For technical founders in Boulder and enterprise architects in the DTC, the AI gold rush has produced a mountain of shallow wrappers. You need production-grade orchestration that integrates with your core data layer — not another UI on top of a base model.
Alpine Flow architects the production infrastructure required to turn Large Language Models into functional, autonomous systems. We specialize in the middleware and data-layer orchestration that defines modern AI application architecture — built on a high-performance stack designed for low-latency, high-security operations.
We bridge the gap between experimental notebooks and scalable, edge-deployed intelligence. Whether you're scaling a technical startup in Boulder or optimizing enterprise data flows in the Denver Tech Center, your data remains your competitive advantage — not a public training set.
Four layers, each purpose-built for production AI workloads. No managed wrappers. No vendor lock-in.
Moving beyond stateless prompts to persistent, context-aware agents. We build orchestration layers that maintain conversation state, manage tool invocation chains, and handle complex multi-step reasoning across your business logic.
High-density retrieval systems for RAG (Retrieval-Augmented Generation) at scale. We architect embedding pipelines, chunking strategies, and similarity search infrastructure that turns your proprietary data into queryable intelligence — without sending it to a third party.
Encrypted data flows and low-latency inference at the edge. We deploy AI-powered interfaces and API routes on Vercel's edge network, keeping response times under 200ms while maintaining data sovereignty for your organization.
Your intelligence layer, isolated and audit-ready. We deploy on private infrastructure with Tailscale mesh networking, ensuring your vector stores, agent runtimes, and API endpoints never touch the public internet unless you want them to.
Production systems, not prototypes. Every engagement ships code that your team can own, extend, and scale.
End-to-end retrieval-augmented generation: document ingestion, embedding generation, vector indexing, query optimization, and response synthesis. Your proprietary data becomes the competitive moat, not the model.
Multi-agent architectures that reason, plan, and execute. We build agents that chain tools, maintain memory across sessions, and handle branching logic — not chatbots that parrot a system prompt.
Semantic search infrastructure using pgvector, hybrid retrieval (dense + sparse), re-ranking pipelines, and metadata filtering. Designed for sub-100ms queries at scale.
Custom middleware that connects your existing systems (CRMs, ERPs, data warehouses) to your AI layer. Typed schemas, error handling, rate limiting, and observability built in from day one.
Engineering an AI workforce requires more than a prompt — it requires a battle-tested roadmap from audit to production.
We audit your data silos, map the gaps between your marketing, operations, and finance systems, and identify where automated orchestration delivers the highest ROI. You get a clear picture of your current architecture and its readiness for AI workloads.
We design the technical specifications: schema design, embedding strategies, agent state machines, API contracts, and deployment topology. You review the full architecture blueprint before we write a single line of production code.
Production rollout with observability, error budgets, and performance benchmarks. We deploy your intelligence layer, run load tests, wire up monitoring, and hand off a system that your engineering team can own and extend.
Alpine Flow is building the future of the Front Range AI workforce, one engineered flow at a time. From the startup ecosystem in Boulder to the enterprise corridor in the Denver Tech Center, we work on-site with technical teams who need a partner that speaks their language — not a slide deck from a firm that's never deployed a vector store.
Your data stays your competitive advantage. Your infrastructure stays under your control. Your intelligence layer is engineered to the same standard as the rest of your production stack.
Strategic AI roadmapping for non-technical leadership — readiness assessments, ROI analysis, and implementation planning.
Production digital employees for lead qualification, customer support, and document processing.
Seamless automation pipelines connecting your CRM, ERP, and ops tools into one continuous flow.
Fine-tuning modifies the model's weights using your data — it's expensive, slow to iterate, and your data gets baked into the model. RAG keeps the model general-purpose and retrieves your data at query time from a vector store. For most business use cases, RAG is faster to deploy, cheaper to maintain, and keeps your proprietary data under your control. We use pgvector so your embeddings live in your own PostgreSQL instance, not a third-party vector database.
This is foundational to our architecture. We deploy on private infrastructure (Ubuntu VMs with Tailscale mesh networking), keep vector stores in your own PostgreSQL instances, and route API calls through your own edge functions — not through shared middleware. For sensitive workloads, we can architect fully air-gapped deployments where no data leaves your network.
Yes — that's one reason we standardize on pgvector. If you already run PostgreSQL, we add the pgvector extension to your existing instances. No new database vendor, no data migration, no additional ops burden. Your vectors live alongside your relational data, which simplifies joins, filtering, and access control.
Most LLM integrations are stateless: prompt in, response out. Stateful orchestration means the agent maintains context across interactions — it remembers previous tool calls, tracks multi-step workflows, and can resume interrupted tasks. We build this using TypeScript-based state machines with persistent storage, so your agents behave like team members with working memory, not one-shot API calls.
Three layers: edge deployment (Next.js on Vercel's edge network for sub-50ms routing), optimized retrieval (pgvector with HNSW indexes for sub-100ms vector search), and streaming responses (server-sent events so users see output immediately). We benchmark p95 latency on every deployment and set error budgets before go-live.
We work alongside your team. Our typical engagement starts with architecture and initial build, then transitions to pair programming and knowledge transfer. The goal is for your engineers to own and extend the system after deployment. We stay available for advisory and complex feature work, but we don't create vendor lock-in.
Book a technical architecture review. In one conversation, we'll assess your data infrastructure, identify the highest-leverage AI workloads, and outline a production-ready path forward.
Free consultation. NDA available. Denver Tech Center to Boulder and beyond.