The buzzed-about however nonetheless stealthy New York Metropolis startup Augmented Intelligence Inc (AUI), which seeks to transcend the favored "transformer" structure utilized by most of right now's LLMs resembling ChatGPT and Gemini, has raised $20 million in a bridge SAFE spherical at a $750 million valuation cap, bringing its whole funding to almost $60 million, VentureBeat can completely reveal.
The spherical, accomplished in underneath per week, comes amid heightened curiosity in deterministic conversational AI and precedes a bigger elevate now in superior phases.
AUI depends on a fusion of the transformer tech and a more recent expertise referred to as "neuro-symbolic AI," described in higher element under.
"We understand you could mix the brilliance of LLMs in linguistic capabilities with the ensures of symbolic AI," mentioned Ohad Elhelo, AUI co-founder and CEO in a latest interview with VentureBeat. Elhelo launched the corporate in 2017 alongside co-founder and Chief Product Officer Ori Cohen.
The brand new financing contains participation from eGateway Ventures, New Period Capital Companions, present shareholders, and different strategic traders. It follows a $10 million elevate in September 2024 at a $350 million valuation cap, coinciding with the firm’s introduced go-to-market partnership with Google in October 2024. Early traders embrace Vertex Prescription drugs founder Joshua Boger, UKG Chairman Aron Ain, and former IBM President Jim Whitehurst.
In response to the corporate, the bridge spherical is a precursor to a considerably bigger elevate already in superior phases.
AUI is the firm behind Apollo-1, a brand new basis mannequin constructed for task-oriented dialog, which it describes because the "financial half" of conversational AI — distinct from the open-ended dialog dealt with by LLMs like ChatGPT and Gemini.
The agency argues that present LLMs lack the determinism, coverage enforcement, and operational certainty required by enterprises, particularly in regulated sectors.
Chris Varelas, co-founder of Redwood Capital and an advisor to AUI, mentioned in a press launch supplied to VentureBeat: “I’ve seen a few of right now’s prime AI leaders stroll away with their heads spinning after interacting with Apollo-1.”
A Distinctive Neuro-Symbolic Structure
Apollo-1’s core innovation is its neuro-symbolic structure, which separates linguistic fluency from process reasoning. As a substitute of utilizing the commonest expertise underpinning most LLMs and conversational AI techniques right now — the vaunted transformer structure described within the seminal 2017 Google paper "Consideration Is All You Want" — AUI's system integrates two layers:
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Neural modules, powered by LLMs, deal with notion: encoding person inputs and producing pure language responses.
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A symbolic reasoning engine, developed over a number of years, interprets structured process components resembling intents, entities, and parameters. This symbolic state engine determines the suitable subsequent actions utilizing deterministic logic.
This hybrid structure permits Apollo-1 to keep up state continuity, implement organizational insurance policies, and reliably set off software or API calls — capabilities that transformer-only brokers lack.
Elhelo mentioned this design emerged from a multi-year information assortment effort: “We constructed a shopper service and recorded thousands and thousands of human-agent interactions throughout 60,000 dwell brokers. From that, we abstracted a symbolic language that defines the construction of task-based dialogs, separate from their domain-specific content material.”
Nevertheless, enterprises which have already constructed techniques constructed round transformer LLMs needn't fear. AUI needs to make adopting its new expertise simply as simple.
"Apollo-1 deploys like every fashionable basis mannequin," Elhelo informed VentureBeat in a textual content final evening. "It doesn’t require devoted or proprietary clusters to run. It operates throughout commonplace cloud and hybrid environments, leveraging each GPUs and CPUs, and is considerably extra cost-efficient to deploy than frontier reasoning fashions. Apollo-1 may also be deployed throughout all main clouds in a separated surroundings for elevated safety."
Generalization and Area Flexibility
Apollo-1 is described as a basis mannequin for task-oriented dialog, that means it’s domain-agnostic and generalizable throughout verticals like healthcare, journey, insurance coverage, and retail.
In contrast to consulting-heavy AI platforms that require constructing bespoke logic per consumer, Apollo-1 permits enterprises to outline behaviors and instruments inside a shared symbolic language. This strategy helps sooner onboarding and reduces long-term upkeep. In response to the workforce, an enterprise can launch a working agent in underneath a day.
Crucially, procedural guidelines are encoded on the symbolic layer — not discovered from examples. This allows deterministic execution for delicate or regulated duties.
For example, a system can block cancellation of a Primary Economic system flight not by guessing intent however by making use of hard-coded logic to a symbolic illustration of the reserving class.
As Elhelo defined to VentureBeat, LLMs are "not a great mechanism while you’re in search of certainty. It’s higher if you realize what you’re going to ship [to an AI model] and at all times ship it, and you realize, at all times, what’s going to return again [to the user] and tips on how to deal with that.”
Availability and Developer Entry
Apollo-1 is already in energetic use inside Fortune 500 enterprises in a closed beta, and a broader common availability launch is anticipated earlier than the top of 2025, in response to a earlier report by The Data, which broke the preliminary information on the startup.
Enterprises can combine with Apollo-1 both by way of:
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A developer playground, the place enterprise customers and technical groups collectively configure insurance policies, guidelines, and behaviors; or
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An ordinary API, utilizing OpenAI-compatible codecs.
The mannequin helps coverage enforcement, rule-based customization, and steering by way of guardrails. Symbolic guidelines permit companies to dictate fastened behaviors, whereas LLM modules deal with open-text interpretation and person interplay.
Enterprise Match: When Reliability Beats Fluency
Whereas LLMs have superior general-purpose dialog and creativity, they continue to be probabilistic — a barrier to enterprise deployment in finance, healthcare, and customer support.
Apollo-1 targets this hole by providing a system the place coverage adherence and deterministic process completion are first-class design targets.
Elhelo places it plainly: “In case your use case is task-oriented dialog, you must use us, even in case you are ChatGPT.”
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