What does the enterprise AI platform support?
Encapsa supports enterprise workflows with closed-circuit LLM systems, multi-model orchestration, agents and tool use, MCP, RAG and PageIndex retrieval, self-hosted vLLM inference and enterprise integrations. The platform spans 300+ live enterprise deployments across seven verticals and supports shared, single-tenant and on-prem deployment options.
The deployment count measures live deployments rather than distinct customers. The capability map describes approved platform scope, not the topology of every customer environment or a claim that each integration runs in all deployments.
What is Jordan’s leadership responsibility?
As Co-founder & CTO at Encapsa AI, Inc., I lead eight full-time team members: three AI/ML engineers, two full-stack engineers, one UX/UI designer, and two customer-success staff. My work spans hands-on AI engineering and platform architecture, enterprise integrations, customer instruction during every deployment and ongoing training, alongside internal training and usage tooling.
Customer outcomes in this portfolio are selected team deliveries. The individual claim is my leadership and operating responsibility, not sole authorship of every workflow or every component. Engineering, design and customer success are represented in the team composition, rather than being collapsed into an engineer-only headcount.
Which technical capabilities connect the platform?
The work connects integration, orchestration, retrieval, data and context, inference, workflow engineering and delivery. Current architecture includes custom encoding/decoding for AI-based lookup across 100% of platform data, with the governed Context Engine extending that foundation. The coverage figure is not a measure of retrieval accuracy or output quality.
The application and infrastructure stack includes Rust/Axum, Ruby on Rails, Node, React, Temporal, RunPod and Fly.io. Daily tools include Claude Code CLI, Codex CLI, custom Goose harness, and Mistral. Listing a technology describes current experience and architecture work, not proof that every planned integration has shipped platform-wide.
- Agents and tool use with MCP integrations
- RAG and PageIndex retrieval
- Self-hosted vLLM inference
- Shared, single-tenant, and on-prem deployment options
What production outcomes can be examined?
A selected Encapsa underwriting delivery reduced submission-to-decision time from three days to 15 minutes and recaptured 300+ staff hours per month without adding headcount. Other selected deliveries addressed energy-services reporting, education board preparation and hospitality new-hire training. Each outcome retains its own workflow and customer scope.
The detailed underwriting and operational-workflow pages describe the inputs, work and reported result separately. Customer identities remain private. The metrics are reported delivery outcomes in the approved record, not an independently audited platform-wide benchmark, and are not combined into a total-savings claim.
What does the record show?
| Measure | Record | Scope |
|---|---|---|
| Live deployments | 300+ | Enterprise deployments, not a count of distinct customers |
| Verticals | Seven | Industries served by the platform |
| Team | 8 full-time | three AI/ML engineers, two full-stack engineers, one UX/UI designer, and two customer-success staff |
| Deployment options | Shared, single-tenant, and on-prem | Available modes, not a description of every deployment |
| Customer instruction | Every deployment | Plus ongoing training and internal training and usage tooling |
Which skills does this project demonstrate?
- Artificial Intelligence (AI)Closed-circuit LLM systems, multi-model orchestration and operational workflows.
- Engineering ManagementLead eight full-time AI/ML, full-stack, design and customer-success staff.
- Software ArchitecturePlatform capabilities and shared, single-tenant and on-prem options.
- Large Language Models (LLM)Agents, MCP, retrieval and self-hosted vLLM inference.
- Generative AIEnterprise workflows paired with customer instruction and internal tooling.
What does this case study not claim?
- 300+ is the live-deployment count, not the number of distinct customers.
- Shared, single-tenant and on-prem are available modes, not a promise that every request stays inside one customer perimeter.
- Selected customer outcomes are attributed to the Encapsa team Jordan leads.