SummAIze is an AI agent development company. We build custom AI agents for business: assistants that answer customers on WhatsApp or the web from the records they may see, agents that take over repetitive operations work, and models that read documents and data. Each one acts inside rules you set, with a person approving what matters.
An agent is one part of a system. We build the workflow around it, the approvals and the logs, and an app or platform when it needs a screen.
We name agents by the job they take, not by the model behind them. Every one runs with a defined set of records it may read, actions it may take, and cases it must hand to a person.
An agent earns its place where the work needs reading and judgment: a message in the customer's own words, a document that varies, a decision that depends on several records. Where the work is a fixed rule, we automate it without a model, which is cheaper and easier to test. Most engagements mix the two.
An assistant inside an existing product, an agent for one back-office task, or a product built around a companion. In Rizzolution, Rizu remembers weeks of reflections, rewrites a schedule when the user confirms, and surfaces one insight a week with one model call per user. In Stead, one edge function drafts the daily care update and screens messages for scam patterns, writing an audit row for every call.
Eight jobs we build agents for, described as they run. Each one stops at the point where a person should decide.
Answers the questions that repeat, on WhatsApp or the web, from the customer's own records: where is my order, can I move my booking, what does this invoice cover. New or sensitive questions go to a person with the conversation attached.
Reads a request in the customer's own words, checks stock, capacity or policy, records it in your system and confirms back. Missing details are asked for, not guessed.
Matches payments to invoices, prepares refunds and payouts in batches with the evidence attached, and stops at the approval step. Nothing moves money on its own.
Invoices, IDs, forms and contracts read into structured fields, checked against rules and filed with the original kept. The uncertain ones are flagged for a person.
Drafts the reply, sorts the inbox, fills the form, chases the overdue item and writes the weekly summary, inside the tools your team already uses and with every change logged.
A companion with memory the user can read and delete, a draft the owner approves before it sends, a message screened before it does harm. Built into the app you already run.
Answers staff questions from the handbook, the policies and the records they are allowed to see, in Arabic or English, and says so when the answer is not in the documents.
Reads replies to outreach, sorts them, drafts the next message for a person to send, and keeps the list of who is waiting on whom current.
Two traces of the kind every agent writes: what came in, what it read, where a person confirmed, what went out.
customerCan I move my booking to Friday?
agentReads the booking and Friday's capacity, drafts a reply in the customer's language.
approvalApproved
sentReply on WhatsApp. Booking moved, record updated, audit row written.
triggerA payment lands at the bank without a reference.
agentMatches it to the open invoice by amount and payer, notes the bank charge, prepares the entry.
approvalApproved
resultLedger updated, invoice closed, the weekly finance view already current.
One agent, one job first. It runs beside the old process with your team reviewing its drafts, and widens only when the logs say it should.
We start with the conversations or tasks as they happen today: the questions that repeat, the records needed to answer them, and the cases that must stay with a person.
What the agent may read, what it may do, what it must hand over, and what gets logged. Agreed in writing, with an evaluation set of real cases, before any build.
The agent runs beside the old process on real work: drafts reviewed by your team, replies approved before they send. Nothing is switched off until you trust it.
With your own numbers: answered without a hand-off, time to first reply, corrections made by people, cases flagged. Reported from the logs, not from memory.
Widen what the agent may answer, add the next task, and keep the evaluation set growing with every correction. Alerts when something fails, and a person who knows the system answering you.
An agent is only as useful as the records behind it. We connect it to the systems that hold the answers and give it read access to exactly what a conversation needs: the booking, the order, the invoice, the handbook. For conversations, that means WhatsApp, web chat, email or the inside of your own app; for records, your CRM, spreadsheets, database, payment provider and the apps you already run.
A WhatsApp AI agent runs on the WhatsApp Business platform, which needs a verified business account on your side; we take you through that setup. Where a tool cannot be reached live, we say so in the scope and work from scheduled exports instead of promising a connection that does not exist.
Before the first prompt is written we define which records the agent may read and which actions it may take. Enforcement is on the server, with row-level security, not in the prompt.
Every change to app data, every message to a customer about something new, and every movement of money waits for a person. The agent proposes; a person confirms.
Prompts are tested against real cases before they run and after every change. Corrections your team makes become new cases, so the agent is measured against the work it actually sees.
Every model call writes an audit row: what it read, what it produced, what was approved. Memory an agent keeps about a user can be read and deleted by that user.
The agents below run today in products we built and maintain.
In Rizzolution, each reflection distills into Rizu's long-term memory; the user can read the log and delete entries.
Rizu proposes schedule changes and applies them when the user confirms; nothing moves on its own.
A single interpreted observation per week, limited to one model call per user. Cost is a design constraint, not an afterthought.
In Stead, one edge function drafts the daily update from the handbook and the day's facts; the guest approves before it sends, and every call writes an audit row.
The same Stead function screens messages and flags the patterns scams follow in the thread, with the flag held server-side.
The company's first product was an AI study assistant: summaries, text extraction from photos, flashcards and an AI tutor. Retired in 2026, its work on generation, memory and cost control carried into Rizzolution and client work.
Each agent is scoped on its own, after a short mapping call in which we go through the conversations or tasks as they happen today and agree what the agent may read, do and hand over. Scope decides cost: the channels, the systems behind the answers, and the evaluation set we need before it runs on real work. We send a scope and an estimate after that call, not a price list before it.
An assistant that answers from one system, or an agent for one back-office task, is usually running beside the old process within weeks rather than months. What takes time is access to the records behind the answers and the evaluation set we build with your team. The plan with its first milestone is part of the scope.
WhatsApp, web chat, email and the inside of your own app for conversations; CRMs, spreadsheets, databases, payment providers and the apps you already run for the records. A WhatsApp AI agent runs on the WhatsApp Business platform, which needs a verified business account on your side; we take you through that setup.
Yes, and that is where it earns its place. An agent without your records is a chatbot that guesses. We connect it to the systems that hold the answers, give it read access to exactly the records a conversation needs, and route any change it proposes through your existing approval path.
It hands over. Every agent has a defined set of questions it may answer and actions it may take alone; anything outside that set, or anything it is not confident about, goes to a person with the conversation and the records attached. Changes to data, messages about something new and anything involving money wait for a confirmation, whether the model is confident or not.
The agent sees only the records a conversation needs, enforced on the server with row-level security rather than in the prompt. Every model call writes an audit row with what it read, what it produced and what was approved. Memory an agent keeps about a user can be read and deleted by that user, and we write down which data goes to which model provider before the build so you can approve it.
Yes. Assistants answer in the language the customer writes in, Arabic or English, and the evaluation set holds cases in both. Right-to-left layouts, Arabic-first copy and complete English are standard in everything we build.
The agent is monitored: alerts when a call fails, a page that shows what it answered, what it handed over and what was corrected, and a person who knows the system answering you. Corrections your team makes become new cases in the evaluation set, and the support cadence is agreed per project.
Describe the conversations or tasks as they happen today, the records behind them, and what must stay with a person. We reply with questions first, then a scope and an estimate.