Meridian Analytics is a B2B SaaS company that sells a business intelligence platform used by operations and finance teams at 250+ mid-market companies. Their core product — a reporting and dashboard tool — has strong retention but faces increasing competitive pressure from AI-native entrants that offer automated insight generation.
The Chief Product Officer has committed to enterprise customers that Meridian will ship an AI Assistant feature in Q2 — a conversational interface that lets users query their data in plain language and receive AI-generated summaries and recommendations. The feature has been sold into four enterprise contracts. Failing to ship it on time risks those renewals.
You are the PM assigned to this initiative. You are 3 weeks into the role. Here is what you have learned so far:
Meridian has no internal ML engineering team. All AI capabilities must be delivered via third-party LLM API. The one ML engineer on staff manages existing data pipelines — she is not available for model work.
Four of Meridian's largest enterprise customers have contractual data residency requirements. Their data and queries cannot be sent to external APIs without explicit per-account consent and a documented data handling agreement. Legal has not yet signed off on a standard agreement.
Engineering capacity for this initiative is three full-stack engineers. They have six weeks before the Q2 deadline.
The Customer Success team has flagged that customers are cautious about AI after a competitor's high-profile AI feature failure last year (incorrect financial summaries caused two customer incidents). Trust is a real and present concern — not a theoretical one.
The CPO expects a written brief and roadmap before the engineering kickoff meeting in four days.
Your job is to produce the brief and roadmap the CPO is asking for — and to show your thinking about the hardest decisions you had to make along the way.
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