AI Assistant Suite for Product Discovery & GTM

Product teams repeat the same analytical artifacts in each cycle: synthesizing interviews, creating CSD matrices, defining personas, mapping journeys, writing testable hypotheses, planning GTM. Each artifact requires a method. Every time it is done manually, the format varies, insights are lost, and senior work hours go to structuring tasks instead of judgment. Using a generic AI chat does not solve this: without a defined system prompt, framework, and output format, each session produces a different result, making it impossible to chain in a pipeline.

A suite of 17 assistants built as Custom GPTs in ChatGPT (Plus or Team). Each assistant has a structured system prompt that defines persona, reasoning framework, constraints, and output format, using techniques like Zero-shot, Few-shot, Chain-of-Thought, and XML placeholders {{VARIABLE}} to maintain consistent results. Some carry reference examples via Knowledge to calibrate depth and style. None require external tools, code, or backend: they are shareable with the team and immediately usable.

The suite is not a loose collection. The assistants are designed to chain together: the outputs of Affinity Diagram, Qualitative and Quantitative Summary, and CSD Matrix feed into the Desk Research Summary; segmentation artifacts converge in the Segmentation Summary; GTM strategy and A/B tests converge in the GTM Summary. The result is a discovery-to-launch pipeline operated through a chat interface.

Phase 1: Discovery and Research

• Affinity Diagram Specialist: transforms research responses and transcripts into an exhaustive hierarchy of themes, with qualitative, quantitative analysis, and correlations.
• Qualitative and Quantitative Analysis Specialist: synthesizes documents and datasets into hierarchical topics with qualitative, quantitative, and correlation layers, using placeholders {{DOCUMENT}} and {{EXAMPLES}}.
• CSD Matrix: organizes research data into Certainties, Assumptions, and Doubts, with traceable evidence associated with each item.
• Desk Research Summary: consolidates the outputs of the three previous assistants into individual summaries, cross-cutting themes, and an integrative conclusion.

Phase 2: Definition and Strategy

• Target Audience: detailed analysis and strategic segmentation of the target audience, with justifications for each segment.
• Persona and Empathy Map: creates detailed personas and empathy maps with representative phrases.
• User Journey: maps the journey in table format, with correlations between stages and elements.
• Ideal Customer Profile (ICP): builds ideal customer profiles based on data.
• Segmentation Summary: synthesizes the outputs of Target Audience, Persona, Journey, and ICP into a single view.
• Business Solutions: develops solutions using Value Proposition Canvas, Product Vision Board, and Lean Canvas.
• Hypotheses and Metrics: maps testable hypotheses and metrics using the AARRR, HEART, and Jobs to Be Done frameworks.
• Solution Space Summary: integrates the outputs of Business Solutions and Hypotheses and Metrics into a synthesis of the solution space.

Phase 3: Go-to-Market

• GTM Strategy and Channels: develops comprehensive go-to-market strategies in 7 steps.
• A/B Test Plan: creates structured experimentation strategies with A/B tests.
• GTM Summary: consolidates GTM strategy and test plan into an integrated synthesis for launch.
• Report Generation: produces market intelligence reports with an 80/20 focus, ready for stakeholders.

Transversal

• Prompt Engineer: transforms vague descriptions into structured prompts (persona, context, CoT, few-shot, rules, format), with the rationale explained. This is the meta-assistant used to build and evolve the others.

• 17 reusable assistants covering the product cycle from end to end, with zero code and zero backend
• Outputs in predictable and chainable formats: each artifact from one phase serves as input to the next
• Shareable with the team without technical onboarding; anyone can open and use it
• Each artifact is no longer restructured from scratch each cycle: the fixed format and embedded framework eliminate the manual assembly step
• In use in real discovery projects, from research gathering to go-to-market planning

The lesson from the suite: the difference between "using ChatGPT" and having an AI-assisted discovery operation lies in the system prompt. Fixed format, explicit framework, and traceable evidence transform a generic chat into a methodical tool. And it is the method that allows chaining the artifacts in a pipeline.