Your operational teams
The people who need a number a few times a week and currently either wait for an analyst or guess. Less impressive in a board demo, far more likely to still be used in six months.
Service
AI & Advanced Analytics
AI tools that answer questions about your data are genuinely useful, and they are only as reliable as the data underneath them. We help you work out whether you are ready, then build what is missing before you commit.
Book a discovery callA confident answer is not the same as a correct one, and your team cannot tell them apart.
Four preconditions we check first
The technology works. These four things decide whether it helps your business or quietly causes problems.
Everyone agrees what the words mean
Ask most systems for 'revenue' and there are several reasonable answers. An AI picks one, and might pick a different one tomorrow depending on how the question was phrased.
Permissions that still hold
If your access rules live in the reporting tool, an AI querying the database directly walks straight past them. Ask whether a regional manager could see another region's numbers.
A way to sanity-check the answer
The tool should show how it worked the number out, which definitions it used, how fresh the data is and what the previous period looked like. Otherwise the person asking has no way to spot a wrong answer.
Clear limits on what to ask it
These tools are good at 'what was revenue last month by region'. They are much weaker at 'why did churn go up', where a confident wrong answer is worse than no answer at all.
How we test it properly
Most trials are set up to succeed rather than to tell you anything useful. Gathering keen volunteers and asking whether they liked it measures enjoyment, and people tend to enjoy answers that sound confident.
- We use real questions where we already know the right answer
- We score how often it is right, and separately how often it admits uncertainty
- The second score is what tells you whether it is safe to roll out
We will tell you if you are not ready
Every precondition on this page is data groundwork. If it is missing, we will say so rather than sell you something that will produce confident wrong answers.
Common questions
Should we wait until our data is in better shape?
Not wait, but sequence it honestly. Everything on this page is data groundwork, and it is exactly the work these tools are often sold as a way to skip. If it is already in place, this pays back quickly. If it is not, that groundwork is worth doing regardless of whether you ever buy an AI tool.
Do you build custom models or set up existing tools?
Both, and we will argue against building where an existing tool would do. Custom models need ongoing care that is easy to underestimate when you commission them.
What about the AI making things up?
The useful question is whether it knows when it is unsure. A tool that is right eight times in ten but flags the shaky ones is safe to use. One that is right nine times in ten and sounds equally certain every time is not, because nobody can spot the wrong one until they have acted on it.
What we build once the groundwork is there
The work itself, once the four preconditions above are met.
Forecasting
Demand, revenue, churn and capacity, with a stated range rather than a single precise-looking number.
Anomaly detection
Surfacing the unusual automatically, instead of relying on someone noticing a change in a chart.
Predictive models
Built where they earn their place, and retired when they stop earning it, which is the part most programmes forget.
Segmentation and behaviour analysis
Grouping customers by what they actually do rather than by what the categories in your CRM happen to be.
Related services
Ready to turn complexity into your next advantage?
Ask two people in your business to pull last quarter's revenue. If the numbers differ, that is where to start.
Book a discovery call