Weekly Progress

This week marked a major evolution in Entonomy’s journey toward a fully autonomous venture creation engine — one capable of identifying market problems, scoring them for opportunity, mapping them to product archetypes, and launching them as ventures without human intervention. The focus was on building an end-to-end automated problem discovery and conversion system at scale, alongside significant targeting automation improvements and live campaign testing.

1. Automated Problem Discovery & Scoring
We built and deployed the new Venture Problem workflow, designed to simulate real market conditions and surface problems with measurable commercial potential. The process begins by selecting a random historical date within the last year. This date is used to seed a Query Generator GPT agent, which outputs ten realistic search queries for problems people might have been discussing around that time.

These queries are passed to Perplexity, which returns a structured JSON array of ten real-world problems, each tied to specific events or trends relevant to the simulated date. The output includes:

• Problem text (raw from Perplexity)

• Source URL (if available)

• Tags (short keyword summaries)

From there, a Problem Parsing Agent refines and normalizes the data:

• Name: Concise 2–5 word title

• Text: 3–5 sentence cleaned and clarified problem description

• Tags: Three lowercase keywords

• Source URL: Valid or null

• Market Scores: Ten dimensions scored 0–10 — pain, urgency, TAM, monetizability, trending, frequency, competition, scalability, founder fit, emotional weight

• Market Score Profile: Ordered list of the ten scoring dimensions

A code module calculates the Market Total Score (0–100) by summing the individual dimensions, allowing quick comparison across all problems.

2. Mapping Problems to Product Archetypes
Once scored, problems enter the Solution Modeling Agent. This GPT agent evaluates the opportunity and maps it to one or more of our 14 predefined product archetypes (P1–P14), which range from micro apps and AI reports to embedded widgets, asset packs, and managed BPO stacks. For each problem, it produces:

• Model Confidence: Likelihood of successful productization

• Model Top 3: Best three archetypes with scores, suggested stacks (e.g., Next.js + Supabase), and key KPIs

• Solution Hint: Short guidance on how to implement

• Model Best Code: Single best archetype for immediate execution

3. Problem Database Architecture
A dedicated Problem Table in our database now stores every discovered problem and its metadata:

• Description, tags, and source

• All market scores and profiles

• Total market score

• Model evaluations and best archetype

• Flags for promotion to venture and venture ID links

• Solution hints, alternative codes, and confidence metrics

This structure enables long-term tracking, filtering, and promotion of the highest-value problems.

4. Venture Conversion via Perplexity Business Plans
Top-scoring problems are passed back to Perplexity for full business plan generation using the 24 Steps to Disciplined Entrepreneurship framework. These structured plans feed directly into the Venture Create workflow, triggering persona extraction, targeting, creative generation, landing page deployment, and ad launch — all without manual involvement.

5. Targeting Agent Overhaul
Targeting automation was rebuilt into two coordinated layers:

• Category-Specific Agents: Dedicated AI agents handle interests, behaviors, family statuses, income levels, industries, and life events. They are now fed structured JSON from the business plan, eliminating the misinterpretations we previously saw with plain text inputs. For example, the income agent now reads directly from the income field in the JSON instead of trying to infer it from paragraphs of description.

• Education & Work Agent: This agent generates relevant majors, schools, statuses, work positions, and employers. It uses Facebook’s targeting search to pull real audience sizes, builds a flexible targeting object, and merges it with demographic specs like age and gender.
  
  

A validation loop checks whether audience sizes fall within our optimal range. If not, a correction agent generates specific instructions to fix overly narrow or broad targets. The workflow then recursively re-runs until the problem is resolved, with max retries to prevent infinite loops.

6. Lovable-to-HTML Content Automation
Landing page creation was enhanced by enabling Lovable HTML Conversion. We can now take an existing Lovable-designed page (e.g., TotHop), extract the HTML, and programmatically replace text, visuals, and other content based on JSON generated by our agents. This preserves design quality while allowing for high-speed customization across ventures.

7. Live Campaign Testing – TotHop
The TotHop venture received focused campaign testing this week:

• Day 1: 13 leads (landing page promoted as free)

• Day 2: 15 leads (still free)

• Day 3: 6 leads (price introduced at $4.99)
  The pricing change sharply reduced lead flow, and no payments were completed. The price was lowered to $1.99, but still no card attachments occurred. One ad variation was updated to display pricing directly in the creative.

Weekly Learnings

1. Large-Scale Problem Generation is Now Real — The Venture Problem workflow can continuously produce, score, and filter thousands of problems from real-world data, anchored to market signals.

2. Structured Data Eliminates Agent Confusion — Passing structured JSON into category-specific targeting agents has removed prior misclassifications between end-user and buyer profiles.

3. Pricing Significantly Affects Conversion — Early free-offer campaigns had strong lead volume; adding even low pricing caused sharp drop-offs in signups and zero conversions.

4. Recursive Targeting Adjustment Works — The validation and re-run loop can self-correct targeting issues without manual oversight, keeping audience definitions in optimal ranges.

5. Lovable Conversion Bridges Quality & Speed — Combining a pre-designed Lovable layout with dynamic JSON content replacement produces high-quality, ready-to-deploy pages faster than raw HTML generation.
   
   

Plan for Next Week

• Productization Focus on P1 — Prioritize developing live, functional products (e.g., micro apps) directly from venture creation outputs. Explore using GPT-5 or alternative code generation services to produce deployable repositories, commit them to GitHub, and deploy via CI/CD to production servers.

• Pricing & Offer Experiments — A/B test lead capture with free vs. low-cost vs. value-added upsell to find optimal acquisition economics.

• Scale Problem Database — Push the automated generation process toward the 10,000 problem milestone, continuously evaluating and promoting top scorers into ventures.

• Integrate Product Deployment into Venture Workflow — Automate the build and deployment of P1 products as the final step in the venture pipeline.

• Creative Optimization Loops — Establish a recurring cycle for reviewing and refining ad copy, visuals, and targeting for performance improvements.