Private & on-prem AI
We deploy open models such as Llama and Mistral in your environment, with network isolation where required. Processing location, outbound connections, logs and model updates are defined for the deployment.
Services
Private AI built around your data requirements
Alpine Edge builds AI applications with open models and Anthropic Claude. For self-hosted deployments, open models run in your environment. For managed models, we agree the provider, processing region, retention settings and data access controls with your team before rollout.
- On-prem & self-hosted LLMs
- GDPR & sensitive-data handling
- MCP servers & AI agents
- AI-native software
Official
Partner
We build AI applications with Anthropic Claude using an agreed managed deployment.
Example self-hosted deployment: documents, databases, CRM systems and internal APIs connect through an audited MCP server to an open language model. This design keeps the data path inside your infrastructure and blocks public AI APIs.
What we deliver
Four building blocks for private, production-grade AI, combined into whatever your use case needs.
GDPR & sensitive data
Depending on the deployment, controls include personal-data detection and redaction, encryption with your own keys, role-based access and audit logs. Self-hosted open models can also be isolated from external networks. We agree the controls needed to support your GDPR and ISO 27001 requirements.
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your internal tools, APIs, and data to AI agents with scoped permissions and complete auditability, so access can be withdrawn at any time.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 using the agreed hosting arrangement, with the required security and data controls in place before rollout.
Run
We host, monitor, and continuously improve the system with the same DevOps discipline as the rest of your infrastructure.
Questions, answered
Private AI covers deployments with agreed controls over data access, processing and retention. Self-hosted AI runs open models such as Llama or Mistral in your environment. Managed AI uses a provider-operated model, so its processing location and data terms must be checked separately.
We support GDPR requirements through data minimisation, access controls, encryption and audit logging. Each deployment also needs appropriate processing arrangements, retention rules and any required assessments, agreed with your legal and privacy teams.
An MCP (Model Context Protocol) server connects AI assistants and agents to your internal tools, APIs and data with scoped permissions and audit logs. Alpine Edge builds these connections so agents can retrieve relevant information and perform permitted actions. Outputs and actions still need validation appropriate to their risk.
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.
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