Private GPT Infrastructure
Secure,
Customizable
GPTs
Your team's knowledge, protected. Custom AI assistants built on your data, deployed on your infrastructure, owned by you. Not a chatbot plugin. Real AI infrastructure.
Who We Are
We build private AI systems. You own them.
ShooflyAI builds AI infrastructure for businesses that take their data seriously. Not chatbot plugins. Not wrapper apps that put a logo on someone else's API. We build custom GPTs that run on your servers, trained on your documents, governed by your policies. When you work with us, you get a system. Not a subscription to someone else's.
“Your company’s knowledge is its competitive advantage. The question is whether you’re comfortable with a third party holding every question your team has ever asked. We build systems where that data never leaves your building.”
Diego Herrera-Rios · Managing Partner & CTO
01
Second Brain Architecture
We pioneered the Second Brain system for businesses. A knowledge architecture that captures institutional know-how, indexes it, and makes it instantly accessible to AI. Your GPT is only as good as what it knows. We solve that problem first.
02
Deployed, Not Demoed
Every system we build goes into production. Real teams use them daily. We handle the messy parts: SSO integration, role-based access, document ingestion pipelines, audit logging. The work that turns a prototype into infrastructure.
03
Ongoing Operations
Your business changes. Your SOPs get updated. New products launch. New compliance requirements appear. We operate the system after deployment so the GPT stays current with your business, not frozen at the day it shipped.
The Problem
Your AI tools know everything. So does everyone else.
Every time an employee pastes a client contract into ChatGPT, that data leaves your building. Every prompt, every document, every proprietary process lands on a third-party server. You have no audit trail. No control over what gets trained on. And the AI gives the same generic answers to your competitors.
Data Exposure RiskFrom first prompt to full leakage
Day 1
First Use
Employee signs up for ChatGPT with company email
Week 2
Habitual Use
Uploading SOPs, contracts, client data to get faster answers
Month 3
Shadow AI
Sensitive data scattered across personal accounts you cannot see
68%
of employees use AI tools at work without IT approval (Salesforce, 2024)
$4.88M
average cost of a data breach in the United States (IBM, 2024)
0
audit trail entries when employees use personal ChatGPT accounts
100%
of your prompts visible to the AI provider unless you opt out manually
When an employee leaves, their prompt history goes with them. So does every document they uploaded, every client name they mentioned, every internal process they described. You will never know what was shared or where it went.
The Fix
Your data. Your servers. Your AI.
We build custom GPTs that run inside your environment. Trained on the documents your team actually uses. Governed by the access controls you already have. Every query logged. Every response traceable. Nothing leaves your network.
Infrastructure
Deployed on Your Servers
The GPT runs on infrastructure you control. Cloud, on-prem, or hybrid. Your data stays in your environment at every stage: ingestion, training, inference, and storage. No data ever routes through our servers or any third party.
Knowledge
Trained on Your Business
We map your institutional knowledge and build a structured ingestion pipeline. SOPs, policy docs, product specs, customer playbooks, compliance frameworks. The GPT answers questions the way your best employee would, because it learned from the same sources.
Control
Role-Based Access + Full Audit
Different teams see different things. Sales gets sales knowledge. Compliance gets compliance. Every query is logged with user, timestamp, input, and output. When someone asks the GPT about a sensitive topic, you know about it.
82%
of executives say data privacy is the top barrier to AI adoption.
The demand is there. The trust gap is the problem. (Cisco AI Readiness Index, 2024)
How It Works
Five steps from audit to operating system.
We handle the entire lifecycle. You stay focused on running your business.
Week 1
AI Operating Assessment
We audit your current workflows, AI usage, data landscape, and security posture. This surfaces the gaps, the shadow AI risk, and the highest-value use cases for a private GPT. You get a written assessment with prioritized recommendations.
Weeks 2-3
Knowledge Mapping
We catalog every document, SOP, playbook, and knowledge source your team relies on. Then we design the ingestion architecture: what gets indexed, how it gets chunked, what access controls apply, and how updates flow in automatically as your docs change.
Weeks 3-6
Build + Train
We build the GPT on your infrastructure. Deploy the model. Wire the knowledge base. Configure role-based access. Set up audit logging. Build the integrations with your existing tools. Then we train it on your actual documents and test it against real-world queries from your team.
Week 6-7
Pilot + Deploy
We roll out to a pilot group first. Real users, real queries, real feedback. We tune the system based on what they ask and how they use it. Then we deploy company-wide with documentation, training, and onboarding for every team.
Ongoing
Operate + Evolve
Your business changes. New SOPs get written. New products launch. Compliance frameworks shift. We operate the system so the GPT stays current. Monthly knowledge base updates, model tuning, usage analytics, and security reviews. The system gets better over time, not stale.
What We Build
Real systems. In production.
These are capabilities we have built and operate today. Not concept slides. Not roadmap items. Working infrastructure.
24/7
Autonomous AI agents that operate around the clock. Research, draft, prioritize, and execute tasks without human intervention. Built on our Second Brain architecture with full knowledge base access and tool integration.
ShooflyAI Internal Operations
5,400+
Pages of institutional knowledge indexed and accessible in our production knowledge base. SOPs, frameworks, client data, research archives. All structured, searchable, and feeding live AI agents that use it daily.
ShooflyAI Knowledge Architecture
15
Purpose-built AI agents operating in production across sales, marketing, operations, content, and research. Each one trained on role-specific knowledge with defined responsibilities, tool access, and audit trails.
ShooflyAI Agent Fleet
$4.88M
Average cost of a data breach in the United States. Companies using AI and automation in security saw costs $2.2M lower than those without. (IBM Cost of a Data Breach Report, 2024)
The Pattern
We see the same gaps at every company.
Most businesses are not ignoring AI. They are adopting it in the worst possible way: individually, without governance, and without a plan for what happens when it fails.
Gap 01
Shadow AI Everywhere
Employees using personal ChatGPT, Gemini, and Claude accounts for work tasks. Uploading client data, financial reports, and internal communications. No visibility. No policy. No way to recall what was shared.
Gap 02
Generic Tools, Generic Answers
Off-the-shelf AI does not know your business. It gives the same answers to you and your competitor. It cannot reference your SOPs, your pricing models, or your compliance requirements. So employees paste those in manually, every time.
Gap 03
No Knowledge Retention
When an employee leaves, everything they knew about how to do their job leaves too. Their prompt history, their custom GPTs, their workarounds. There is no system capturing institutional knowledge. Just people, and people leave.
What We Don't Do
Honest about what this is not.
We don't resell ChatGPT.
We do not wrap OpenAI's API in a branded interface and call it custom. If you want a ChatGPT subscription with your logo on it, any agency can do that. We build systems.
We don't store your data on our servers.
Your data never touches our infrastructure. Not during ingestion, not during training, not during inference. The system runs entirely in your environment.
We don't build one-size-fits-all chatbots.
Every GPT we deploy is custom. Different knowledge base, different access controls, different integrations. Your business is not generic. Your AI should not be either.
We don't lock you into our platform.
You own the system. The knowledge base, the model configuration, the integrations. If you stop working with us, nothing disappears. No vendor lock-in. No data hostage.
The Alternatives
What you are actually choosing between.
vs. ChatGPT Enterprise
Better privacy. Same generic AI.
Enterprise tier adds admin controls and data isolation. But it is still a general-purpose chatbot. It does not know your SOPs, your workflows, or your compliance requirements. You are paying a premium for a locked room with nothing useful inside it.
vs. Microsoft Copilot
Deep integration. Shallow understanding.
Copilot connects to your M365 stack and surfaces existing documents. But it does not restructure your knowledge, enforce role-based access at the content level, or learn your operational processes. It searches. It does not think.
vs. Building In-House
Possible. Eventually.
If you have an ML engineering team, budget for a 12-month build cycle, and appetite for ongoing model operations, you can do this internally. Most companies do not. The ones that try usually get a demo that never reaches production.
vs. Doing Nothing
Your employees are already using AI.
The question is not whether AI enters your business. It already has. The question is whether you control it. Every week you wait, more proprietary data lands on third-party servers through personal accounts you cannot see or govern.
The Offer
Start with the Assessment.
The AI Operating Assessment
A comprehensive, one-week audit of your current AI posture. We map your workflows, identify shadow AI risk, catalog your knowledge architecture, and deliver a prioritized roadmap for building a Private GPT system. This is not a sales pitch disguised as a consultation. You get a written deliverable with specific recommendations whether you build with us or not.
Complete workflow and data landscape audit
Shadow AI risk assessment across all teams
Knowledge architecture mapping and gap analysis
Security posture review for AI tool usage
Prioritized build roadmap with timeline and scope
Written deliverable you keep regardless of next steps
Assessment fee is credited in full toward the Private GPT build if you move forward.
FAQ
Questions we actually get asked.
How is a Private GPT different from ChatGPT Enterprise?
ChatGPT Enterprise still routes your data through OpenAI's servers. A Private GPT runs on infrastructure you control. Your prompts, your documents, and your outputs never leave your environment. You also get a system trained specifically on your processes, not a general-purpose chatbot with a company wrapper.
What kind of data can the GPT be trained on?
Anything your team uses day to day. SOPs, internal wikis, policy documents, customer service scripts, onboarding guides, product specs, compliance frameworks. We map your knowledge architecture during the assessment and build the ingestion pipeline around what actually matters to your operations.
How long does it take to build and deploy?
The assessment takes one week. The build typically runs four to six weeks depending on the complexity of your knowledge base and integration requirements. Deployment is staged, starting with a pilot group before rolling out company-wide.
Can it integrate with our existing tools?
Yes. We build connectors for CRMs, ERPs, project management tools, document stores, and internal databases. The GPT becomes part of your existing workflow, not a separate app your team has to context-switch into.
Who owns the data and the model?
You do. The GPT runs on your infrastructure. The training data stays in your environment. The model weights, the prompt engineering, the knowledge base, all of it belongs to you. If you stop working with us, nothing disappears.
Your knowledge is your edge. Protect it.
Every day your team uses generic AI tools, your proprietary data gets less proprietary. Book the AI Operating Assessment. Find out where you stand and what it takes to own your AI infrastructure.
