari's private album opens only to the people on that record.
Cancellations create refund requests; refunds and weekly payouts run in batches with bank evidence, and a returned payout needs a second person.
Every fifteen minutes a scheduled job in Stead asks what is waiting in the verification queue and emails ops, so no submission depends on someone remembering to look.
Rizzolution's server checks every five minutes which reminders are due, respects each user's quiet hours and daily cap, and sends each one at most once.
Every project is scoped on its own, after a short mapping call in which we write down the workflow as it happens today and agree what the first version should do. Scope decides cost: how many systems the flow touches, where a person must approve, and what has to be tested before money or private data moves. We send a scope and an estimate after that call, not a price list before it.
Most engagements begin with one workflow, and the aim is to have it running beside the old process within weeks rather than months. The exact time depends on how many systems it connects to and how quickly we get access to them. The plan, with its first milestone, is part of the scope we send after the mapping call.
Email, spreadsheets, CRMs, payment providers, WhatsApp and other messaging, databases and the apps you already run. If a tool has an API, a webhook, an export or an inbox, we can connect to it. We build the jobs and agents on Supabase or Railway, and we tell you before the build if a tool can only be reached through scheduled exports rather than a live connection.
Yes, and we prefer it. Most of the value in automation lives between systems, so we start from the tools your team already knows and connect them. We only propose replacing a tool when it cannot be connected or when it is the cause of the manual work, and we say so in the scope.
It stops and asks. Every AI step in a flow has a defined set of cases it may handle alone; anything outside that set, or anything it is not confident about, is flagged for a person with the record and the draft attached. Money, private data and new customer contact always wait for an approval, whether the model is confident or not.
The jobs and the model see only the records a step needs, enforced on the server with row-level security rather than in the prompt. Every automated action is logged with what it read, what it did and who approved it. Before the build we write down which data goes to which model provider and for what, so you can approve it, and we keep that list current.
Yes. Both languages are first-class in everything we build: assistants reply in the customer's language, notifications go out in the user's language, layouts work right-to-left and left-to-right, and discovery calls can be held in either.
The flow is monitored: alerts when a step fails, a page that shows what ran and what is waiting, and a person who knows the system answering you. Ongoing support and the cadence of reviews are agreed per project, and each review usually removes the next step that is still done by hand.
Describe the work as it happens today: who does it, with which tools, and where it goes wrong. We reply with questions first, then a scope and an estimate.