What STRATIS is, how it's different, what happens to your data, and how quickly you're up and running. The answers, in plain language.
Find answers to common questions about the platform, how it reasons, how it's governed, and how it works inside your operation.
A marketing intelligence platform that reasons across your entire media operation and produces the recommendations a senior strategist would, at the speed and scale your team needs to act on. Not a dashboard, not a reporting tool, and not a chatbot wrapped around your data: a decision layer that sits above your existing stack and surfaces what to do next, with the reasoning to back it up.
When your team signs in, the platform has already done its overnight reasoning across every channel you run. The home surface shows what changed, what matters, and what to do: live dashboards with pacing and risk flags, structured insight cards, specific recommendations with projected impact, and an approval queue. Your operators approve, modify, or reject, and the platform learns from every decision.
No. A general model with your data plugged in produces text that sounds like marketing analysis. STRATIS produces marketing analysis. It is trained on the reasoning of senior operators rather than web text, configured against your specific brand context, reconciled against live external signal, and it learns from what actually happens when its recommendations are acted on. A general tool has the substrate; STRATIS is everything built above it.
It does not replace your team; it redirects what they spend time on. A large share of analyst capacity goes to recurring reporting: pacing decks, performance reviews, executive pulls, agency reconciliation. STRATIS automates that layer and surfaces the insight underneath, so those hours move to strategy, creative briefing, and the work that compounds. Same headcount, materially more strategic output.
No. Every recommendation requires explicit human approval before execution. The default posture is human-in-the-loop: the platform produces the recommendation with reasoning and projected impact, and waits. Brands can choose to make specific lower-risk classes more autonomous over time, under their own governance, but the default for new deployments is approval on everything.
Through a feedback loop we call the data flywheel: operator feedback, simulated operators, in-model testing, real-world outcomes, causal impact measurement, and long-horizon pattern learning. Each source sharpens a different dimension of the system. The compound effect is that the model producing your recommendations in year three is meaningfully better than the one in year one, even though the interface is the same.
Your operating data stays inside your tenant and is used to configure and improve the system for your brand specifically. It is never used to improve another brand's experience. Platform-level improvements are built by STRATIS from internal and synthetic data, not by borrowing from one tenant to benefit another. In one line: your data improves your instance, not anyone else's.
Yes. Audit is an architectural primitive, not a feature. Every recommendation is preserved with its full reasoning chain, the evidence retrieved, the brand context and external signal at the time, and the operator action taken. Every approval, modification, and rejection is logged with identity and timestamp in an append-only log, so any decision can be reconstructed and traced to the human who approved it.
It is built specifically to prevent the failure mode general AI is known for: confident text that is not grounded in evidence. Every recommendation cites the evidence behind each step, the platform verifies those citations actually support the claim before surfacing anything, and it abstains rather than fabricate when confidence is too low. Recommendations that fail verification are sent back for re-reasoning rather than shown.
No. The platform is built for marketing operators, not data scientists. It handles the data, the analysis, and the reasoning chain; your team brings marketing judgment, reviewing recommendations and providing reasoning when they override. The reasoning is shown in marketing language, and that judgment is the high-value signal that closes the loop.
Three gated phases, each earning the next. Phase 01 is a historical proof on a static subset of your own data, with no live integration. Phase 02 goes live on a single channel with operators in the platform. Phase 03 widens to multiple channels on one campaign. Each gate is a real decision, the infrastructure carries forward, and by the final gate the full engagement decision is made entirely on proof from your own operation.
Results show up on three timelines. Operational, within weeks: reporting time collapsed and the cycle from insight to executed decision compressed from weeks to hours. Performance, across quarters: improvement on the metrics you are measured against, attributable through the impact framework. Strategic, across years: a marketing function at higher leverage, with an accumulated brand context no individual could hold in their head.
Three, worth surfacing. The first three months are the hardest, because the brand context layer is shallow at the start and sharpens with use. The platform requires habit change: operators write down reasoning when they override. And not every recommendation will be right; it is an AI system working on incomplete data, which is exactly why the human-in-the-loop and the gated pilot exist.
Because the platform's value compounds with time on the system, and the compounding cannot be backfilled later. The brand context deepens with every interaction, the reasoning tunes to your operation, and the flywheel produces more signal. A competitor who started six months ago has a head start that gets harder to close. The cost of waiting is the compounding you are not capturing.