Offering three founders a free data infrastructure audit — looking for honest feedback in return
I've spent six years as a data analyst, usually as the first hire, inside e-commerce, fintech, and insurance businesses. The pattern I kept seeing: companies have more useful data than they realise and almost none of it is set up in a way that lets them actually use it.
I recently founded Sentier Étoilé to productise what I was already doing; but I'm early stage and I want to work with real businesses before I start charging.
Here's the offer: I'll audit your data infrastructure for free. That means a clear picture of how your data is currently being captured, stored, and distributed across your business, what's missing, where the gaps are, and a practical roadmap for turning it into a scalable system capable of supporting proper analytics and eventually custom AI models.
It'll take about two hours of your time. In return I'm asking for an honest testimonial I can use.
Who it's for: founders running a business with under £10M ARR and believe data can be a strategic asset they are ready to utilise.
If you're interested or know someone who might be, drop a comment or message me directly.
Good initiative. I've done similar audits with funded FinTech and SaaS startups and the most common findings cluster into three areas:
No separation between transactional and analytical data. The same SQL Server or Postgres instance serving both the app and the reporting layer, so queries compete and reports slow down as the app scales.
Business metrics calculated differently in different places. The sales dashboard says one MRR number, the finance sheet says another, the CEO's Power BI report says a third. No single source of truth means every strategy conversation starts with 20 minutes of "which number is right."
No data quality monitoring. ETL pipelines run in silence and founders only discover something broke when a stakeholder notices an anomaly two weeks later.
For founders who want to do a quick self-diagnostic before Cameron's audit (or after), I put together 6 free SQL scripts that surface common data health issues → https://growthwithshehroz.gumroad.com/l/psmqnx
Cameron — solid positioning. The audit-for-testimonial approach is a good way to build case studies in this space.
Thanks Shehroz, I think your second point is something a lot a founders overestimate how difficult it is to solve. A semantic layer is easy and cheap to build early on and prevents so many problems later on
100% agree. The semantic layer is one of those things that feels like overhead until you're managing 4 different MRR definitions across 3 dashboards. In FinTech especially, once compliance, finance, and product are all pulling metrics from different sources, reconciliation eats hours every week. Building the semantic layer early — even just a simple metrics mart — pays off fast. Curious what tool you typically recommend at that stage for early-stage teams.
One underrated challenge in startups:
teams often collect far more data than they actually know how to use.
Over time that creates hidden inefficiencies:
reporting inconsistencies
unreliable metrics
duplicated tools
poor decision confidence
Getting visibility into the infrastructure layer early is valuable, especially before scaling analytics or AI initiatives.
Good opportunity for founders who know data should become a strategic asset instead of just operational exhaust.
https://teams.live.com/l/invite/FAAk3iOSJkDyS11JQE?v=g1
This is a really strong offer — especially for early-stage teams that think they have analytics but are actually sitting on fragmented data sources.
The framing is spot on:
most companies don’t have a “data problem,” they have a “data usability problem.”
What you’re essentially selling (even in free form here) is:
• visibility across systems
• identification of data gaps
• and a roadmap toward decision-grade analytics + AI readiness
That’s high leverage for founders under £10M ARR, especially in e-commerce and fintech where tooling often grows faster than architecture.
One thing that could make this even sharper:
• include a small example output (sanitized audit screenshot or sample “before → after” diagram)
• clarify whether this covers modern stacks (e.g., Snowflake, BigQuery, Segment, etc.)
• highlight what “actionable deliverable” they get at the end (not just findings)
Right now the value is clear, but the artifact they receive could be made more tangible.
Emmanuel here 👋
I work with AI systems, data workflows, and product engineering — I’d be interested in being one of the founders you audit and giving honest feedback on both the technical depth and the clarity of the output.
If you’re still selecting people, happy to collaborate.
https://teams.live.com/l/invite/FAAk3iOSJkDyS11JQE?v=g1
Thanks Emmanuel, dropped you a line on teams.
I think a lot of early-stage founders underestimate how fragmented their data becomes over time until they actually try to answer a simple business question and realize the information lives across 5 different tools.
The “roadmap before AI” angle is smart too. A lot of companies want AI outcomes before their underlying data structure is even reliable enough to support them.
Precisely! I've seen companies spend millions of pounds refactoring data warehouses because the choose the wrong tools and built unscalable data storage systems in the name of quick wins. Building a system that can be built upon for decades isn't as difficult as most founders think.
This is a strong offer because the pain is usually not “we need more dashboards.” It is that the business has useful data scattered across tools, but no clear system for turning it into decisions, forecasting, or eventually AI workflows.
I’d probably make the outcome more concrete in the positioning. “Data infrastructure audit” sounds useful, but founders under £10M ARR may not immediately feel urgency. Something like “find the data gaps blocking better reporting, automation, and AI readiness” makes the value sharper.
One thing I’d watch early is the Sentier Étoilé name. It has a premium feel, but for e-commerce, fintech, and insurance founders, it may create a pronunciation and recall gap. If this becomes a serious data strategy and AI-readiness platform, Beryxa .com would give it a cleaner enterprise SaaS feel.
Thanks Aryan, helping businesses actually use their data is exactly what Sentier Étoilé.
As for the name. Sentier Étoilé means starlit path is exactly what I'm going for
That makes sense. “Starlit path” is a strong founder story, and I understand why you chose it.
The question I’d pressure-test is not whether the meaning is good. It is whether the buyer experiences that meaning before they hit pronunciation, recall, spelling, and trust friction.
For e-commerce, fintech, and insurance founders, that matters because you are selling data clarity, automation, reporting, and AI readiness. The name has to survive sales conversations, referrals, search, and repeat mentions.
If useful, I can do a focused naming/positioning audit for Sentier Étoilé: current name risk, buyer-memory friction, category framing, domain perception, and whether the brand can hold up if this moves from service/audit offer into a serious data strategy or AI-readiness SaaS.
Not a long consulting thing. Just a sharp written breakdown with practical recommendations.
I’m doing a few of these at $99 while refining the format. If useful, connect here and I can give you a clear outside read before you build more assets around the current name:
https://www.linkedin.com/in/aryan-y-0163b0278/
No thank you.
That meaning is strong, and I can see why you like it.
The only thing I’d separate is internal meaning versus buyer memory.
“Starlit path” is a good founder story, but most e-commerce, fintech, and insurance founders will first experience the name through pronunciation, recall, spelling, and whether they can repeat it after one mention.
That matters more in your category because you’re selling trust around data, reporting, automation, and AI readiness. The brand has to feel easy to say, easy to remember, and enterprise-safe before the founder even understands the deeper meaning.
So I would not say the name has no value. It does. But if the product becomes a serious data strategy or AI-readiness SaaS, I’d pressure-test whether Sentier Étoilé carries the buyer-facing clarity you need, or whether a cleaner name like Beryxa would convert better in sales conversations.