Your Data Stays Inside Your Environment. Full Stop.

Most AI tools route your data through servers you do not own. Customer records, financial documents, legal communications, internal strategy. Every call is processed and retained by a vendor operating under their own terms. Most businesses using these tools do not know what those terms say about data retention or training use.

For businesses with client data agreements, healthcare obligations, or legal privilege requirements, that is not a footnote. It is an exposure that stops AI adoption cold until someone builds the right environment. That is what I do.

From initial audit to full production deployment. I define what gets built and how it works before anything starts.

I run open-source AI models on your own hardware, fully isolated from external services. Your data is processed internally. Nothing leaves your environment during any AI operation.

I assess your current AI tool stack for data exposure and compliance risk. Most businesses I audit find problems they did not know existed. Better to find them before your legal team does.

Versioning, monitoring, updates, and production operations for self-hosted models. I build the operations layer so the infrastructure runs reliably without requiring manual intervention on your end.

I Benchmark open-source models against the specific tasks your business needs to run. You get a recommendation based on cost, performance, and data requirements for your situation.

I assess every AI tool currently in use and map where your data is going. Most businesses are surprised by what they find. You get a clear picture of the exposure before we design anything.

I design the private deployment architecture specifically for your infrastructure, your compliance requirements, and your use cases. No generic blueprints that do not account for how your business actually works.

I install, integrate, and test the model inside your environment. I verify it is working correctly before I hand anything over. You do not inherit an untested system.

Monitoring, performance tracking, model updates, and ongoing optimization. I build the operations layer so you are not managing it manually as your usage grows.

A professional services organization had their legal team flag the entire AI tool stack for routing sensitive client data through public APIs. AI adoption inside the firm came to a stop. I designed and deployed a private AI infrastructure inside their own environment. All AI processing now runs internally. No data leaves their building. Legal cleared the stack and the firm resumed and expanded AI adoption within 30 days of deployment.

AI tools routing client records through OpenAI, Anthropic, and Azure APIs. Legal hold on further AI adoption. Compliance review pending.

Private LLM deployment on-premises. All AI processing internal. Legal cleared the stack. AI adoption resumed and expanded within 30 days.

Every engagement starts with a real conversation, not a sales call. Start free, or book a focused session when you're ready to move.

Free

Ebby is trained on Andre's consulting frameworks and business methodology. Describe your situation, answer a few structured questions, and get an immediate assessment of your highest-leverage opportunities.

✓ Answer structured questions about your business and operations

✓ Ebby analyzes your situation using Andre's consulting frameworks

✓ Get a diagnosis of your highest-leverage operational opportunity

✓ Andre reviews your intake and responds within 1 business day

1 Hour + Written Report

$150 one-time

A focused one-hour session on your specific challenge. You receive a written Tech Stack Report: a clear recommendation on what to build, what tools to use, and why. Research-based. No implementation required to get value.

✓ 1-hour deep-dive on your challenge

✓ Written Tech Stack Report delivered after session

✓ Tool recommendations with rationale

✓ No implementation required. Pure expertise

The 21 cities in 8 weeks figure comes from a local service business that needed location pages for every market they were entering as part of a multi-city expansion. They had a content problem and an infrastructure problem at the same time.

The content problem: writing unique, accurate location pages for 21 cities manually would have taken their 2-person marketing team roughly 3 months and produced content of inconsistent quality. The infrastructure problem: they had no system that could take a city name and a service type and produce a complete, published page without manual intervention at every step.

A private content generation pipeline was built that ran on their own infrastructure. The pipeline took a structured input file with city data, service area details, and local specifics, passed it through a hosted model running internally, generated the page content against a validated template, and wrote the output directly to their CMS via API. No content left their environment. No third-party AI service touched their proprietary service details.

The full pipeline took 4 weeks to build and test against a batch of 5 cities. Once the quality benchmarks were met, the remaining 16 cities ran in a single overnight batch. All 21 pages were live within 8 weeks of starting the engagement. The same infrastructure now handles their ongoing content needs as they expand to new markets.

The comparison is to manual pace: at their team size and content complexity, 21 location pages done by hand would have taken 6 to 9 months. The infrastructure compressed that to 8 weeks and made every subsequent city a 2-hour task instead of a 2-week project.