( 03 )Data

AI analytics you can ask in plain English.

We build AI-powered analytics that answers business questions directly. Ask in plain English and get the number, the chart behind it and the data it came from, without waiting in the analyst queue.

Start with a Spark session

Ask your data a question and get an answer, not another dashboard.

( Drag )
A simple question waits a week in the analyst queue.
( Go )
Ask in plain English, get the number and the chart behind it.

( Sound familiar )

Signs it's time.

If two or more of these sound like your week, it's worth a sprint.
  1. 01

    Simple questions wait days for someone who knows the database.

  2. 02

    There are plenty of dashboards, and still no clear answer.

  3. 03

    The same report is rebuilt by hand every week or month.

  4. 04

    Problems show up in the numbers long after they started.

( What we build )

What Smart Analytics covers.

Each piece can ship on its own. We start with the one that removes the most drag.

  • Natural-language BI

    Ask a question in plain English and get an answer drawn from your own data, with the query shown so it can be checked.

  • Live dashboards

    A small set of dashboards that track the numbers your team actually decides on, refreshed automatically.

  • Data pipelines

    Reliable pipelines that bring sales, operations and finance data into one place, cleaned and ready to query.

  • KPI monitoring

    The metrics that matter, watched continuously, with a heads-up when one moves out of its normal range.

  • Anomaly detection

    Models that spot unusual patterns in orders, costs or usage early, while there is still time to act.

( Where it fits )

Built for real teams.

Retail
Ask which products and stores drove last week's change in margin.
Manufacturing
Watch throughput and scrap rates, and flag a line that drifts.
Technology
Answer product-usage and revenue questions without writing SQL.

( How it runs )

Spark. Build. Go.

Every smart analytics project runs the same three moves.
  1. Step ( 01 )

    Spark

    One working session with your team to find where the hours leak. Then we pick the single idea worth building first.

  2. Step ( 02 )

    Build

    We design and engineer a working AI product, wired into the tools you already use, and put it in front of real users early.

  3. Step ( 03 )

    Go

    We ship it, measure it and tune it until it runs on its own. Then we find the next drag and go again.

( Questions )

Fair questions.

( 01 )

What is natural-language BI?

Natural-language business intelligence lets anyone ask a question about company data in everyday words and get an answer, instead of building a report or writing a database query.

( 02 )

How do we know the answers are right?

Every answer shows the data and query behind it, and we test the system against questions your analysts have already answered before it goes to the wider team.

( 03 )

Do we need a data warehouse first?

Not necessarily. If your data is spread across tools, building the pipeline that brings it together is part of the project.

( Keep going )

Other AI solutions.

Let's go.

Tell us where the hours go today. We'll come back with the first thing worth shipping, and what it takes to get it live.

[email protected]