Enterprise Deployment

AI that runs
on your hardware.
Managed by us.

We spec the hardware, procure it, install it, deploy the models, and maintain everything going forward. Your AI runs on-premises, under your roof, with your data never touching a public cloud.

What we deliver

Site survey & hardware specification
Procurement coordination
Physical installation & rack setup
Model deployment & configuration
Network & security hardening
Staff training & documentation
Ongoing remote monitoring
The Case for On-Premise

Your data belongs
in your building.

Cloud AI is convenient. On-premise AI is sovereign. For organizations handling sensitive client data, regulated information, or proprietary operations — the architecture matters as much as the capability.

01

Complete Data Sovereignty

Every query, every document, every conversation stays within your physical facility. Nothing is transmitted to a third-party inference provider. No API logs. No shared compute.

02

No Ongoing API Costs

Cloud AI bills per token — costs scale with usage and surprise you at the end of the month. On-premise hardware is a capital expense. Once it's running, inference is effectively free.

03

Air-Gap Capable

For environments requiring full network isolation — legal, healthcare, government, defense-adjacent — our deployment architecture supports fully air-gapped operation with zero internet dependency.

04

Performance at Scale

On-site GPU hardware delivers sub-second inference with no rate limits, no throttling, and no latency from a round-trip to a cloud endpoint. The model runs at the speed of your network.

Full-Service Deployment

We handle everything.
You use the result.

Most technology deployments require you to manage vendors, coordinate logistics, and troubleshoot configurations. Ours do not. One engagement. One point of contact. We take it from conversation to operational.

01
Site Survey

Power, cooling, rack space, and network assessment

02
Hardware Spec

GPU tier, memory, storage, and network configuration matched to your workload

03
Procurement

Vendor coordination and delivery logistics managed by our team

04
Installation

Physical rack setup, cabling, and power distribution

05
Deployment

Model installation, quantization, and performance tuning

06
Training

Staff onboarding, documentation, and workflow integration

07
Monitoring

Ongoing remote health checks, updates, and support

Hardware Configurations

Matched to your workload.
Installed by our team.

Every deployment is sized against your actual usage volume, concurrent user count, and model requirements. These represent our standard tiers — custom configurations available for specialized workloads.

Professional

Workstation Deployment

Small teams — up to 25 concurrent users
  • GPUNVIDIA RTX 4090 (24GB)
  • RAM128GB DDR5
  • Storage4TB NVMe SSD
  • Model capacityUp to 34B parameters
  • Form factorTower workstation
  • Typical modelsLlama 3.1 8B / Mistral 7B
Enterprise

Rack Server Deployment

Mid-size operations — up to 150 concurrent users
  • GPUNVIDIA A100 80GB (×2)
  • RAM512GB DDR5 ECC
  • Storage16TB NVMe RAID
  • Model capacityUp to 70B parameters
  • Form factor2U rack server
  • Typical modelsLlama 3.1 70B / Mixtral 8x7B
Sovereign

Multi-Node Cluster

High-volume or air-gapped — unlimited scale
  • GPUNVIDIA H100 SXM (×4–8)
  • RAM1TB+ DDR5 ECC
  • StorageCustom SAN / NAS
  • Model capacity405B+ parameters
  • Form factorMulti-rack cluster
  • NetworkInfiniBand interconnect
The Architecture

Built on RAG.
Not locked to one model.

Every deployment runs on a retrieval-augmented architecture — your knowledge base lives separately from the language model itself. That single decision is what makes these systems accurate, scalable, editable, and transparent — and built to outlast any one model generation.

01

Grounded, Not Guessing

Answers are retrieved from your actual data, not recalled from a model's training. Every response traces back to a real source — not a plausible-sounding guess.

02

Editable Without Retraining

Update the knowledge base directly — add a document, correct a fact, remove something outdated. No fine-tuning, no retraining cycle, no waiting on a model update.

03

Transparent by Default

You can see exactly what the system retrieved and why it answered the way it did. What it knows, and where that knowledge came from, is never a black box.

04

Interchangeable Models

The knowledge layer is decoupled from the model running it. As better, faster, or cheaper models come out, we swap the model — not rebuild the system.

As the technology grows and the hardware changes, your programs won't have to.

The Platform

Six enterprise assistants.
Deployed on your hardware.

Every assistant runs locally on your infrastructure — no cloud inference, no third-party data handling. Each is purpose-built for a specific operational function and configured to your organization.

01

Grant Writer

Searches active federal, state, and private grant databases. Drafts structured applications — NSF SBIR, TNECD, SBA programs — with your organization's information and objectives already loaded.

02

Customer Service

Handles inbound inquiries, resolves standard issues, and escalates intelligently. Consistent and available around the clock — even during peak volume periods your team can't absorb.

03

Intake & Discovery

Conducts structured discovery with new prospects. Collects the information your team needs to move forward — without the follow-up, the back-and-forth, or the scheduling overhead.

04

Sales Assistant

Qualifies leads, tracks buying signals, and maintains consistent follow-up cadence. No opportunity goes cold from inaction. No lead falls through because someone was too busy.

05

Call Outcome Detection

Analyzes call transcripts and identifies what separates converted conversations from lost ones. Gives your sales organization the feedback to systematically improve close rates.

06

Hospitality Host

Manages guest and client communications with consistent professionalism. Every interaction handled at the same standard — regardless of staffing levels or time of day.

Deployment Options

Three ways to deploy.

Cloud-managed for organizations that want zero infrastructure responsibility. Standalone for teams with existing infrastructure. Full on-premise hardware deployment for organizations that require complete data sovereignty.

Cloud Managed
Managed Subscription
$499/mo

We host and maintain everything. All six enterprise assistants, always updated, zero infrastructure overhead on your end.

  • All 6 enterprise assistants
  • White-label configuration
  • API access included
  • Fully managed by Serge
  • Cancel anytime
Get Started — $499/mo
Self-Hosted
Standalone Purchase
$8,999 one-time

Full enterprise assistant bundle delivered. Deploy on your own infrastructure using your own API keys. No monthly fees.

  • Complete enterprise assistant bundle
  • Deploy on your infrastructure
  • Your API keys — your data
  • Full source code included
  • No monthly fees ever
Buy Outright — $8,999
Due Diligence

Questions we expect.

What does a hardware deployment engagement actually cost?

Hardware costs vary significantly by configuration — a professional workstation deployment starts around $8,000–$15,000 in hardware, while a multi-node enterprise cluster can exceed $200,000. Our engagement fee covers the site survey, specification, installation, deployment, and training. Contact us for a scoped proposal specific to your organization's requirements.

Do we need to already have data center infrastructure?

No. We size the deployment to your actual environment. A professional-tier deployment fits in a standard office space — it looks like a workstation, not a data center. Rack deployments require appropriate space, power (20A–30A circuits), and cooling, which we assess during the site survey.

What AI models do you deploy?

We deploy open-weight models including Llama 3.1 (8B, 70B, 405B), Mistral, Mixtral, and Qwen, selected based on your performance requirements and hardware configuration. We can also deploy fine-tuned variants if your use case benefits from domain-specific training. All models run locally — no external inference calls.

How long does a full hardware deployment take?

From signed engagement to operational system: typically 3–6 weeks. Site survey and specification take 1–2 weeks. Hardware procurement lead times vary by configuration (1–3 weeks). Physical installation and model deployment take 2–4 days on-site. Staff training follows.

What happens after installation?

We provide remote monitoring, model updates, and support as part of the ongoing service arrangement. If something goes wrong with the hardware or software, you have a single contact who knows your exact configuration and deployment.

Can this work in a fully air-gapped environment?

Yes. Our Sovereign configuration supports complete network isolation — no internet connectivity required for inference operations. This is appropriate for classified environments, highly regulated data handling, or organizations with strict data perimeter requirements. Additional lead time and planning is required for air-gap deployments.

Can the platform assistants be white-labeled?

Yes. All six enterprise assistants can be deployed under your brand — your name, your voice, your domain. Clients and staff interact with your branded assistant. Nothing references Serge in the user-facing interface.

This starts with a conversation.

Tell us about your organization. We'll scope the right deployment and put a proposal in front of you.

Request an Engagement admin@sergebusiness.com