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Published Paper Details:
ValidatorAI: A Schema-Constrained Prompt Framework with Search-Grounded Generation for Automated Startup Viability Assessment
Authors
Ratna Patil,Chetan Shelar,Saranya Sawant,Rushikesh Sawale,Athrva Shelke
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Keywords
Large language models, Prompt engineering, Startup evaluation, Search-grounded generation, Structured output generation, JSON schema validation, Retrieval-augmented generation, Business intelligence, Venture capital due diligence, AI-assisted decision systems, Hallucination reduction, Automated startup assessment
Abstract
Pre-seed startup evaluation is a messy problem. There is no funding history to analyse, no revenue to model, and the founder's pitch deck may be the only artefact that exists. Venture capitalists still need to assess market size, map competitors, stress-test the business model, and flag risks--but at this stage, much of that work amounts to educated guesswork.
Large language models can generate business analyses that read well, yet their outputs suffer from two recurring failures: structural inconsistency (reports that cover financials one time and skip them the next) and hallucinated data (invented market figures, nonexistent competitors).
This paper introduces ValidatorAI, a system that addresses both problems by constraining LLM output through a seven-dimension JSON schema derived from standard VC due-diligence checklists and grounding generation in live web search via the Gemini API.
We evaluated three configurations against 50 startup concepts spanning ten sectors, scored by four independent raters on factual accuracy (FA), analytical completeness (AC), and decision quality (DQ) on a 1-5 Likert scale (Cohen's ? = 0.76). A 20-concept held-out subset was also run on OpenAI's GPT-4o.
The full pipeline (C3) achieved FA = 3.9, AC = 4.1, DQ = 3.7--a 41% accuracy gain and 28% completeness gain over the unconstrained baseline. GPT-4o under the same schema matched on completeness (AC = 4.0) but scored lower on accuracy (FA = 3.2), lacking native search grounding.
Paired t-tests confirmed significance for all C1-to-C3 comparisons (p < 0.01). The two mechanisms fix different things: schema enforcement drives completeness; search grounding drives accuracy. Neither alone was enough.
IJCRT's Publication Details
Unique Identification Number - IJCRT2605722
Paper ID - 308931
Author type - Indian Author
Page Number(s) - g359-g372
Pubished in - Volume 14 | Issue 5 | May 2026
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No Of Downloads - 8
Author Country - India, 422012, Nashik, Nashik, 422012, Science and Technology
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Ratna Patil,Chetan Shelar,Saranya Sawant,Rushikesh Sawale,Athrva Shelke, "ValidatorAI: A Schema-Constrained Prompt Framework with Search-Grounded Generation for Automated Startup Viability Assessment", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 5, pp.g359-g372, May 2026, Available at :http://www.ijcrt.org/papers/IJCRT2605722.pdf
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