AI Assistant Suite for Product Discovery & GTM

Assistants

17 Custom GPTs covering the entire product cycle, from research synthesis to go-to-market planning.

Published assistants: 17
Product cycle phases covered: 3
Required code and backend: Zero

The problem: Product teams redo the same analytical artifacts every cycle, and a generic AI chat does not solve it: without a system prompt, a framework and a defined output format, each session returns something different and nothing chains into a pipeline.
What was done: A suite of 17 Custom GPTs, each with persona, reasoning framework, constraints and output format defined by a structured system prompt, covering Discovery, Definition and GTM.
The result: Artifacts with a consistent, chainable format across the three phases of the product cycle, with no code, backend or external tool — the suite is shareable with the team and usable right away.
Where it applies: Product teams that repeat research synthesis, CSD matrices, personas, journeys and hypotheses every cycle and spend senior hours on structuring instead of judgment.

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Fact sheet

Sector: Proprietary Product
Scope: IAMV Consulting
Model: AI Solutions Builder · Own product
Stack: ChatGPTGPT-4oCustom GPTPrompt Engineering

Solution Architecture

Phase 1 · Discovery

Affinity DiagramQualitative and Quantitative AnalysisCSD Matrix→ Desk Research SummaryConsolidates the three previous outputs

Phase 2 · Definition

Target AudiencePersona and EmpathyJourneyICP→ Segmentation SummaryConverges the segmentation artifactsBusiness SolutionsHypotheses and Metrics→ Solution Space SummaryIntegrates solutions and hypotheses

Phase 3 · GTM

GTM Strategy and ChannelsA/B Test Plan→ GTM SummaryConsolidates strategy and testsReport Generation

The pipeline from discovery to GTM in three linked phases: the output of each assistant feeds into the phase summary, which serves as input to the next phase. The 17th assistant, the Prompt Engineer, is transversal and sits outside the pipeline.

The challenge

Product teams repeat the same analytical artifacts each cycle: synthesizing interviews, creating CSD matrices, defining personas, mapping journeys, writing testable hypotheses, planning GTM. Each artifact requires a method. Each 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 the issue: without a defined system prompt, framework, and output format, each session produces a different result, making it impossible to chain in a pipeline.

The solution

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: the outputs of the Affinity Diagram, Qualitative and Quantitative Summary, and CSD Matrix feed into the Desk Research Summary; the 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.

How it works

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 via 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. It is the meta-assistant used to build and evolve the others.

Results

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 them
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 suite's learning: 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 artifacts in a pipeline.