Constructing an enterprise AI firm on a "basis of shifting sand" is the central problem for founders immediately, in accordance with the management at Palona AI.
As we speak, the Palo Alto-based startup—led by former Google and Meta engineering veterans—is making a decisive vertical push into the restaurant and hospitality area with immediately'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 process 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 the right way to transfer past "skinny wrappers" to construct deep methods that clear up 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 immediately’s LLM ecosystem. “So we constructed an orchestration layer that lets us swap fashions on efficiency, fluency, and value.”
VentureBeat spoke with Howes and co-founder and CEO Maria Zhang in particular person not too long ago at — the place else? — a restaurant in NYC in regards to the technical challenges and laborious classes discovered 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 alerts — similar 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 contains managing catering orders, opening and shutting checklists, and meals prep achievement. By correlating video alerts from Imaginative and prescient with Level-of-Sale (POS) knowledge 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 crew’s first 12 months was a lesson within the necessity of focus.
Initially, Palona served trend and electronics manufacturers, creating "wizard" and "surfer dude" personalities to deal with gross sales. Nevertheless, the crew shortly realized that the restaurant {industry} offered a singular, 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 info pipeline" that processes imaginative and prescient, voice, and textual content in tandem.
That readability of focus opened entry to proprietary coaching knowledge (like prep playbooks and name transcripts) whereas avoiding generic knowledge scraping.
1. Constructing on ‘Shifting Sand’
To accommodate the fact 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 value.
They use a mixture of proprietary and open-source fashions, together with Gemini for pc 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 present 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" colour 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 determine what's happening on this world of eating places".
3. The ‘Muffin’ Answer: Customized Reminiscence Structure
Some of the 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 "common" order.
The crew 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 normal vector-based approaches that battle with structured knowledge, Muffin is architected to deal with 4 distinct layers:
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Structured Knowledge: Steady info like supply addresses or allergy info.
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Gradual-changing Dimensions: Loyalty preferences and favourite objects.
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Transient and Seasonal Reminiscences: Adapting to shifts like preferring chilly drinks in July versus scorching cocoa in winter.
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Regional Context: Defaults like time zones or language preferences.
The lesson for builders: If the very best accessible instrument isn't ok in your particular vertical, you should be prepared to construct your personal.
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 threat. A current incident at Stefanina’s Pizzeria in Missouri, the place an AI hallucinated faux offers throughout a dinner rush, highlights how shortly model belief can evaporate when safeguards are absent.
To forestall such chaos, Palona’s engineers observe its inside GRACE framework:
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Guardrails: Laborious limits on agent conduct to forestall unapproved promotions.
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Purple 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 methods.
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Compliance: Grounding each response in verified, vetted menu knowledge 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 large 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 get rid of 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 methods" that may see, hear, and assume inside a selected 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 "common," and monitoring the restaurant operations to make sure they ship that buyer the meals in accordance with their inside processes and pointers, flagging every time one thing goes incorrect or crucially, is about to go incorrect.
For Zhang, the purpose is to let human operators concentrate on their craft: "When you've received that scrumptious meals nailed… we’ll inform you what to do."
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