Everyone's asking about your AI.The model was never the hard part.
Anthropic's own internal analytics agent answered just 21% of questions accurately until they fixed the data foundation under it. Same model, 95%+ after. Here's what that means for the quarter your board just attached to an AI commitment.
Two runs of the same eval, on the same model. Everything that moved sits underneath the model, not inside it.
Nobody agrees what it is. Everybody agrees it's due.
A competitor announces an assistant. A board member forwards the press release. A customer asks about your AI story on a renewal call. Suddenly it's on the roadmap with a quarter attached, and your name is on the commitment.
"Excited to announce our new AI assistant."
"What's our AI story? Two portfolio companies shipped this quarter."
"Where does AI fit on your roadmap? We're being asked internally."
"It's on the roadmap for Q3."
Same model, two answers.The difference was underneath.
The most useful public data on this comes from Anthropic, the company behind Claude, writing about its own internal analytics agent.
The demo always works.Week six is the real test.
From the same Anthropic post, on what happened after their agent was live.
Your data model changes every sprint too. Whoever builds your AI analytics layer signs up to re-verify accuracy continuously, forever. That's the line item that never makes the build estimate, and it's the difference between shipping a feature and staffing one.
Four questions for any AI demo.Ours included.
Ask them of every vendor on your list, and ask them of your own team before you commit a quarter. The answers separate a product that stays accurate from a chat box that demos well.
What happens when our schema changes?
A named process that re-verifies customer-facing answers on every schema change, with someone accountable for running it. "We keep an eye on it" is the same answer Anthropic had before they lost 30 points.
Show me your eval numbers.
A standing regression suite that tests answers, not just code, run on every model upgrade and every schema change, with numbers they'll put on the screen. Spot-checking isn't an eval.
Who owns the definitions, and how often do they change?
A named owner, one place the definitions live, and a process for changing them. If the answer is that the model infers definitions from the schema, then two teams' versions of "active customer" are already in production.
What's your wrong-answer story?
A specific one. Everybody who has run this in production has a story, and how they found out, from their own evals or from a customer, tells you which of those two you'd be.
We'd rather you ask us these four than not ask anyone. A vendor who can't answer them is asking you to trust a demo.
Both paths, on the accuracy questions
The build column assumes the build goes well. The documented case is that accuracy is the thing that stops going well first.
The build-column cost math is shown in full, line by line, on the engineering cost page. Short version: 1.5 to 2 engineers at a $240K to $280K fully-loaded cost, using Stack Overflow's 2024 salary data, for 6 to 12 months.
Build your own AI analytics layer if:
AI analysis is your product
If what you sell is being the team that gets AI answers right, that competency belongs in-house. Own the evals, staff them, and treat drift as your engineering problem.
Your data model barely moves
Stable and simple enough that accuracy drift is a minor risk. When the schema stops changing, the maintenance argument mostly goes away.
You have evaluation capacity
Data engineers with standing time for continuous evaluation, and a preference for owning the stack over owning the roadmap time.
If the pressure is "we need an AI story this year" and the team is already busy, building is how the AI story becomes next year's story.
The accuracy work is our job.The AI story is yours.
Querri is AI-native analytics, white-labeled inside your product. Your customers ask questions in plain language and get accurate answers under your brand. The data foundation work that separates 21% from 95%+ sits with Querri, not on your roadmap.
The chat is the easy eighth
To name the objection directly: this isn't a chat wrapper. Underneath it sits the data foundation that keeps answers accurate, your metric definitions applied on every question, your permission model enforced on every path the AI can take to the data, and a standing eval suite that catches wrong answers before your customers do. On top of it ship the things launch day actually needs: dashboards, embeddable chat, exports, and a tuning interface your product team drives without filing engineering tickets.
Your definitions
Defined once by you, applied on every question, so two teams can't get two answers.
Your rules
The AI inherits your access model and can never answer beyond what the user is allowed to see.
Accuracy, maintained
A standing regression suite on answers, run through model upgrades and schema changes, so drift gets caught here.
Your brand
Ships in your UI under your name. Your sales team demos it as yours, because it is.
These are the answers people act on
Notice what these have in common. Every one is a number somebody spends money against, which is why a confident wrong answer is worse for your customer than no feature at all.
The short answers
How long does it take to go live?
Two gates. Your white-labeled instance is up with your data connected in about a week. Customer-facing launch typically lands two to four weeks in, after tuning, which your product team does without engineering time. The AI layer, the accuracy work, and the data foundation are Querri's side of the line. Compare either gate with the quarter your board just asked about.
What does it cost?
Less than the cost of one engineer, on consumption pricing with free viewing and sharing. The build alternative starts at $150K in year one before maintenance.
We can fine-tune our own model. Doesn't that solve accuracy?
Anthropic's numbers say no. The 21% and the 95%+ came from the same model. Accuracy lives in the data foundation and context layer, and that layer needs maintenance as your schema evolves, not a one-time training run.
What happens when our data model changes?
That's exactly the drift scenario. Keeping answers accurate through schema change is the core of what Querri maintains, and it's the part that turned Anthropic's 95% into 65% in a month when documentation went stale.
One argument, four doors
Every page shows its numbers and its sources, including the honest cases where building is the right call.
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 a worksheet you can rerun.
Your export button is a demand signal
Analytics rated 43% of an application's perceived value a decade ago. Your customers want it more now, not less.
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.
The model isn't the hard part.The data foundation is.
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 your product. Free, and yours to keep.