Ask your KPIs anything
Ask in plain language, “How much did we sell in the north yesterday?” or “Which customer stopped buying?”, and get the answer instantly, along with the source it comes from.
AI for business
AI agents that work with your company’s data
Ask your data questions the way you would ask an analyst: sales, inventory, customers and orders, answered instantly with reliable information.
Sales assistant
Illustrative example · sample data
- Martina (Head of Sales): Which customers stopped buying this month?
- Assistant: 38 customers who used to buy every month didn’t buy in September. They represent $420K in monthly sales. I’ve already built the list by sales rep, sorted by revenue.Source: ERP and CRM · updated today at 7:00 AM
- User: Which products are about to run out of stock?
- Assistant: Three products have less than 5 days of stock left:
- Classic Chips 400 g: 3 days
- Frozen Fries 1 kg: 4 days
- Instant Mashed Potatoes 125 g: 4 days
What you can solve
Artificial intelligence applied to real problems
It’s not about adding yet another chat window, but about saving hours of work and deciding sooner. These are the use cases that create the most value in commercial and industrial companies.
Your numbers on WhatsApp or Teams
Your sales team checks sales, inventory and targets from their phone, in the same app they already use every day.
Automated order entry
The agent reads free-text orders that arrive by WhatsApp or email, enters them into your system and flags anything that is missing.
Alerts with context
It spots deviations in sales, inventory or collections and alerts you, explaining what happened and who is affected, before the problem grows.
Forecasts and recommendations
Predictive models for demand, pricing or customer churn, with concrete recommendations your team can act on.
An assistant for your documents
Quick answers about price lists, sales policies, manuals and contracts, citing the source document.
The foundation
AI is only as good as the data it runs on
If the data is duplicated, out of date or scattered across spreadsheets, the agent gives poor answers. That’s why every project starts by gathering, curating and organizing the information it will use.
Data journey
Explore the chart
Hover over or tab through each element to see what it contributes and how we handle it.
1 Data sources
- CRMSalesforce · HubSpot
- ERPSAP · Odoo · Dynamics
- Excel and spreadsheetsTargets · prices · stock
- .txt and .csv filesExports · logs
- WhatsAppOrders · inquiries
- EmailOrders · attachments
- DatabasesSQL Server · Oracle
- APIs and e-commerceMercado Libre · Shopify
2 Curation, cleaning and organization
- IngestionWe connect every source automatically and securely, and centralize the data in a single repository with scheduled updates.
- CleaningWe fix data-entry errors, invalid formats and missing values, and extract useful data from free text such as messages and emails.
- DeduplicationWe merge duplicate customers, products and suppliers across systems so each one has a single record.
- StandardizationWe bring everything to the same units, currencies, dates and codes so data from different sources can be compared.
- ValidationWe apply quality rules on every update and raise alerts when something doesn’t add up, before it reaches a decision.
- OrganizationWe organize the information into a documented data model with permissions and lineage: ready for dashboards and for AI.
3 AI agent
An artificial intelligence agent connected to curated data answers accurately, without making up numbers: it queries, cross-references and analyzes information from across the company in seconds.
4 Outcomes
- AnswersIn plain language
- ForecastsSales and demand
- AlertsProactive and timely
- AutomationRepetitive tasks
Trust and security
How we design every agent
It answers with data, it doesn’t make things up
The agent queries curated, verified information. If it doesn’t have the data, it says so instead of improvising an answer.
Everyone sees only what they should
We respect the permissions in your systems: a sales rep sees their own accounts and leadership sees the full picture.
Traceable answers
Every answer shows which source it comes from, so anyone can verify it.
In your own cloud
Whenever possible, we deploy the solution in your company’s cloud, for example in your Azure subscription.
How we get started
From idea to a working agent, step by step
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Assessment
We identify the highest-impact use cases and check whether your data is ready for each one.
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Data preparation
We integrate, curate and organize the sources the agent will need.
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Focused pilot
We launch a first agent with a group of users and measure the results.
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Scale-up
We add users, sources and use cases based on what worked.
Does my data need to be organized before we start?
No. Most of the time it isn’t, and that is exactly our specialty: preparing the data is the first step of any AI project that works.
Which artificial intelligence models do you use?
We work with the leading models on the market, such as Claude (by Anthropic) and OpenAI’s models, including through Azure OpenAI, and we choose based on the use case, the cost and where your data needs to live. For forecasting, we also build our own predictive models.
Will my data be used to train third-party models?
It shouldn’t, and we take care of that in the design: we use enterprise services that don’t train their models on their customers’ information, and we review it with your team before we start.
Will AI replace my team?
No. It takes over repetitive work, like looking up numbers, building reports or entering orders, so your team can spend its time analyzing and deciding.
Where do I start?
With a data assessment: in two weeks you’ll know which AI use cases are feasible with your current data and what is missing for the rest.
Try an AI agent with your data.
On a call, we’ll show you which questions an agent could answer in your company.