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 at the moment's LLMs akin to ChatGPT and Gemini, has raised $20 million in a bridge SAFE spherical at a $750 million valuation cap, bringing its whole funding to just about $60 million, VentureBeat can solely reveal.
The spherical, accomplished in below every week, comes amid heightened curiosity in deterministic conversational AI and precedes a bigger elevate now in superior levels.
AUI depends on a fusion of the transformer tech and a more recent know-how referred to as "neuro-symbolic AI," described in higher element beneath.
"We realize that you can combine the brilliance of LLMs in linguistic capabilities with the guarantees of symbolic AI," stated 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, current shareholders, and different strategic traders. It follows a $10 million elevate in September 2024 at a $350 million valuation cap, coinciding with the corporate’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 line with the corporate, the bridge spherical is a precursor to a considerably bigger elevate already in superior levels.
AUI is the corporate behind Apollo-1, a brand new basis mannequin constructed for task-oriented dialog, which it describes because the "economic half" of conversational AI — distinct from the open-ended dialog dealt with by LLMs like ChatGPT and Gemini.
The agency argues that current 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, stated in a press launch offered to VentureBeat: “I’ve seen some of today’s top AI leaders walk 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 job reasoning. As a substitute of utilizing the most typical know-how underpinning most LLMs and conversational AI programs at the moment — the vaunted transformer structure described within the seminal 2017 Google paper "Attention Is All You Need" — AUI's system integrates two layers:
Neural modules, powered by LLMs, deal with notion: encoding person inputs and producing pure language responses.
A symbolic reasoning engine, developed over a number of years, interprets structured job components akin to intents, entities, and parameters. This symbolic state engine determines the suitable subsequent actions utilizing deterministic logic.
This hybrid structure permits Apollo-1 to take care of state continuity, implement organizational insurance policies, and reliably set off device or API calls — capabilities that transformer-only brokers lack.
Elhelo stated this design emerged from a multi-year knowledge assortment effort: “We built a consumer service and recorded millions of human-agent interactions across 60,000 live agents. From that, we abstracted a symbolic language that defines the structure of task-based dialogs, separate from their domain-specific content.”
Nevertheless, enterprises which have already constructed programs constructed round transformer LLMs needn't fear. AUI desires to make adopting its new know-how simply as straightforward.
"Apollo-1 deploys like any modern foundation model," Elhelo instructed VentureBeat in a textual content final night time. "It doesn’t require dedicated or proprietary clusters to run. It operates across standard cloud and hybrid environments, leveraging both GPUs and CPUs, and is significantly more cost-efficient to deploy than frontier reasoning models. Apollo-1 can also be deployed across all major clouds in a separated environment for increased security."
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.
Not like consulting-heavy AI platforms that require constructing bespoke logic per shopper, Apollo-1 permits enterprises to outline behaviors and instruments inside a shared symbolic language. This strategy helps quicker onboarding and reduces long-term upkeep. In line with the crew, an enterprise can launch a working agent in below a day.
Crucially, procedural guidelines are encoded on the symbolic layer — not realized from examples. This permits deterministic execution for delicate or regulated duties.
For example, a system can block cancellation of a Primary Financial 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 searching for certainty. It’s higher if what you’re going to ship [to an AI model] and at all times ship it, and , 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 lively use inside Fortune 500 enterprises in a closed beta, and a broader normal availability launch is anticipated earlier than the tip of 2025, in line with a earlier report by The Data, which broke the preliminary information on the startup.
Enterprises can combine with Apollo-1 both through:
A developer playground, the place enterprise customers and technical groups collectively configure insurance policies, guidelines, and behaviors; or
A normal API, utilizing OpenAI-compatible codecs.
The mannequin helps coverage enforcement, rule-based customization, and steering through guardrails. Symbolic guidelines enable 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 job completion are first-class design targets.
Elhelo places it plainly: “If your use case is task-oriented dialog, you have to use us, even if you are ChatGPT.”
