Constructing an enterprise AI firm on a "basis of shifting sand" is the central problem for founders at the moment, in line with the management at Palona AI.
At this time, the Palo Alto-based startup—led by former Google and Meta engineering veterans—is making a decisive vertical push into the restaurant and hospitality house with at the moment's launch of Palona Imaginative and prescient and Palona Workflow.
The brand new choices remodel the corporate’s multimodal agent suite right into a real-time working system for restaurant operations — spanning cameras, calls, conversations, and coordinated job execution.
The information marks a strategic pivot from the corporate’s debut in early 2025, when it first emerged with $10 million in seed funding to construct emotionally clever gross sales brokers for broad direct-to-consumer enterprises.
Now, by narrowing its focus to a "multimodal native" strategy for eating places, Palona is offering a blueprint for AI builders on how you can transfer past "skinny wrappers" to construct deep techniques that resolve high-stakes bodily world issues.
“You’re constructing an organization on prime of a basis that’s sand—not quicksand, however shifting sand,” mentioned co-founder and CTO Tim Howes, referring to the instability of at the moment’s LLM ecosystem. “So we constructed an orchestration layer that lets us swap fashions on efficiency, fluency, and price.”
VentureBeat spoke with Howes and co-founder and CEO Maria Zhang in individual just lately at — the place else? — a restaurant in NYC concerning the technical challenges and arduous classes realized from their launch, progress, and pivot.
The New Providing: Imaginative and prescient and Workflow as a ‘Digital GM’
For the tip person—the restaurant proprietor or operator—Palona’s newest launch is designed to perform as an automatic "finest operations supervisor" that by no means sleeps.
Palona Imaginative and prescient makes use of in-store safety cameras to investigate operational indicators — comparable to queue lengths, desk turnover, prep bottlenecks, and cleanliness — with out requiring any new {hardware}.
It displays front-of-house metrics like queue lengths, desk turns, and cleanliness, whereas concurrently figuring out back-of-house points like prep slowdowns or station setup errors.
Palona Workflow enhances this by automating multi-step operational processes. This consists of managing catering orders, opening and shutting checklists, and meals prep success. By correlating video indicators from Imaginative and prescient with Level-of-Sale (POS) information and staffing ranges, Workflow ensures constant execution throughout a number of areas.
“Palona Imaginative and prescient is like giving each location a digital GM,” mentioned Shaz Khan, founding father of Tono Pizzeria + Cheesesteaks, in a press launch offered to VentureBeat. “It flags points earlier than they escalate and saves me hours each week.”
Going Vertical: Classes in Area Experience
Palona’s journey started with a star-studded roster. CEO Zhang beforehand served as VP of Engineering at Google and CTO of Tinder, whereas Co-founder Howes is the co-inventor of LDAP and a former Netscape CTO.
Regardless of this pedigree, the workforce’s first yr was a lesson within the necessity of focus.
Initially, Palona served style and electronics manufacturers, creating "wizard" and "surfer dude" personalities to deal with gross sales. Nonetheless, the workforce rapidly realized that the restaurant {industry} introduced a novel, trillion-dollar alternative that was "surprisingly recession-proof" however "gobsmacked" by operational inefficiency.
"Recommendation to startup founders: don't go multi-industry," Zhang warned.
By verticalizing, Palona moved from being a "skinny" chat layer to constructing a "multi-sensory data pipeline" that processes imaginative and prescient, voice, and textual content in tandem.
That readability of focus opened entry to proprietary coaching information (like prep playbooks and name transcripts) whereas avoiding generic information scraping.
1. Constructing on ‘Shifting Sand’
To accommodate the truth of enterprise AI deployments in 2025 — with new, improved fashions popping out on a virtually weekly foundation — Palona developed a patent-pending orchestration layer.
Quite than being "bundled" with a single supplier like OpenAI or Google, Palona’s structure permits them to swap fashions on a dime based mostly on efficiency and price.
They use a mixture of proprietary and open-source fashions, together with Gemini for laptop imaginative and prescient benchmarks and particular language fashions for Spanish or Chinese language fluency.
For builders, the message is evident: By no means let your product's core worth be a single-vendor dependency.
2. From Phrases to ‘World Fashions’
The launch of Palona Imaginative and prescient represents a shift from understanding phrases to understanding the bodily actuality of a kitchen.
Whereas many builders battle to sew separate APIs collectively, Palona’s new imaginative and prescient mannequin transforms current in-store cameras into operational assistants.
The system identifies "trigger and impact" in real-time—recognizing if a pizza is undercooked by its "pale beige" coloration or alerting a supervisor if a show case is empty.
"In phrases, physics don't matter," Zhang defined. "However in actuality, I drop the cellphone, it all the time goes down… we wish to actually work out what's happening on this world of eating places".
3. The ‘Muffin’ Resolution: Customized Reminiscence Structure
Probably the most vital technical hurdles Palona confronted was reminiscence administration. In a restaurant context, reminiscence is the distinction between a irritating interplay and a "magical" one the place the agent remembers a diner’s "regular" order.
The workforce initially utilized an unspecified open-source instrument, however discovered it produced errors 30% of the time. "I feel advisory builders all the time flip off reminiscence [on consumer AI products], as a result of that can assure to mess all the things up," Zhang cautioned.
To unravel this, Palona constructed Muffin, a proprietary reminiscence administration system named as a nod to net "cookies". In contrast to customary vector-based approaches that battle with structured information, Muffin is architected to deal with 4 distinct layers:
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Structured Information: Secure details like supply addresses or allergy data.
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Gradual-changing Dimensions: Loyalty preferences and favourite objects.
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Transient and Seasonal Recollections: Adapting to shifts like preferring chilly drinks in July versus sizzling cocoa in winter.
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Regional Context: Defaults like time zones or language preferences.
The lesson for builders: If the perfect obtainable instrument isn't ok on your particular vertical, you should be prepared to construct your individual.
4. Reliability by means of ‘GRACE’
In a kitchen, an AI error isn't only a typo; it’s a wasted order or a security danger. A current incident at Stefanina’s Pizzeria in Missouri, the place an AI hallucinated faux offers throughout a dinner rush, highlights how rapidly model belief can evaporate when safeguards are absent.
To forestall such chaos, Palona’s engineers comply with its inner GRACE framework:
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Guardrails: Laborious limits on agent habits to forestall unapproved promotions.
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Crimson Teaming: Proactive makes an attempt to "break" the AI and determine potential hallucination triggers.
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App Sec: Lock down APIs and third-party integrations with TLS, tokenization, and assault prevention techniques.
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Compliance: Grounding each response in verified, vetted menu information to make sure accuracy.
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Escalation: Routing advanced interactions to a human supervisor earlier than a visitor receives misinformation.
This reliability is verified by means of huge simulation. "We simulated 1,000,000 methods to order pizza," Zhang mentioned, utilizing one AI to behave as a buyer and one other to take the order, measuring accuracy to remove hallucinations.
The Backside Line
With the launch of Imaginative and prescient and Workflow, Palona is betting that the way forward for enterprise AI isn't in broad assistants, however in specialised "working techniques" that may see, hear, and assume inside a particular area.
In distinction to general-purpose AI brokers, Palona’s system is designed to execute restaurant workflows, not simply reply to queries — it's able to remembering clients, listening to them order their "regular," and monitoring the restaurant operations to make sure they ship that buyer the meals in line with their inner processes and pointers, flagging every time one thing goes flawed or crucially, is about to go flawed.
For Zhang, the objective is to let human operators deal with their craft: "In case you've obtained that scrumptious meals nailed… we’ll inform you what to do."
