Build or Buy a Restaurant Data Platform? Yes.

Somewhere around location fifteen, a version of this conversation happens in every growing restaurant company. Someone on the leadership team, often the CFO, sometimes a newly hired head of IT, looks at the monthly software bills and says it out loud: "Why don't we just build this ourselves?"

It's a fair question. Snowflake is a credit card away. Every POS vendor publishes an API. On a whiteboard, a custom restaurant data platform looks like a warehouse, a few pipelines, and a dashboard tool.

Plenty of operators are already past the whiteboard. They have an engineer three months into a Snowflake project, a half-finished Toast connector, and a growing suspicion that the second half is going to be harder than the first.

So when operators ask us whether they should build or buy, our answer is yes.

Not as a dodge. Both paths are real, we run both of them, and which one is right for you depends on facts about your company that we can't guess from here.

Two paths, one team

CONVX builds custom data platforms for a living. Our heritage is enterprise and public-sector work where custom is often the only option that clears the requirements. If your situation calls for a platform built to your specification, we will scope it and build it, soup to nuts.

CONVX also makes OpSage, a restaurant intelligence platform you can license today and connect to your POS this afternoon. It runs on the same data foundation and the same permissions model as our custom work, packaged so an operator of any size can afford it.

The two feed each other. What we build for enterprise clients hardens into the product, and what the product learns goes back into every custom build. That's why the choice in front of you is not a choice between a good option and a compromise. It's a choice between two delivery models for the same underlying capability.

What a build requires

If you go the custom route, whether with our team or your own, this is the parts list. We published the full itemized version in Restaurant Data Platform: Build vs. Buy (and a Bill of Materials). Here is the short form. Print it, hand it to whoever proposed the build, and ask them to put an owner's name next to every line. The exercise is clarifying either way.

1. Core data infrastructure. Warehouse and compute across dev, staging, and production, object storage, pipeline orchestration, ELT tooling, and the observability that tells you a sync failed before your operators act on a week of wrong numbers.
   
   
2. Integration connectors. POS, labor, inventory, reviews, and weather, each with credential storage, token refresh, retry logic, and schema-change detection. Toast alone is four separate data streams.
   
   
3. Data modeling and the semantic layer. Entity resolution, brand and region hierarchy, day-part definitions, and metric definitions everyone agrees on, maintained every time you open a store or change a menu.
   
   
4. Analytics, ML, and AI. Anomaly baselines tuned to your concept, forecasting trained on your own history, sentiment scoring, a text-to-query layer, and the retraining that keeps all of it accurate.
   
   
5. Security and access control. Identity provider and SSO, role-based access enforced at the data layer, sensitivity classes so payroll detail stays with the people entitled to it, plus audit logging and same-day revocation.
   
   
6. Delivery and the operator experience. A web app a manager will open on a phone in a stockroom, scheduled PDF and CSV reports, email and SMS alerting, Slack delivery, and a support path for the GM whose numbers look wrong on a Sunday.
   
   
7. The people. Data engineer, analytics engineer, a share of a security engineer, a data scientist once ML enters the picture, and a product owner who knows restaurant operations well enough to arbitrate metric definitions.
   
   
8. The ongoing tab. Compute and storage as volume grows, connector maintenance that never ends, on-call coverage, security review on a cycle, and roadmap work your operators will ask for the moment version one goes live.

The math, stated plainly

Put conservative numbers on the build. A minimal team is one data engineer and one analytics engineer, with a fraction of a security or platform engineer's time. In the current market that's roughly $350,000 to $500,000 a year fully loaded, before a line of ML. A realistic timeline from kickoff to a platform your operators trust is 12 to 18 months. Warehouse and tooling costs are real but small next to the people.

A 50-location group is plausibly looking at $500,000 or more in year one, plus a permanent engineering line item after that.

Now the product. OpSage's Growth tier, built for multi-unit and multi-concept operators, runs $59 per location per month on an annual contract. For that same 50-location group, roughly $35,000 a year, with unlimited application users and no per-user fees for alerts, reports, or distribution.

There's also a cost that never appears on the spreadsheet: time to answers. A build delivers its first trustworthy insight in a year, sometimes longer. An OpSage workspace answers questions about reviews, sentiment, and weather within about an hour of adding locations, and cross-domain analysis sharpens as each integration comes online.

When the build path is the right one

Custom is the right call in specific situations, and pretending otherwise would be vendor spin.

Build when your requirements are unlike anyone else's: proprietary systems no connector will ever support, regulatory or sovereignty rules that dictate exactly where every byte lives, or a business model where the data platform is itself the product. The same goes for workflows so particular that configuring around them costs more than writing them from scratch. And if you already employ a data engineering organization, this becomes one project among many that team will own for years, which changes the math considerably.

If that describes you, talk to our professional services team. We do this work, we scope it straight, and you get the parts list above with real numbers attached to every line.

When the buy path is the right one

For most restaurant companies, the problem is hard in a shared way. Every multi-unit operator fights the same fragmentation: sales in the POS, hours in the labor system, cost in the inventory tool, and sentiment scattered across three review sites. A shared problem is what a product is for, because the R&D gets spread across hundreds of operators instead of sitting entirely on your P&L.

Buy when you want answers this quarter rather than next year. Buy when the people who would run the build are the same people already keeping your POS rollout on schedule. The strongest argument is usually the last one: your engineering budget should go toward something a competitor can't copy, and that is rarely a data pipeline.

Five questions that settle it

1. Is your data problem unique, or just painful? Painful is universal. Unique is rare, and only unique justifies custom.

2. Who maintains it in year three? If the answer involves one person who might take another job, that's your risk profile.

3. What's the fully loaded cost across five years? Count salaries, not just cloud bills, and count the years after launch.

4. How long can your operators wait? A year of building is a year of decisions made on instinct.

5. Does the option you're leaning toward clear your security bar? For OpSage that means row-level access enforced at the data layer, five layers of defense, and a SOC 2 Type 2 certified identity provider. Hold any build plan to the same standard and watch the estimate grow.

Build or buy? Yes.

You're not choosing between a real answer and a consolation prize. You're choosing a delivery model, and the decision belongs to you.

Pick the product and you get an enterprise-grade foundation, fully managed, running the same week, at per-location pricing. Pick the build and you get a platform shaped to requirements only you have, from a team that has done this work for organizations with the strictest standards in the country. Either way the integrations, the semantic layer, the ML, and the security model come from the same bench.

Bring us the version of the meeting you've been having. Book a demo, or start with the pricing page and the FAQ and Release Notes if you have questions before that call.

Find us at FSTEC. Come tell us where your build-or-buy conversation is stuck. We will show you OpSage Ask running a root-cause analysis across sales, labor, weather, and reviews at once, then show that same answer coming back inside Claude or ChatGPT under the same permissions. Bring the question your current reporting stack cannot answer.