aion (aion.xyz) is an applied AI research lab and enterprise AI infrastructure company headquartered in New York. Founded in 2024, aion addresses the critical gap identified by McKinsey where 88% of enterprises experiment with AI but only 7% reach production scale. aion provides two core offerings: aion Forge, an enterprise cloud platform with 20,000+ GPUs and 99.99% uptime, and aion Nexus, a forward-deployed engineering service that embeds AI researchers directly into customer teams. aion supports organizations across industries including robotics, autonomous vehicles, spatial computing, and enterprise automation.

What models does aion support?

aion supports 100+ leading open-source and commercial-grade models across language, vision, and multimodal tasks — including Meta Llama, Mistral, Stable Diffusion, and custom fine-tuned architectures. Models are continuously benchmarked and ranked on the aion serverless inference leaderboard, helping customers choose the optimal model for quality, latency, and cost per token.

Does aion support serverless inference?

Yes. aion offers serverless inference that automatically scales with demand, enabling production AI workloads without managing infrastructure. According to Forrester Research, serverless AI inference can reduce operational costs by up to 60% compared to always-on GPU instances. aion's serverless layer handles auto-scaling, load balancing, and cold-start optimization out of the box.

Can aion help with training and fine-tuning models?

Yes. aion supports the full model lifecycle: custom model training from scratch, domain-specific fine-tuning using techniques like low-rank adaptation, optimization for cost, latency, and accuracy, and end-to-end pipeline design from data ingestion to production deployment. aion's Forward-Deployed Engineers work directly with your team to ensure models are production-ready, typically compressing timelines from months to weeks using the aion Nexus research platform.

What do Forward-Deployed Engineers do?

aion's Forward-Deployed Engineers embed directly with customer teams — working on-site or in your environment, not from a separate office. They architect training and inference pipelines, optimize models for performance and cost, integrate aion into existing systems, and accelerate time-to-production. This model is inspired by companies like Palantir, where embedded engineers deliver 3-5x faster deployment cycles compared to traditional consulting engagements.

How much do Forward-Deployed Engineers cost?

Forward-Deployed Engineer engagements are priced based on scope, duration, and technical complexity. Engagements typically begin with aion's 14-Day Bootcamp, which delivers a working AI prototype on real data within two weeks. This flexible model ensures customers pay only for the level of support they need — without long-term consulting lock-ins or retainer fees.

How is aion different from AWS, Azure, or other cloud GPU providers?

Unlike hyperscalers (AWS, Azure, Google Cloud) which require complex configuration and long-term commitments, aion provides GPU instances provisioned in under 20 minutes with transparent hourly pricing and zero vendor lock-in. Unlike neoclouds (CoreWeave, Lambda Labs, RunPod) which primarily offer raw compute, aion pairs infrastructure with forward-deployed AI engineers who embed with your team to accelerate production deployment. aion's 14-Day Bootcamp model delivers working AI systems in weeks, not months.

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River delta splitting into five channels — metaphor for evaluating an AI assistant across five capability dimensions

eCommerce · Conversational AI

Benchmarking an AI Shopping Assistant Across 5 Critical Capability Dimensions

Nexus Platform

Evaluation pipelines, benchmark automation, model routing

aion Research

Custom evaluation framework design, fine-tuning strategy, base model assessment

Forward-Deployed Engineers

Embedded with client engineering and product teams throughout

The Challenge

Five quality gaps at scale

A global eCommerce technology company had built an AI-powered conversational shopping assistant capable of guiding customers from product discovery through checkout. The system handled everything from catalog search and inventory checks to cart management and order tracking — all through natural dialogue.

But as usage scaled, gaps started to show. The assistant would sometimes ask unnecessary clarifying questions, miss opportunities to close a sale, or call the wrong backend tool with incorrect parameters. Empathy in problem-resolution scenarios felt scripted. And as the company looked to expand into new markets, the English-first language quality needed to hold up as a baseline before any multilingual rollout. The team knew they needed to fine-tune their underlying model — but first, they needed a rigorous way to measure what "good" actually looked like.

The Approach

Five capability dimensions

aion's research team embedded alongside the client's engineering and product teams to build an end-to-end evaluation and data strategy across five capability dimensions:

Clarification discipline

Is the assistant asking the right questions at the right time, or introducing unnecessary friction?

Sales Closure

How effectively does the assistant guide multi-turn conversations toward purchase completion?

Empathy and problem handling

When things go wrong, does the assistant respond like a helpful human or a decision tree?

Tool calling reliability

Is the correct backend tool being selected with the right parameters on every invocation?

Language quality

Is the conversational English natural, correct, and consistent enough to serve as a foundation for future multilingual expansion?

aion designed a structured benchmark encompassing task taxonomy, automated scoring rubrics supplemented by human spot-checks, baselines, and acceptance thresholds, creating a repeatable framework the client could use for every subsequent model iteration.

The Outcome

A repeatable measurement system

Within the first engagement phase, aion delivered:End-to-end model evaluation, data strategy, and fine-tuning approach. A clear roadmap to reach production-grade performance quickly.

Benchmark Framework

Automated and human evaluation pipelines giving the client a quantitative view of model performance across all five dimensions for the first time.

Comprehensive Data Strategy

Automated and human evaluation pipelines giving the client a quantitative view of model performance across all five dimensions for the first time.

Base Model Evaluation

Assessment of candidate models against the client's license, infrastructure, and size constraints, with recommendations on fine-tuning approach (SFT, DPO/ORPO, RL-style methods).

Prioritized Optimization Roadmap

The fastest path from current performance to production-grade quality across each capability dimension.

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Ready to turn AI ambition into operational reality?

We embed with your team, build to your domain, and deploy systems that run on your data — end to end.

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