The Mperativ AI Assistant (MAIA) began with a bold goal—to build an AI agent that could think, decide, and solve complex marketing problems. It was designed to move beyond static dashboards, delivering independent analysis and decision-making on complex marketing questions.
We built an agent that combines a large language model (LLM) with a library of purpose-built descriptions for each analytical tool—defining the tool’s role, the right time to use it, and the type of answer it should produce.
Optimized for marketing intelligence, MAIA can choose the right path for tasks like writing performance analyses, comparing KPI progress by business function, identifying funnel strengths and bottlenecks, or highlighting top companies, titles, sources, and deals. With this structured playbook, MAIA routes each request to the right capability before it ever writes a single sentence of narrative.
Step 1: Designing for Revenue-Grade Accuracy
From day one, we knew the bar for accuracy was higher than in most AI use cases. MAIA deals with revenue data—pipeline, bookings, conversion rates—where even small errors can shift an entire strategy. Hallucinations aren’t an acceptable failure mode. Every calculation must come directly from the GTM data lake, and every output must be verifiable.
Step 2: Mapping the Analytical Terrain
Our guides act as MAIA’s operating map. Depending on the question, they determine whether to identify the ideal customer profile, break down performance by channel, compare target account segments, or track movement through the funnel. Each path has its own sequence of queries, transformations, and calculations before feeding results back to the LLM for narrative generation.
Step 3: Prompt Engineering for Business Intelligence
Most LLMs are great at creative writing. Ours needed to think like a revenue analyst. We crafted prompt templates that force MAIA to use data first and interpret second. Dozens of these prompts have been written, tested, and refined—each QA’d for consistency and accuracy.
Step 4: Hardening the System
We’re running controlled tests to refine how MAIA navigates the data lake, finds the right information, calculates it accurately, and explains it clearly. The goal is to take the training wheels off so it can handle complex questions end-to-end, unlocking more adaptive analysis.
Step 5: Eliminating the Human Bottleneck
Legacy AI and BI require teams of specialists to answer complex questions—slow, costly, and hard to scale. Agentic AI changes that by handling the entire process itself, from finding the right data to analyzing it and explaining the results. It’s always on, delivers consistent answers instantly, scales without adding headcount, and comes pre-tuned with marketing expertise. With MAIA, we’re replacing manual effort with scalable, truly agentic AI.
Where We’re Headed Next
The early phase has been about building discipline—helping MAIA follow the right steps, validate results, and resist the urge to “wing it.” Once the foundation is rock-solid, MAIA will operate with far more autonomy, able to follow any line of inquiry and surface insights we didn’t think to ask for.
Building MAIA wasn’t about adding AI to marketing analytics. It was about teaching an AI to act like a seasoned GTM analyst—one that never sleeps, never misses a calculation, and always has the receipts.
