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We build it
and set it up.

Full-stack AI development from architecture through production. We write the code, connect it to your software, test everything, and train your team.

An engineer building and deploying custom AI software.
$175K+
Starting price
6 wk
Avg deployment
Zero
Data breaches
95+
Specialists

What does AI implementation include?

This is where the rubber meets the road. NetAesthetics takes your AI opportunity — whether identified through our strategy engagement or your own research — and turns it into a working production system.

We handle every step: architecture design, model selection and training, API development, integration with your existing software stack, security hardening, testing, deployment, and team training. When we are done, your team can run the system independently.

How long does implementation take?

Our average deployment timeline is 6 weeks from kickoff to production. The industry average for comparable projects is 6 to 18 months at traditional consultancies. We move faster because we have completed over 50 implementations and use proven frameworks for rapid deployment.

What about security and compliance?

Our CEO, Rasheid Scarlett, is a federal appointee to the FirstNet Authority Board — the body overseeing the nationwide public safety broadband network. Every NetAesthetics project is built with the same security standards used by hospitals and government agencies.

We have maintained zero data breaches across 50+ implementations over 20 years. For healthcare clients, we build HIPAA-compliant systems. For financial services. For government contractors, federal security requirements.

What technologies do you work with?

We are cloud-agnostic and work across AWS, Azure, and Google Cloud. Our team builds with Python, TensorFlow, PyTorch, OpenAI APIs, custom LLMs, computer vision frameworks, and enterprise integration platforms. We select technologies based on your specific requirements — not vendor relationships.

What results can we expect?

Our clients average 340% return on investment. Recent results include a healthcare network saving $2.1M per year (a ~950% ROI over three years), an e-commerce company achieving 156% revenue growth, and a nonprofit raising $1.2M in 90 days on a platform built in 12 days.

The 6-week deployment, week by week

Most engagements run 6 weeks from kickoff to production — against an industry average of 6 to 18 months. The pace comes from 50+ prior implementations and proven frameworks, not corner-cutting. Here is how a typical timeline breaks down:

  1. Week 1Discovery & architecture — requirements analysis, data review, and the system design your build will follow.
  2. Weeks 2–3Model development & API build — model selection and fine-tuning plus the API layer that exposes it to your stack.
  3. Week 4Integrations & security review — connection to your CRM, ERP, or EHR, with security hardening to the same standard used in healthcare and government work.
  4. Week 5QA & user acceptance testing — validation against the benchmarks agreed before kickoff.
  5. Week 6Production deployment & team training — go-live plus a hands-on training session, full source code, documentation, and a 90-day post-launch support window.

Case studies

Implementations we've delivered

Healthcare
$2.1M
AI triage system deployed in 6 weeks, saving $2.1M per year
Nonprofit
$1.2M
Emergency fundraising platform built in 12 days, raised $1.2M
Real Estate & Hospitality
40 hrs/wk
Automation system saved a property management company 40 hours per week
E-Commerce
3× faster
AI pricing implementation helped a retailer scale 3x faster
View all case studies ›

Ready to see what AI can do for your business?

Book a free 60-minute consultation. No sales pitch — just honest answers about where AI fits.

Take the Free AI Assessment Talk to Rasheid directly ›

Frequently Asked Questions

What does an AI implementation engagement include?

A NetAesthetics AI implementation engagement covers every stage from architecture design through production launch. Deliverables include: requirements analysis, model selection and fine-tuning, API development, integration with your existing software stack (CRM, ERP, EHR, or custom systems), security hardening, QA testing, production deployment, and a hands-on team training session. You receive full source code, technical documentation, and a 90-day post-launch support window. Engagements start at $175,000 and are scoped to your specific use case.

How long does AI implementation take?

NetAesthetics averages 6 weeks from kickoff to production — compared to the industry average of 6 to 18 months at traditional consultancies. This speed is possible because we have completed more than 50 AI implementations and use proven deployment frameworks rather than starting from scratch. A typical 6-week timeline breaks down as: Week 1 discovery and architecture, Weeks 2–3 model development and API build, Week 4 integrations and security review, Week 5 QA and user acceptance testing, Week 6 production deployment and team training.

What integrations does NetAesthetics support for AI implementation?

NetAesthetics is cloud-agnostic and supports integrations across AWS, Microsoft Azure, and Google Cloud Platform. On the application layer, we connect AI systems to Salesforce, HubSpot, Epic EHR, ServiceNow, SAP, Microsoft 365, Slack, and custom REST or GraphQL APIs. Our engineering stack includes Python, TensorFlow, PyTorch, OpenAI APIs, Anthropic Claude, custom LLMs, and computer vision frameworks. For regulated industries, we build compliance into the integration layer — HIPAA for healthcare, rigorous security controls for financial services, and federal security standards for government contractors.

How do you measure success after AI implementation?

NetAesthetics defines success metrics during the discovery phase and tracks them throughout deployment. Core KPIs typically include: cost savings (clients average $1M+ annual savings), productivity gains (hours saved per week), revenue impact (one e-commerce client achieved 156% revenue growth), and accuracy rates for AI model outputs. We also monitor system uptime, inference latency, data drift, and model accuracy degradation over time. Our clients average a 340% return on investment. You receive a monthly performance dashboard for the first 90 days post-launch so you can verify the system is meeting targets.