Use case
Internal AI copilots
Retrieval and drafting systems grounded in SOPs, tickets, policy, and company knowledge.
Faster answers. Better handoffs. Less repeated internal work.
AI Deployment Studio
We deploy AI, agents, and full products.
Cordillera works with founders and companies to embed AI systems into real workflows, deploy agents across operations, and build complete AI products from idea to production.
Built and shipped across
- Medical automation
- Data-routing agents
- Event lead capture
- Workflow copilots
What AI deployment means
From AI experiments to systems people use.
AI deployment is not choosing a model and waiting for value. It is the work of connecting models, agents, data, product surfaces, permissions, reviews, and monitoring into the way a company already operates.
Cordillera works beside your team to turn one specific workflow into a production system, then expands the pattern when it proves useful.
Deployment path
01
Start with one high-value workflow.
02
Deploy the first useful system with real users.
03
Harden the pattern so it can scale across the company.
What we ship
Proof through shipped systems.
We sell the studio by showing the work: AI products, agents, and workflows embedded into real operations.
Case study
VitaScribe
Clinical visits turned into reviewed notes, structured fields, and export-ready records for care teams.
Less manual documentation. Faster review. Cleaner downstream data.
Case study
Cordix
An AI-native SaaS starter that packages auth, billing, content, analytics, and metering into a reusable launch system.
Faster launches. Stronger foundations. Less rebuild work.
Case study
Applya
A data-routing agent that reads incoming records, updates tools, and escalates exceptions.
Fewer handoffs. Less glue code. Operations that keep moving.
Case study
ShowLeads
Live-event lead capture with immediate qualification, enrichment, and follow-up routing.
Cleaner pipeline. Faster response. Better sales signal.
Use case
Internal AI copilots
Retrieval and drafting systems grounded in SOPs, tickets, policy, and company knowledge.
Faster answers. Better handoffs. Less repeated internal work.
Deployment
Document extraction
Contracts, invoices, forms, and PDFs converted into structured fields with review built in.
Cleaner records. Faster intake. Less manual copy-paste.
Deployment
Support copilots
Customer messages triaged, drafted, and routed with context from product docs and past tickets.
Shorter response times. Better escalations. Consistent support.
Deployment
Sales intelligence
Calls, forms, and account context turned into CRM updates, deal notes, and next actions.
Better pipeline hygiene. Faster follow-up. More useful sales signal.
Deployment
Workflow agents
Agents that own repeatable internal processes across tools, approvals, and exception paths.
Fewer handoffs. Durable automation. Operations that scale.
Engagement model
Clear path from workflow to production.
Week 1: map the deployment
We identify the workflow, users, data sources, risks, approval points, and the metric that will prove the system is worth scaling.
Weeks 2-4: ship the first system
We build with real data and real operators, then deploy the first AI workflow, agent, or product surface where the team can use it.
Weeks 5-8: harden and hand over
We add evals, monitoring, permissions, cost controls, documentation, and the deployment path your team needs to operate it.
The studio
Senior builders, not a handoff team.
Cordillera is led by Julian Urrego, who’s built and shipped AI products end-to-end, deployed agents into workflows, and taken systems from prototype to production.
The people scoping the system are the people designing, building, and deploying it. No layers, no offshore handoffs, and no strategy deck that disappears before implementation.
3+
Ventures shipped
4
Sectors
Embedded
Team model
Built for real operations
Built to deploy inside your company.
Companies do not need another AI experiment. They need systems that respect data, permissions, approvals, budgets, and the way work actually moves.
Your cloud or ours
We can deploy inside your cloud, existing product, or managed infrastructure depending on your data and compliance requirements.
Any model or provider
We are not tied to one lab, model, or cloud. We choose the stack that fits security, latency, accuracy, and cost.
Human review where needed
We design approval flows, exception handling, and permission boundaries so AI can move work without hiding risk.
Monitoring and handover
You get logging, evals, cost controls, architecture notes, and ownership transfer so the system can run without us.
Model and cloud agnostic
We deploy across the major AI labs and infrastructure providers. The model, cloud, and architecture follow the workflow, data, security needs, and cost profile.
AI labs and models
- OpenAI
- Anthropic
- xAI
- Mistral
- Meta
Cloud and infrastructure
- Azure
- AWS
- Google Cloud
- Vercel
- Supabase
- Cloudflare
Contact the studio
Tell us what you want to build.
Send a short note about the product, agent, or workflow you want to ship. We will reply with a direct next step.