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From AI
experiment
to production.

We help companies optimize, build, and operate AI infrastructure — from GPU to production.

aevon_deploy.py
# Migrate from OpenAI → self-hosted LLM
import aevon as ai
 
# Model selection + benchmark
model = ai.select(
  task="legal-extraction",
  target_cost=0.30, # vs $2.1
  platform="coreweave-h100"
)
 
# Deploy + monitor
ai.deploy(model, infra="databricks")
# Cost reduction: 74%
74%
Cost reduction
8wk
To production
60%+
Gross margin
NVIDIASI Partner
DatabricksPro Services
SnowflakePartner Connect
Hugging FaceEnterprise
CoreWeaveDelivery Partner
NebiusCloud Partner
What we do

Full-stack AI. One firm.

We own every layer — from GPU infrastructure through model selection, fine-tuning, and deployment to production. No handoffs. No gaps.

01

AI Strategy & Architecture

Engagements from model selection and GPU economics to enterprise AI roadmaps. We translate your business problem into the right technical architecture — and tell you what not to build.

Model selection Cost analysis Roadmapping
02

Build & Delivery Pods

Dedicated pods of AI Architects and LLM Engineers embedded in your delivery cycle. RAG pipelines, fine-tuning, MLOps, inference optimisation — we build it, you own it.

RAG / LLM Fine-tuning MLOps
03

AI Managed Services

Ongoing model monitoring, retraining, performance optimisation, and SLA-backed support. Keep your AI stack current as models, hardware, and requirements evolve.

Monitoring Retraining SLA-backed
Why Aevon.ai

The AI stack is complex.
Most firms pick one layer.
We own all of it.

Enterprise AI projects fail at infrastructure, not ideas. Too many vendors. Too many handoffs. Nobody owns the outcome. Aevon.ai is different: one engagement, one team, every layer.

About the team

Built by operators, not consultants.

Built by operators who have shipped production AI at Microsoft, Salesforce, and venture-backed startups. We know what enterprise-ready actually means.

Platform-embedded, not platform-neutral

Deep integrations into NVIDIA, Databricks, Snowflake, and Hugging Face. Better infrastructure access, faster deployments, and a path to clients already on these platforms.

We build the unit economics of AI

Cost per inference, cost per output, payback model — we make the numbers visible so your CFO sees the same picture as your CTO.

You own everything we build

All code, models, infrastructure configuration, and documentation from your engagement is yours. No lock-in, no dependencies. A client who can walk away freely is a client who chooses to stay.

How we work

From first call to production
in eight weeks.

01

Discovery

30-minute call. We diagnose the problem: model choice, infrastructure gaps, cost drivers, and blockers. Honest assessment — we'll tell you if we're not the right fit.

02

Proof of Concept

Two-week POC. We prove the architecture with your data. Side-by-side benchmarks — cost, latency, accuracy. Fixed fee. No ambiguity.

03

Build & Deploy

A dedicated pod builds and ships to production. Weekly checkpoints. You own the IP, the code, and the infrastructure. We don't create lock-in.

04

Manage & Evolve

Ongoing managed services: model monitoring, retraining, performance optimisation, and SLA-backed support as the AI landscape continues to move fast.

Ready to move AI
from experiment to production?

Tell us what you're building. We'll tell you how to build it right.