Building AI-native businesses with leaders driving change
Connect your AI to data you trust.
Move a stalled decision to a measured result in weeks.
There's one decision your exco keeps putting off. You know which one. We get your data and AI working together on it, and prove the result on the data you already have. Then the next decision lands faster, on the same foundation.
For leaders driving change who've tried to make AI work, and seen it stall.
Your data exists. The meaning doesn't.
You've done the work. You cleaned the data, bought the software and picked the tools. Then AI made the problem bigger, because your smartest model doesn't understand your company. AI doesn't fix your data problem. It amplifies it.
Now you're the one explaining to the board why all that spend still can't answer a simple question. It shouldn't take three weeks and five people to get one.
- Finance, sales and operations each count customers differently.
- Your AI runs on data nobody trusts.
- The manual report arrives too late to act on.
Fixing AI means fixing the context in your data.
The missing piece is the context layer. It has four parts, built in this order, and each one needs the one before it.
Agreed meaning
Before AI can answer anything, the business agrees what its words mean. What counts as a customer, a stock-out, an active shop, a loyal buyer.
Trusted answers
We work out what the decision needs: where the numbers sit, and what each key word means in each system. Every answer shows where it came from.
A shared picture
AI sees the business the way your leaders talk about it. Shops, orders, gaps, people, and what each is allowed to do.
Context for action
Your AI knows the chain, the history and the rules. So the person who owns the decision can make the call with an answer they trust.
You keep the systems you already have. We add the context on top of them.
We start with where you are.
Every engagement starts with one of three things. Bring the one that keeps you up at night.
- The question you can't answer
- The outcome you can't deliver
- The decision you can't make
Pick your seat at the table. Here's where leaders like you usually start.
CEO
- Which parts of the business are growing, and which are quietly losing money?
- Why does it take three weeks to answer a question I asked on Monday?
- Where should we use AI first to make a real difference?
- Why does every big decision end up waiting on my desk?
CFO
- Where is our cash across the group, and is it working hard enough?
- Why don't finance, sales and operations agree on the same number?
- Which customers and products make us money?
- How confident are we in this quarter's forecast?
COO
- Where are we losing time or money that nobody can see?
- Which sites, stores or routes are underperforming, and why?
- Why do we only hear about a problem once it's already cost us?
- Which suppliers can we rely on?
CIO
- Why can't the business get answers from the data systems we've already paid for?
- How do we give AI our data without losing control of it?
- Which of our master data can we trust?
- How do we prove an AI use case without a two-year programme?
CMO
- Which customers are we about to lose?
- Did our last campaign move sales?
- Why does every report show a different number of customers?
- Where are we missing distribution we didn't know about?
Risk and compliance
- Can we show an auditor where every number came from?
- Which customers or transactions carry risk we're not seeing?
- How do we stay in control once AI starts making recommendations?
- Are we applying the same rules in every system?
Sales
- Which accounts are growing, and which have gone quiet?
- Where is pricing quietly costing us volume?
- Which deals will close this quarter?
- Which customers should we be talking to this week?
Bring the question. We'll find the data it needs. See the solution for every role
Book a use case workshopEveryone fixes the data. We make it useful.
Most data projects try to boil the ocean. They clean everything, land it in a warehouse and stop. We start where they stop and build on the data work you've already done.
Useful data understands your business.
It knows what a customer is, how stores, products and money relate, and where every number came from. That's what AI needs before anyone can trust it.
| Fixed data | Useful data |
|---|---|
| Clean, complete and in one place | Knows what it means and how it connects |
| Tells you what's in the table | Helps you decide what to do next |
| Each system still has its own definition | One agreed definition everyone uses |
| Needs a technical person to query | Anyone can ask a plain question |
| Ends at a tidy warehouse | Starts at the decision |
Laying the foundation for an AI-native business.
You've spent a lot getting your systems and data in order. You hoped it would get you AI and faster decisions. It stalled, because the middle was missing.
That's where we start. We build the context layer first, so your AI finally has something to stand on.
Sooner or later, every business has to become AI-native to compete.
Execution layer
Where decisions get made and acted on. Your people, AI that helps them, and the result measured every time.
Context layer
This is where we startYour business, written down. What things mean, how they relate, the rules that apply and where every number came from.
Infrastructure and data
The systems you already run, from finance and sales to stock and spreadsheets. We connect to them. Nothing gets ripped out.
Every decision you prove makes the next one faster.
The first decision does the heavy lifting. We agree what words like customer, order and margin mean. We connect the systems that hold them, and prove one answer on your data.
The second decision starts from there. The words are agreed and the systems are connected. So it lands faster, and the answer is better, because it builds on the first.
Fix finance's view of the customer, and sales, marketing and risk inherit it. Each decision done well makes the next one faster and better. That's how the impact compounds.
Stalled decision
Different numbers in the meeting. The call goes up to the top, and waits.
Accelerated decision
One set of numbers. The owner makes the call, and the next decision starts further ahead.
From stalled decision to measured result. In weeks.
About a week from the first workshop to a signed Proof of Value. A measured result two to three weeks later. One decision, proved on your data, then the next.
About two hours
Use case workshop
Two hours with the executives who own the problem. You agree the one decision worth solving first, and what success looks like in numbers.
Two to three days
Proof Point Accelerator
A working mock-up on a slice of your data, and an agreed scope for the Proof of Value.
Two to three weeks
Proof of Value
A measured result on one decision, without disrupting the business.
Ongoing
Production
The proven decision in daily use. The next one reuses the same foundation, so it lands faster.
Your call at every gate: go, adjust or stop.
Connect your AI to data you trust, one decision at a time. Four steps, then the Value Accelerator. Every decision you prove makes the next one faster.
What you can hold us to.
Answers you can trust
One trusted picture of your business. Ask a question and the answer holds up.
Understanding in real time
Ask in plain language and get the same answer every time. If it doesn't know, it says so.
Change you can see
See what's shifting through your data as it happens, not in next month's report.
AI that acts, people in charge
Agents do the routine work. Your people handle the exceptions.
Your IP stays yours
Protected whichever way you host it, and never used to train anyone else's model.
What we've delivered.
Real work, on real data. Client names stay private.
Wholesale
Every store on its own system, joined up
A wholesaler ran a separate system in every store. We mapped how they all fit together, so head office could ask one question and get one answer.
Delivered by Streem AI
Who you'll work with
A founder who's run it himself
Michael Cowen, our co-founder, built a data and machine learning business. It grew to over 50 people across London and Johannesburg, with no middle managers.
His measure of success as CEO was a day of no decisions. His keynote asks leaders one question: If you had to rebuild your business from the ground up using AI, what would you do differently?
What's the one decision your business can't make right now?
Bring it to a use case workshop: two hours with the executives who own the problem. We'll start with a short call to check it's a fit.