Buy vs. build the analyticsyour customers keep asking for.
A working guide for senior living software vendors. Work through it and you'll leave with the build priced honestly, three questions to put to your engineering lead, and a checklist you can take into any vendor conversation, including ones that aren't ours.
Where you are, in three numbers.
Want the full picture of the category before you decide? See the vendor track page →
Price the build beforeyour team commits to it.
Ask for the estimate in writing, then check it against this one. Most internal estimates price the visualization layer honestly and underprice everything beneath it.
A production analytics capability, the kind that survives contact with a 60-community operator, needs three things: a data platform, the team to run it, and an AI layer, because that's what your customers now mean when they say analytics. Stack Overflow's 2024 Developer Survey puts the median US back-end developer at roughly $170,000. With standard fully-loaded multipliers, one senior engineer costs $240K to $280K a year, and a credible first version needs about 1.5 to 2 of them.
Year one
Compare the right two things. Not vendor fee versus free, but vendor fee versus two engineers, a year, and a permanent claim on your roadmap. For a vendor doing $10M to $30M, that's a material slice of engineering capacity spent on a capability that's someone else's core business.
If your estimate came in well under this, it's worth finding out what it left out. Three questions that surface it →
Ask your engineering leadthese three questions.
The estimate covers building it. Keeping it truthful is the harder problem, and it's where in-house builds stall after the v1 dashboards ship. These three questions surface whether your estimate accounted for it.
You're not looking for a yes. You're looking for a mechanism. If the answers are confident but general, the expensive half of the work hasn't been scoped yet.
"How will a regional director's permissions be enforced when the AI writes the query, not the UI?"
Names the mechanism, and offers to show you the same question asked by two different roles returning different data. Enforcement sits on every path a model can take to the data, not on the interface in front of it.
"The AI respects our permissions." That's the claim, not the mechanism. Dashboards are a solved problem; AI-written queries are not. Get this wrong in a market that handles resident health information and you haven't shipped an analytics feature.
"When two dashboards disagree on occupancy, whose definition wins, and where does that definition live?"
One place, owned by us, applied at the moment the question is asked, so the same question returns the same answer for every user on every surface. Unglamorous, and it's most of the work.
"It depends on the report." In senior living, close is worse than nothing. An occupancy number can feed a financing covenant, a state report, or a family conversation, and the first wrong one costs you every answer after it.
"Who runs the answer-accuracy regression suite after a model upgrade, and how many test pairs are in it?"
A named owner, a standing suite of known question and answer pairs run continuously, and a human process for triaging drift when the numbers move.
Any version of "we'll test it before launch." Models change under you and schemas drift. This is the single most underestimated line item in every build plan, and it's why "AI insights" sits on the roadmap for three straight quarters.
Got your answers? Now check them against what each path requires. Work through the conditions →
Check what you actually have.
No score, and no verdict from us. These are the conditions each path needs. Read them, count your own boxes, and notice where a column runs out.
Own the whole stack
- Analytics is your core product, not a module wrapped around one. If insight is what you sell, owning the stack is strategy.
- You have, or will genuinely fund, a data team with a leader who has shipped production analytics before.
- Your roadmap and your board can absorb 6 to 12 months and $150K to $340K before customers see anything.
Ship it this quarter
- Analytics is a feature your customers demand, wrapped around a different core product.
- Your data team is small or nonexistent. If you're one of the 56%, this is you.
- There are bake-offs happening this quarter where this is the gap.
Keep the dashboards, add the layer
- You already shipped reporting, and it proves you know the workflows better than any general tool does.
- What customers ask for now is plain-English answers, not more charts.
- You'd rather hire the data team later, from revenue, once the capability is already earning.
If a column came up short, the next conversation is with vendors. Here's what to demand from them →
Take this into everyvendor conversation.
This works for any vendor, including ones that aren't us. It's built from what end users in senior living keep asking for after their software already shipped reporting, which is the pattern worth paying attention to.
Ask for a live demo of each one. Don't accept the claim.
Whichever way the checklist goes, the first move is the same. Start with your own data →
Before it's a build decision,it's a data question.
Both paths assume the data you're sitting on can actually power an analytics product. That's worth checking before you spend a quarter or sign a contract.
Start with one export, or challenge us with as many sources and connectors as you'd like. More sources means the report can show how your tables connect across systems and where the joins break. In 3 to 5 business days Querri sends back a branded 4 to 5 page report on what's clean, what's broken, and the questions your customers could already be asking your product. It's free and it's yours to keep either way. Your file comes back or gets deleted on request.
The numbers behind this guide
Four build vs. buy pages, each one showing its math and its sources.
The real cost of building in-house
$150K to $340K in year one, 6 to 12 months, and a maintenance tail that never ends. The math, line by line, with inputs you can rerun.
Everyone is asking when you ship AI
Anthropic's own analytics agent went from 21% to 95%+ answer accuracy on the same model. The model was never the hard part.
Your export button is a demand signal
Analytics rated 43% of an application's value a decade ago. Your customers want it even more now.
The premium tier you haven't priced yet
A median 25% price premium for analytics, set on static dashboards a decade ago. Build vs. buy decides when you start collecting.
Your product already has the data.Find out what it can support.
Start with one export, or challenge us with as many sources and connectors as you'd like. In 3 to 5 business days you get a branded 4 to 5 page report: what's clean, what's broken, and the questions your customers could already be asking. Free, and yours to keep.