VideoAmp is a media performance platform: the cross-channel system buyers and sellers use to buy, sell, and measure advertising. So when VideoAmp set out to add AI-powered reporting to that platform, it started from a harder bar than conversational query — plausible answers were never going to be good enough. Credible is the AI Analytics Engine underneath: it helps VideoAmp capture what its measurement actually means, then delivers the relevant part of that semantic data model to the agents on every question, so answers come back with VideoAmp's verified numbers instead of the AI's assumptions.
The challenge: almost right is still wrong
VideoAmp wanted to give clients answers to complex queries in real time, rather than a dashboard export and a spot in the analyst queue. But speed and plain-English interaction with a data product are becoming the norm, not a differentiator. For VideoAmp the bar sits higher than conversational queries and usability alone. Its measurement is what buyers and sellers use to plan campaigns, transact on ad inventory, and defend budget decisions after the fact. A figure that looks right and isn't doesn't stay inside the platform. It ends up in a business plan, a contract negotiation, or a budget decision.
Measurement data is also where generic AI struggles most. The rules that make a number correct sit with the people who built the methodology, not in the database and certainly not in the LLM's base training. Those domain experts know which metrics can be summed, which double-count when combined, and which numbers only mean something at a particular grain. An AI pointed straight at the raw data sees none of that and answers confidently anyway.
So the question VideoAmp started with was whether it could build an AI solution that it — and, more importantly, its clients — could stand behind for the highest-stakes business decisions.
“Our measurement backs real media spend, so an AI answer that looks right and isn't could cost the business. We documented what every metric means and what it's allowed to do, and our agents reason from that instead of their own assumptions. Credible helped us put the right definitions in front of them on every question.”
By the numbers
- 47% → 100% coverage. The share of questions the engine could answer from VideoAmp's own definitions, first launch to live.
- 35 of 35. Given 20 analyst questions with no answer key, the agent surfaced every dimension and measure the correct answers depend on — VideoAmp's governed definitions, not the AI's guesses.
- 13 of 13 weekly campaign trends matched exactly, with metrics matching certified reporting to the decimal.
- 4 of 4 declined. Asked for data the report doesn't carry, the AI named the gap all four times instead of producing a plausible figure.
- 2,000+ definitions, business rules, and warnings, authored and owned by VideoAmp's product, engineering, and data science teams.
- About four months from kickoff to a live demo at Cannes Lions.
The build: give the AI the right metrics for the question being asked
VideoAmp's reporting carries a large number of metrics and dimensions, and the rules for combining them correctly aren't visible in the data itself. Working with Credible, VideoAmp turned those rules into a governed data model. Credible delivers the relevant part of it to the AI on every question, so the AI reasons from the right metrics instead of filling the gaps on its own.
The methodology moved out of people's heads and into the product
VideoAmp's team wrote down what their data means at production depth: more than 2,000 plain-language definitions, business rules, and warnings, covering everything from what "reach" is allowed to do to which questions belong to which data. Rules that used to live across data science, engineering, and product are now infrastructure the company owns — versioned, reviewed, and maintained by the same teams that build the rest of the platform.
The system refuses to answer rather than answer wrong
The governed model helps the agents find the right answer and stops them producing a wrong one. Queries that would double-count a metric return an error. Questions that need a specific grain are forced to choose one. When data is partial or out of scope, the AI says so instead of improvising.
Tested against certified reporting before launch
Before launch, VideoAmp tested the AI against an exhaustive set of real analyst questions with answers certified in its existing reporting. Metrics matched to the decimal. Thirteen of thirteen weekly campaign trends matched exactly. The questions that fell short have a direct feedback loop from the engine's telemetry, and fixes to the model are tracked, versioned, and re-tested like any other production change. When the AI mishandled a question in production, the correction went into the model once and every future answer inherited it. No prompt patches, no whack-a-mole.
The definitions are not a property of one chat box. The same governed model is already extending to additional AI product lines and to VideoAmp's internal reporting, so each new surface starts from documented meaning instead of a blank page.
The impact: AI VideoAmp can put in front of clients with confidence
- Validated before launch. Answers match certified reporting, which is the bar to clear before putting anything in front of a client who transacts on the numbers.
- Bounded by the data. The AI can't produce answers the measurement doesn't support, reach into other advertisers or reports, or interpret the methodology on its own terms.
- Owned by VideoAmp. The model is maintained as standard product infrastructure, under the same review and release discipline as the rest of the codebase.
- Reusable. Each new surface starts from captured meaning instead of a blank page.
About VideoAmp
VideoAmp is a media performance platform that provides the infrastructure for media buyers and sellers to find their most valuable audiences, optimize to what's working, and measure the real-world impact of advertising. By leveraging big data, privacy-forward technology, and AI, VideoAmp delivers a comprehensive view of media performance across streaming, digital, and linear TV, so every campaign decision is made with clarity and every result is measurable.