Restaurant technology interview: how Digitory’s Shivprakash Mogali is architecting the AI operating system for hospitality
A restaurant runs on a POS, an inventory tool, a reservations platform and a recipe spreadsheet — none of which share data. In this restaurant technology interview, Digitory’s founder explains what it takes to collapse those layers into one operating system, where AI does real work, and what breaks when the platform scales across brands and borders.
An AI-powered Restaurant Operating System
Hospitality runs on software that was never designed to work together. A typical restaurant operates a point-of-sale system, a separate inventory tool, a reservations platform and a spreadsheet for recipes — none of which share data, and all of which the operator reconciles by hand. The cost shows up in margin: stock that vanishes, recipes that cannot be costed accurately, and decisions made on guesswork rather than numbers.
Digitory’s answer is to collapse those layers into a single system. It describes itself as an AI-powered Restaurant Operating System — a platform that unifies operations, inventory, recipes, CRM and analytics, with intelligence applied across the data rather than bolted onto one tool. The question this restaurant technology interview pursues is a technical one: what does it actually take to build an operating system for a business that runs on the floor, in real time, with perishable stock and high staff turnover?
Digitory is led by founder Shivprakash S. Mogali, whose background spans two decades across technology, media and digital transformation. The company has moved quickly in India’s hospitality sector and has begun expanding internationally. Readers will come away with a clear view of how a restaurant OS is architected, where AI does genuine work versus where it is decoration, and what breaks when you try to scale that platform across brands and borders.
Digitory — at a glance
– Category: AI-powered Restaurant Operating System (vertical SaaS for hospitality)
– Platform scope: operations, real-time analytics, inventory intelligence, recipe management, CRM, business automation
– Recent engineering focus: AI-powered CRM, barcode-enabled inventory management, cashless event solutions, business intelligence
– Customers: restaurants, pubs, breweries, cafés and multi-location brands — including Bier Library, Bier Garten, 1522 – The Pub, Effingut, Toit and Byg Brewski Brewing Company
– Markets: India, with operations in Thailand and plans for the UAE
– Recognition: K-Tech Elevate Award for innovation in restaurant technology
– Positioning: a full-suite platform rather than a point tool; the stated design goal is “invisible” technology
OS versus tool
Q1. You call Digitory a Restaurant Operating System rather than a tool. Architecturally, what makes it an OS — which layer does everything else hang off, and what does that layer own?
SSM: At the core of Digitory is the item and recipe master. Every dish on the menu is linked to a recipe, each recipe to its raw ingredients, and every ingredient to stock, vendors and the last purchase price. So, when a butter chicken is billed, the system knows what ingredients should have been consumed and what that dish cost the restaurant that day.
The POS, KDS, QR ordering, CRM, central kitchen and AI capabilities all sit on this common foundation. That’s what makes Digitory an Operating System rather than just another tool. Most restaurants use six or seven disconnected applications and rely on Excel to reconcile data every night. We bring that operational data together in one place.
Q2. How does the platform sit alongside a restaurant’s existing POS and payments stack? Is it replacing them or integrating, and how do you handle that technically without forcing a rip-and-replace?
SSM: It depends on the part of the technology stack. We typically replace the existing POS because billing is where the cleanest operational data originates. Building intelligence on top of another billing system can create inconsistencies. However, we don’t force a complete transition on day one. Many clients start with inventory and recipes before moving their billing to Digitory once their teams are comfortable with the platform.
For the rest of the ecosystem, we integrate with payment gateways, Swiggy, Zomato, Tally, printers and existing hardware. Restaurants don’t need to discard systems that are already working well. Our focus is on bringing the relevant data into one connected platform.
AI that does real work
Q3. Digitory’s intelligence spans inventory, recipes and CRM. Where does the AI genuinely do work a rules engine could not — and where is it deliberately not used?
SSM: Not everything needs AI. If stock falls below a predefined level, the system can raise an indent. That’s a straightforward rule, and we keep it that way.
AI adds value when data is messy and the system needs to identify patterns or make informed estimates. For example, it can estimate preparation requirements for a Saturday before Diwali, detect unusual variance in whisky consumption at a particular bar, or identify an ingredient from an abbreviated vendor invoice.
However, we deliberately keep AI away from operations where precision is essential. Billing, taxes, settlements and stock closing follow fixed logic. AI can flag anomalies, but it doesn’t independently change financial or inventory figures.
Restaurant Data is Messy
Q4. Restaurant data is messy: inconsistent recipe units, stock variance, staff-entered entries. What is the hardest data problem you had to solve to make the intelligence reliable on top of that?
SSM: The biggest challenge has been inconsistent units and poor master data.
Paneer may be purchased in kilograms, used in recipes in grams and issued from stores in packets. Similarly, the same ingredient may appear under different names, creating duplicate stock records. Incorrect recipe mapping can also lead to the wrong ingredients being deducted when a dish is sold.
To address this, we’ve built an ingredient master containing more than 4,000 items, each with a base unit and defined conversions. Local ingredient names are mapped to this master, and duplicates are consolidated before they spread across the system.
We also recognise that staff entries will never be perfect. Instead of relying entirely on accurate data entry, the system compares actual entries with expected consumption based on sales and flags discrepancies. This approach has proved more reliable than expecting every entry to be error-free.
Inventory and the physical world
Q5. Barcode-enabled inventory is on your roadmap. What is technically hard about tracking perishable, unit-inconsistent stock in real time, and how close is the system to a trustworthy number?
SSM: Packaged products are relatively easy to track because a barcode identifies a standard quantity. Fresh ingredients are more complicated. Chicken loses weight during cleaning, vegetables are trimmed, and many ingredients arrive from local vendors without barcodes.
That’s why barcodes alone aren’t enough. We’re building an approach that combines scanning during stock receipt and issue, yield factors for individual ingredients, and expected consumption based on sales.
When these three indicators align, restaurants can have greater confidence in their stock figures. When they don’t, the variance itself becomes useful in identifying potential wastage or leakage.
We’re close to this level of reliability for bar and packaged inventory. For fresh produce, a dependable daily stock figure is a more realistic near-term goal than promising complete real-time accuracy.
Reliability under fire
Q6. A system that goes down at dinner service is unusable. How do you design for reliability, latency and offline tolerance when the floor cannot wait?
SSM: Our first principle is that billing must continue even if the internet goes down. The POS operates locally at the outlet and synchronises with the cloud, rather than depending on a cloud connection for every order. Bills and kitchen order tickets can continue offline, with data synchronising when connectivity returns.
We’re also working on cloud failover for the local server, so restaurants can switch over if their outlet machine fails. On the cloud infrastructure side, we use AWS and Google Cloud.
We also separate operational screens from reporting and analytics. This ensures that a manager generating a sales report doesn’t slow down a captain entering an order during a busy service.
Multi-location data models
Q7. Multi-location brands need standardisation across outlets that genuinely differ. How does the data model absorb per-location variation without fragmenting?
SSM: Multi-location brands need consistent reporting, but their outlets have different prices, vendors, local specials and operating requirements.
We maintain a central master at the headquarters while allowing outlets to override specific attributes, such as pricing, availability or vendors. The underlying dish and ingredient structure remain consistent.
This means butter chicken sold in Bengaluru and Hyderabad can still appear as one item in consolidated brand-level reports. The key is to allow controlled flexibility without letting every outlet create separate versions of the same menu item. Otherwise, standardisation and meaningful comparisons become difficult.
Localisation engineering
Q8. You have begun operating in Thailand and are planning the UAE. What is technically involved in localising — tax, payments, units, language, compliance — versus simply translating the interface?
SSM: Translating the interface is the easy part. The real complexity lies in adapting the platform to local tax regulations, payment systems and compliance requirements.
India has GST, including its CGST and SGST components. Thailand has VAT and its own service-charge practices, while the UAE has VAT and specific invoice requirements. Tax rates, invoice formats and rounding rules must be configured for each market.
Payments also require local integrations, alongside support for different currencies, date formats, vendor units and applicable data and e-invoicing requirements.
Our experience has taught us that embedding India-specific rules into the core product makes international expansion unnecessarily complicated. We now aim to keep country-specific requirements configurable while maintaining the same underlying platform across markets.
What comes next
Q9. Which technology shift do you expect to change restaurant operations most in the next 24 months, and how is Digitory positioned for it?
SSM: I believe restaurant operations will increasingly move from software that teams must manage themselves to services that deliver operational outcomes. Inventory, procurement and accounts are clear examples.
The challenge has never been just the software. Restaurants also need trained people to configure recipes, clean item lists, map vendors, manage purchase orders, close stock and reconcile accounts. Many outlets lack these resources, which means even good software often remains underutilised.
We’ve been addressing this by building our own inventory knowledge base and standard library of ingredients, units and conversions. Our AI uses this foundation to accelerate client onboarding, clean and map data, and identify duplicates and incorrect mappings.
We now offer inventory, procurement and accounts as a service. Restaurant teams handle day-to-day activities such as receiving stock, issuing ingredients and recording wastage, while our team uses AI to manage the underlying processes. The goal is to give owners reliable food-cost, inventory and financial numbers without requiring them to become experts in the systems behind them.
If You Were Starting Today
Q10. What is the one technical decision you would make differently if you were starting Digitory today?
SSM: I would have designed our inventory module for scale much earlier.
When we started in 2017, we built our own POS alongside inventory, forecasting and procurement. The initial inventory module worked well for individual restaurants and smaller groups. However, as we expanded to multi-outlet chains, central kitchens and bars managing hundreds of SKUs, we had to keep adapting a system that wasn’t originally designed for that level of complexity.
That’s why we rebuilt the module from the ground up as Inventory 2.0. It is built around our ingredient master and knowledge base, with AI supporting onboarding, data cleansing and mapping. It is designed to scale across outlets while maintaining consistent inventory data.
Inventory has quickly become one of the key reasons clients choose Digitory. While much of the restaurant software industry treats inventory as an extension of billing, we see it as the core of restaurant operations, with the rest of the platform building on it.
If I were starting today, I would build inventory for multi-location scale from day one. It would have helped us reach this stage much earlier.
TechRecast Analysis (The Recast)
The Shift in Context
Vertical SaaS has been steadily consolidating fragmented, spreadsheet-driven industries, and hospitality is one of the last big ones. The pitch is familiar — replace a dozen tools with one system of record — but the engineering is not. A restaurant OS has to be real-time, tolerant of flaky connectivity, and accurate about physical stock. This restaurant technology interview tests how real that consolidation is, technically.
Why This Interview Matters
Hospitality technology is crowded with point solutions and thin on genuine operating systems. Digitory’s claim — a full Restaurant OS with AI built in — is worth interrogating on the architecture, not the marketing, especially as it expands from India into Southeast Asia and the Gulf.
Technology Perspective
The questions that matter are integration and data integrity. Does one platform truly unify POS, inventory, recipes, CRM and analytics, or stitch them at the edges? And can AI produce trustworthy numbers on top of messy, human-entered, unit-inconsistent operational data? Barcode-enabled inventory and the AI CRM are the clearest tests of whether the intelligence is native or layered on.
Business and Market Perspective
The commercial logic is vertical depth: selling to pubs, breweries and cafés means encoding a specific operating model, not a generic one. Multi-brand partnerships suggest early credibility; the open question is retention and expansion revenue as brands add locations.
Leadership Perspective
Mogali’s two decades across technology, media and digital transformation is a founder profile built on identifying market gaps and shipping products to fill them. The relevant lens is whether that pattern holds in a category as operationally specific as hospitality.
The Counterpoint
A fair sceptic would argue that operators resist new software, churn is high, and a full-suite OS is harder to sell than a cheap point tool — especially to independents. On this view, the winners in restaurant tech are the payment and POS incumbents who own the transaction, and the platform layer gets absorbed. This is the strongest opposing case and deserves a direct answer.
What Comes Next
Watch three signals: whether the integration layer genuinely replaces the tool stack rather than wrapping it, whether AI features move from dashboards to decisions operators act on, and whether localisation holds the data model together as the platform crosses borders.
Industry Outlook
If Digitory is right, hospitality consolidates onto operating systems and the point-tool market shrinks. If it is wrong, restaurant tech stays fragmented and the platform layer is absorbed by whoever owns payments and POS.
Key Takeaways
1. Hospitality’s real problem is an integration and data-integrity problem, not a missing-feature problem.
2. An “OS” claim is only credible if the core layer genuinely owns the data that other functions depend on.
3. AI in restaurants has to survive messy, human-entered operational data — that is the hard engineering.
4. Real-time reliability and offline tolerance are non-negotiable in a business that cannot pause service.
5. Multi-location standardisation is a data-model problem before it is a feature problem.
6. The strongest counter-argument is that POS and payment incumbents own the transaction and will absorb the platform layer.