( 04 )Customers

AI customer intelligence that catches churn early.

We build customer intelligence that reads the signals already in your data: purchases, usage, support tickets and feedback. It groups customers by how they behave, flags the ones drifting away and suggests the next best move for each account.

Start with a Spark session

Know every customer like they are your only one.

( Drag )
Customers leave quietly, and the signs were in the data all along.
( Go )
Churn signals flagged early, with the next best move for each account.

( Sound familiar )

Signs it's time.

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

    You find out a customer is unhappy when they cancel.

  2. 02

    Every customer gets the same message, offer and follow-up.

  3. 03

    Support tickets and reviews are read one by one, and the patterns are missed.

  4. 04

    The account team decides who to call next by gut feel.

( What we build )

What Customer Intelligence covers.

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

  • Segmentation

    Customer groups based on what people actually do, not just who they are, updated as behavior changes.

  • Personalization

    Messages, offers and content matched to each segment or customer, inside the channels you already use.

  • Churn signals

    Early warnings when an account starts to look like the ones that left, with the reasons shown.

  • Sentiment analysis

    AI that reads tickets, reviews and survey answers at scale and surfaces the themes behind them.

  • Recommendations

    The next best product, offer or action for each customer, ranked so the team knows where to start.

( Where it fits )

Built for real teams.

Retail
Spot lapsing shoppers and send the offer most likely to bring them back.
Manufacturing
See which distributors are ordering less before the quarter closes.
Technology
Flag accounts whose usage is dropping, ahead of renewal.

( How it runs )

Spark. Build. Go.

Every customer intelligence 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 AI customer intelligence?

It is the use of AI to combine customer data, such as purchases, usage and feedback, into a clear picture of each customer: who they are like, how they feel and what they are likely to do next.

( 02 )

What data do you need?

Usually the data you already have: CRM records, orders or usage logs, and support conversations. We start with what is available and add sources only when they improve the result.

( 03 )

How does the team use it?

The signals show up where the team already works, like the CRM or a daily list, so acting on them does not mean learning another tool.

( 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]