The buzzed-about but still stealthy New York City startup Augmented Intelligence Inc (AUI), which seeks to go beyond the popular "transformer" architecture used by most of today's LLMs such as ChatGPT and Gemini, has raised $20 million in a bridge SAFE round at a $750 million valuation cap, bringing its total funding to nearly $60 million, VentureBeat can exclusively reveal.
The round, completed in under a week, comes amid heightened interest in deterministic conversational AI and precedes a larger raise now in advanced stages.
AUI relies on a fusion of the transformer tech and a newer technology called "neuro-symbolic AI," described in greater detail below.
"We realize that you can combine the brilliance of LLMs in linguistic capabilities with the guarantees of symbolic AI," said Ohad Elhelo, AUI co-founder and CEO in a recent interview with VentureBeat. Elhelo launched the company in 2017 alongside co-founder and Chief Product Officer Ori Cohen.
The new financing includes participation from eGateway Ventures, New Era Capital Partners, existing shareholders, and other strategic investors. It follows a $10 million raise in September 2024 at a $350 million valuation cap, coinciding with the company’s announced go-to-market partnership with Google in October 2024. Early investors include Vertex Pharmaceuticals founder Joshua Boger, UKG Chairman Aron Ain, and former IBM President Jim Whitehurst.
According to the company, the bridge round is a precursor to a significantly larger raise already in advanced stages.
AUI is the company behind Apollo-1, a new foundation model built for task-oriented dialog, which it describes as the "economic half" of conversational AI — distinct from the open-ended dialog handled by LLMs like ChatGPT and Gemini.
The firm argues that existing LLMs lack the determinism, policy enforcement, and operational certainty required by enterprises, especially in regulated sectors.
Chris Varelas, co-founder of Redwood Capital and an advisor to AUI, said in a press release provided 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 Architecture
Apollo-1’s core innovation is its neuro-symbolic architecture, which separates linguistic fluency from task reasoning. Instead of using the most common technology underpinning most LLMs and conversational AI systems today — the vaunted transformer architecture described in the seminal 2017 Google paper "Attention Is All You Need" — AUI's system integrates two layers:
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Neural modules, powered by LLMs, handle perception: encoding user inputs and generating natural language responses.
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A symbolic reasoning engine, developed over several years, interprets structured task elements such as intents, entities, and parameters. This symbolic state engine determines the appropriate next actions using deterministic logic.
This hybrid architecture allows Apollo-1 to maintain state continuity, enforce organizational policies, and reliably trigger tool or API calls — capabilities that transformer-only agents lack.
Elhelo said this design emerged from a multi-year data collection 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.”
However, enterprises that have already built systems built around transformer LLMs needn't worry. AUI wants to make adopting its new technology just as easy.
"Apollo-1 deploys like any modern foundation model," Elhelo told VentureBeat in a text last night. "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 Domain Flexibility
Apollo-1 is described as a foundation model for task-oriented dialog, meaning it is domain-agnostic and generalizable across verticals like healthcare, travel, insurance, and retail.
Unlike consulting-heavy AI platforms that require building bespoke logic per client, Apollo-1 allows enterprises to define behaviors and tools within a shared symbolic language. This approach supports faster onboarding and reduces long-term maintenance. According to the team, an enterprise can launch a working agent in under a day.
Crucially, procedural rules are encoded at the symbolic layer — not learned from examples. This enables deterministic execution for sensitive or regulated tasks.
For instance, a system can block cancellation of a Basic Economy flight not by guessing intent but by applying hard-coded logic to a symbolic representation of the booking class.
As Elhelo explained to VentureBeat, LLMs are "not a good mechanism when you’re looking for certainty. It’s better if you know what you’re going to send [to an AI model] and always send it, and you know, always, what’s going to come back [to the user] and how to handle that.”
Availability and Developer Access
Apollo-1 is already in active use within Fortune 500 enterprises in a closed beta, and a broader general availability release is expected before the end of 2025, according to a previous report by The Information, which broke the initial news on the startup.
Enterprises can integrate with Apollo-1 either via:
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A developer playground, where business users and technical teams jointly configure policies, rules, and behaviors; or
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A standard API, using OpenAI-compatible formats.
The model supports policy enforcement, rule-based customization, and steering via guardrails. Symbolic rules allow businesses to dictate fixed behaviors, while LLM modules handle open-text interpretation and user interaction.
Enterprise Fit: When Reliability Beats Fluency
While LLMs have advanced general-purpose dialog and creativity, they remain probabilistic — a barrier to enterprise deployment in finance, healthcare, and customer service.
Apollo-1 targets this gap by offering a system where policy adherence and deterministic task completion are first-class design goals.
Elhelo puts it plainly: “If your use case is task-oriented dialog, you have to use us, even if you are ChatGPT.”
