Sophara detects royalty anomalies, benchmarks unit economics, and automates compliance auditing across every location — before problems surface in a quarterly review.
Plug into your existing POS, ERP, LMS, and reporting systems. No rip-and-replace. Works alongside FranConnect, Toast, QuickBooks, and 12+ others.
Sophara builds a 12-month rolling model per location — accounting for seasonality, daypart, geography, and vintage. Your benchmark is your network, not a generic average.
Weekly briefing. Real-time alerts. Quinn, our AI assistant, tells you which locations need attention — and why — before it becomes a quarterly problem.
Connects to POS data and reported royalty figures. Flags statistically significant deviations by location — with severity scoring and auto-audit queuing.
AUV, food cost %, labor %, comps growth — ranked against your network, anonymized. Every operator sees where they stand and what the top 10% achieve.
Replace clipboards and 3-hour write-ups. Mobile-first audit flows with auto-generated reports, timestamped evidence, and risk flagging per location.
Aggregates LMS completion data across platforms and correlates it with ops performance. Predicts at-risk locations before compliance failures happen.
*Based on Invotex royalty audit data: 86% of franchisees underreport; conservative recovery modeled at 1.5% of reported sales.
I'm Ronak — a software developer from Mumbai, and the founder of Sophara. I studied Computer Science Engineering at BITS Pilani — Goa Campus, and spent the years after building software across the B2B and SaaS space.
Sophara started from a straightforward observation: franchising is one of India's fastest-growing business models, and the operational intelligence layer is almost nonexistent. Brands with 100–500 locations are running compliance on clipboards and tracking royalties via PDF reports compared in Excel. The data exists. The tools to use it didn't — until now.
I started building Sophara about four months ago. What began as an exploration of the compliance audit problem quickly became clear as a larger opportunity: anomaly detection on royalty data, unit economics benchmarking, training intelligence, and an AI assistant that ties it all together.
We now have three live customers, a team of five, and active conversations with over five more brands. This is early — and that's deliberate. I'd rather be deeply embedded with a small number of customers and build the right thing than scale something half-formed.
"The category isn't unserved. It's underserved by software built before AI was real. FranConnect was founded in 2000. We weren't. That's our advantage."— Ronak Dhawan, Founder, Sophara
B.E. in Computer Science Engineering. Started immediately in software development, focused on backend systems and data infrastructure.
Built across multiple B2B and SaaS contexts. Deep familiarity with enterprise data pipelines, multi-tenant architecture, and API integration at scale. Began working with AI platform APIs in 2023.
Built the anomaly detection layer first — POS connectors, statistical baseline models, severity scoring. First customer conversations began in month two.
First paying customer onboarded — a QSR brand with 40+ locations across Maharashtra. First royalty flags surfaced in week two.
Brought on Priyanshu (marketing), Om (sales), Rakesh (operations), and Rohit (technical). Actively demoing with five more brands.
Target: 25 live customers across QSR, fitness, and personal services verticals by end of 2026. Open to conversations with operators, angels, and franchise-focused investors.
If you're a franchise operator, a VP of Operations, or someone who works with multi-unit brands — I'd genuinely like to talk. Not to sell you something. To understand your actual problem.
We'll walk you through a live network analysis using your own data — and show you what's likely being missed today.
No commitment. Response within 24 hours. Based in Mumbai, serving brands across India.
Five integrated modules. One AI assistant. All the data your operators generate — finally working for you.
Most royalty underreporting is never caught — because brands audit 10–15% of locations per year, manually. Sophara monitors every location, every week, against a statistically-derived baseline that accounts for seasonality, footprint, and market.
| Location | Reported | Expected | Severity |
|---|---|---|---|
| #47 Andheri W | ₹1.2L | ₹2.8L | 9 / 10 |
| #83 Powai | ₹1.9L | ₹2.6L | 6 / 10 |
| #29 Dadar | ₹2.1L | ₹2.7L | 5 / 10 |
| #11 Thane | ₹3.2L | ₹3.1L | 1 / 10 |
| #67 Malad | ₹2.5L | ₹2.4L | 1 / 10 |
Every operator in your network is flying partially blind — they know their own numbers but not how they compare. Sophara gives franchisors the network-level view and gives franchisees the peer benchmark they need to improve.
Field ops teams spend the majority of their time on manual checklists, travel, and write-ups. Sophara compresses a 3-hour audit + 1-hour report into a 45-minute mobile-first flow with the report generated on exit.
| Location | Score | Trend | Status |
|---|---|---|---|
| #14 Juhu | 97 | ↑ +3 | Pass |
| #33 Worli | 91 | ↑ +1 | Pass |
| #47 Andheri | 74 | ↓ -8 | Review |
| #83 Powai | 61 | ↓ -12 | Escalate |
| #29 Dadar | 88 | → 0 | Pass |
Training completion rates are a leading indicator for compliance failures — but most brands track completion in one system and operations in another. Sophara connects them.
Ask your network anything in plain language. Quinn replaces the ad-hoc data requests, the Excel pulls, and the "can you just send me a summary" emails — and proactively surfaces what you'd miss if you didn't know to ask.
+ CSV upload fallback for any system not listed above.
All plans include the full platform. Priced by location count, billed annually. No per-module fees.
No. All five modules — anomaly detection, benchmarking, compliance auditing, training intelligence, and Quinn — are included in every plan. Location count is the only variable.
We'll find the right fit. If you're at 65 locations growing to 120, we'll structure a contract that makes sense and doesn't penalise your growth mid-year.
For Starter and Growth plans: 3–4 weeks to full data connection and baseline model. Scale and Enterprise: 6–8 weeks for full custom configuration. You'll see first anomaly flags in week 2.
Yes. Multi-tenant PostgreSQL with row-level security. Your network's data is completely isolated. Benchmarking is done on anonymised aggregates — no raw competitor data is ever exposed.
Currently focused on India-based franchise networks. We have the architecture to support multi-currency and multi-country — reach out if this is a requirement.
AWS-hosted (Mumbai region), encrypted at rest and in transit, SOC 2 Type II audit underway. We take security seriously — happy to share our security documentation on request.
We started Sophara because the franchise industry runs on data it never actually uses. That felt like a problem worth fixing.
"Franchisors with 100 locations should have the same operational intelligence as a Fortune 500 supply chain team. They generate the data. We make it work for them."
The franchise industry is worth over ₹7 lakh crore in India and growing. But the operational layer hasn't meaningfully changed since the 1990s. Franchisors use spreadsheets and phone calls for compliance. Royalty audits happen once a year, manually, for 10–15% of locations. Training completion is tracked in email threads.
POS systems now have open APIs. LLMs make natural-language data querying cheap and fast. Cloud infrastructure means we can build multi-tenant, enterprise-grade software without enterprise-grade capex. The incumbents (FranConnect, BrandWide) were built before AI was a real product capability. We weren't.