Private & on-prem AI
Open models such as Llama and Mistral run inside your environment or air-gapped, with full EU data residency. Nothing is sent to third-party APIs and nothing trains external models.
Services
AI that never leaves your control
Alpine Edge helps businesses adopt AI without giving up their data. We build private, GDPR-compliant AI, deployed on your own infrastructure or cloud, so sensitive information stays in your environment and is never used to train someone else's model.
- On-prem & self-hosted LLMs
- GDPR & sensitive-data handling
- MCP servers & AI agents
- AI-native software
Official
Partner
We build private, compliance-ready AI solutions on Anthropic's Claude models.
Documents, databases, CRM systems, and internal APIs connect through an audited MCP server to a self-hosted language model. The entire data path remains inside your infrastructure, and access to public AI APIs is blocked.
What we deliver
Four building blocks for private, production-grade AI, combined into whatever your use case needs.
GDPR & sensitive data
PII detection and redaction, air-gapped deployments, encryption with your own keys, role-based access, and full audit logging, aligned with GDPR and ISO 27001.
MCP servers & agents
Model Context Protocol servers connect your internal tools, APIs, and data to AI agents with scoped permissions and complete auditability, so access can be withdrawn at any time.
AI-native software
Retrieval-augmented (RAG) assistants over your own documents, copilots embedded in your workflows, and the call-center AI we already run in production.
Embedded engineering
Forward Deployed AI Engineering
We don't just integrate AI. We find where it creates value.
Our Forward Deployed AI Engineers work directly with your teams to understand how work actually gets done, not just how it looks on paper.
We map processes, identify repetitive work and operational bottlenecks, redesign workflows where necessary, and build AI automation directly into your existing systems.
Embedded in your operations. Focused on measurable outcomes.
Observe → Map → Improve → Automate → Integrate → Measure
Discuss a forward deployed engagementA Forward Deployed AI Engineer is embedded in your operations and works through a continuous loop: observe how work is done, map the process, improve the workflow, automate it with AI, integrate it with your existing systems, and measure the outcome, which then feeds the next iteration.
What a Forward Deployed AI Engineer does
From first conversation on the shop floor to a system your team uses every day, handled by one engineer, not a chain of them.
Process Discovery
We work directly with employees and stakeholders to understand real-world workflows, tools, decisions, and pain points.
Process Mapping
We document how information, tasks, approvals, and decisions move through your organization.
Automation Opportunity Analysis
We identify repetitive, manual, high-volume, or knowledge-intensive activities where AI can deliver meaningful value.
Workflow Redesign
We improve the process before automating it: removing unnecessary steps, bottlenecks, and duplicated work.
AI & Agent Development
We build AI agents, copilots, MCP integrations, RAG systems, and custom automation around the redesigned workflow.
System Integration
We connect AI directly with your CRM, ERP, databases, internal APIs, documents, communication tools, and existing software.
Deployment & Adoption
We deploy into the real working environment, train users, gather feedback, and iterate until it becomes part of day-to-day operations.
Typical automation opportunities
Instead of starting with a model and searching for a problem, we start with your business. Our engineers work backwards from your processes and objectives to determine where AI actually makes sense, and where conventional automation or process improvement is the better solution.
- Manual data entry and reconciliation
- Document processing and information extraction
- Repetitive CRM and ERP work
- Internal knowledge searches
- Customer and employee support workflows
- Reporting and management summaries
- Approval and escalation processes
- Email and communication workflows
- Quality assurance and compliance checks
- Cross-system data transfer
- Research and information gathering
- Decision-support workflows
One engineer between business and technology
A Forward Deployed AI Engineer combines business analysis, process engineering, software development, and AI implementation in one role. That means fewer handovers between consultants, analysts, and developers, and a much shorter path from identifying a problem to putting a working solution into production.
Business analysisProcess engineeringSoftware developmentAI implementation
How an AI project works
Every engagement is scoped to your data and use cases, and delivered in three phases.
Audit
We map your use cases, data sensitivity, infrastructure, and compliance constraints, and agree the right models and architecture.
Build
We deploy the models, MCP servers, and software in your environment, with the security and data controls above built in from day one.
Run
We host, monitor, and continuously improve the system with the same DevOps discipline as the rest of your infrastructure.
Questions, answered
Private AI means running AI models inside your own infrastructure, on-premises or in your own cloud account, instead of sending data to a third-party API. Alpine Edge deploys open models (such as Llama and Mistral) in your environment so your data never leaves it and is never used to train someone else's model.
Yes. Because the models run in your environment with EU data residency, no personal data is shared with external AI providers. We add PII redaction, encryption, access control, and audit logging, and align deployments with the GDPR and ISO 27001 practices from our EU & ISO compliance work.
An MCP (Model Context Protocol) server lets AI assistants and agents securely use your internal tools, APIs, and data with scoped permissions and full auditability. Alpine Edge builds MCP servers so your teams' AI agents can act on your systems safely, instead of hallucinating or being cut off from your data.
Retrieval-augmented (RAG) assistants over your own documents, copilots embedded in your workflows, and automation. We already ship AI in production for call centers: call transcription, summarization, objection detection, lead-intent scoring, and automated QA.
We work in three phases: an audit of your data, use cases, and constraints; a build phase where we deploy the models, MCP servers, and software; and a run phase where we host, monitor, and improve the system with the same DevOps discipline as the rest of your infrastructure.
A Forward Deployed AI Engineer is an engineer who works embedded inside your organization rather than at arm's length. The role combines business analysis, process engineering, software development, and AI implementation in one person, so there are fewer handovers between consultants, analysts, and developers, and a much shorter path from identifying a problem to putting a working solution into production.
Traditional AI consulting starts with a model and looks for a problem to apply it to. Forward Deployed AI Engineering starts with your business: we observe how work actually gets done, map the processes, redesign them where needed, and only then automate. That means we will also tell you when conventional automation or plain process improvement is the better answer than AI.
The strongest candidates are repetitive, high-volume, or knowledge-intensive activities: manual data entry and reconciliation, document processing and information extraction, repetitive CRM and ERP work, internal knowledge searches, support workflows, reporting and management summaries, approval and escalation chains, quality assurance and compliance checks, and cross-system data transfer.
Trusted by teams across hospitality, retail & healthcare
