Burhan.

The problem

Saudi Arabia is in the middle of one of the largest entrepreneurial expansions in the region — 314K business registrations in 2022 grew to 486K in 2025 at ~15.6% CAGR, driven by Vision 2030. But the infrastructure to evaluate new business ideas hasn't kept pace: feasibility studies are expensive (out of reach for first-time founders), market data is scattered across portals and PDFs, rental price information is fragmented, and most aspiring entrepreneurs lack financial modelling knowledge to estimate startup costs or demand seriously.

The result: many new business decisions are made on gut feel and friend advice, with capital deployed before the basic feasibility questions are answered.

The solution

Burhan replaces the expensive consultant engagement with an AI feasibility agent tuned for the Saudi market. The entrepreneur inputs business idea + location (city + area) + space size + domain + staffing plan; the AI returns a rental cost estimate (grounded in actual Saudi rental data, not invented), startup cost estimate, and a structured feasibility study in JSON format.

The product is bilingual by design — Arabic and English inputs handled natively — and the AI explicitly refuses to guess when data is unavailable (e.g., "Find retail rent in Mars City Saudi Arabia" returns a refusal, not a fabricated number).

How it works

Inputs: Business idea, Space Size (m²), City, Area, Business Tag (e.g. "coffee_shop"), Number of Staff. Output: structured JSON with business_type, city, area, average rental price, startup capital estimate, feasibility assessment.

System prompt: "You are a real estate consultant. Avoid guessing price. No emojis. No exclamation. Output format is only JSON structure." Provides good-example outputs as multi-shot examples to anchor structure. Refusal pattern: when data is uncertain, the agent says so rather than inventing — critical for trust in a market where bad data costs founders real money.

Primary model: GPT 5.4, chosen via evaluation against GPT 5.4-mini, GPT 5.1, GPT 4.1, and GPT 4.1-mini. Tested cases: Happy path ("coffee shop in Olaya Riyadh ~50m²"), Arabic input ("retail in Olaya 80m²"), Edge case (small district with limited listings), Negative case (nonexistent location).

Who it's for

Primary buyers: aspiring entrepreneurs and small business founders in Saudi Arabia who have a business idea and want to know if it's worth pursuing — typically first-timers without the budget or expertise for a paid feasibility consultant.

Secondary: investors evaluating opportunities, franchise buyers comparing locations, and SME consultants who need fast feasibility outputs as part of their own client engagements. The product targets the long tail of the Vision 2030 entrepreneurial wave: many ideas, each needing structured analysis, none individually large enough to justify a traditional consulting engagement.

Why it matters

Vision 2030 is structurally reshaping the Saudi economy through entrepreneurial expansion — but the support infrastructure for first-time founders hasn't scaled with the volume. Reliable market data is restricted, traditional feasibility studies are expensive and slow, and the gap between "I have an idea" and "I know whether it will work" is exactly where most new ventures die before they start.

Burhan's structural bet is on AI-automated feasibility analysis as the wedge into Saudi SME support infrastructure. By doing one thing well (Saudi rental + feasibility + startup cost estimation, in Arabic, with refusal discipline), the product can become the default first-step tool for the 100K+ new business registrations happening annually, with downstream expansion paths into vendor recommendations, market demand analysis, and franchise comparison.

At a glance

Project

Burhan

Built by

Sultan Almassari

One-liner

AI feasibility study generator for Saudi entrepreneurs — rental estimates, startup costs, and market viability in Arabic and English, built for Vision 2030.