AI agents built around your processes: the model understands the task, code calculates the numbers
The agent reads an enquiry, a document or a staff question, pulls data from your systems and prepares the result: a quote, an answer, a product card, a summary. Prices, SKUs and totals come from your spreadsheets and ERP, not from the model's memory.
- InputClient brief in PDF: 14 items, deadline, address
- AITurns the brief into a list of items and parameters
- CodeTakes prices from your price list, calculates totals and discounts
- PersonA manager checks the draft and sends the quote
The model does not invent numbers: everything that is calculated is calculated by code from your data.
Six jobs companies bring to us most often
Quotes and estimates from client briefs
The agent reads a brief from PDF, Excel or a scan, matches it with your price list, and code calculates the total. A manager gets a ready draft instead of a manual calculation.
Answers and actions in Odoo, Zoho and SAP Business One
Staff ask in plain words: stock, payments, order status. The agent answers within their access rights, shows where each number comes from, and creates a deal or an invoice at the press of a button.
Product descriptions, specs and photos
Draft descriptions in English and Arabic, supplier price lists into product cards. A photo pipeline: every new product photo goes through the same brand processing and straight into the catalogue.
An executive assistant on WhatsApp
Assignments with statuses, reminders, calendar, summaries. Anything with consequences only after a confirmation button.
Collecting and reviewing open data
Tenders, competitor prices, reviews. Scheduled collection without duplicates, AI picks what matters and writes a short summary.
First line for customers
Customer replies, qualification, an enquiry in your CRM. We have a ready-made product for this.
AI assistantWhy you can trust the agent with numbers
AI understands, code calculates
The model parses the text. Totals, stock, SKUs and specs come from an exact lookup in your data.
Tested on your past data
Before launch we run the agent on 50-100 real examples with known answers and show how many are right and where the errors are.
A person approves consequences
An email to a client, a change in the ERP, a discount: only after a staff member presses the button. Everything goes into a log.
The same approach runs in our store, IWANT
IWANT is a fashion store built on our own engine. We put AI where hours went into manual work or where a shopper was left without an answer. We do not promise a sales uplift in percent: where an effect cannot be measured honestly, we do not claim one.
- 01
Product descriptions
AI drafts the description right in the admin panel, a manager edits and publishes it.
- 02
Photo processing
We retouched product photos with a neural network by hand. For stores we turn this into a pipeline: every catalogue photo in one brand style.
- 03
Size advice
A shopper asks on the product page, AI picks the size from the size chart.
- 04
IRIS stylist
Builds a look from items in stock and answers shoppers on the site.
Our own AI assistant answers ETERN8 clients on WhatsApp and Telegram.
We start with a pilot on one scenario
The price is fixed before work starts. Each stage has its own acceptance criteria, and the code and access stay with you.
- One scenario on your data
- Connection to the systems it needs within the pilot scope
- Testing on 50-100 real examples
- Accuracy report and launch plan
Answers and document processing sit near the lower end, actions in your ERP or CRM near the upper end.
- All scenarios and departments
- Staff interface or WhatsApp
- Access rights and an action log
- Code and access stay with you
We quote after the pilot, once the real workload is clear.
- Updating reference data and agent instructions
- Regular accuracy checks against the log
- Bug fixes and small improvements
Model and server costs are billed separately at actual usage. We estimate the volume in the pilot.
From sample data to launch
Task and sample data
You send 5-10 real briefs or questions, we look at where the data lives.
Pilot
We build the agent for one scenario.
Testing
A run on past data and an accuracy report.
Launch and support
We onboard your staff and watch the log.
When we will turn a task down
Nothing to test against
Without past examples with a known answer, the agent's accuracy cannot be proven.
An error costs more than the saving
We do not automate medical conclusions or legal decisions without a human check.
Plain automation is enough
If a rule or an integration solves the task, we do it without AI: cheaper and more reliable.
Questions about AI agents
Models, data, language and errors.
Describe a task your team does by hand
We reply within one business day: whether it suits AI and what a pilot costs.
Discuss automation
What your staff do now and which systems hold the data.