Automated Intelligence Platform & CI/CD Pipeline
Replaced manual public document parsing with multi-agent AI pipelines, built on a fully isolated CI/CD foundation that made shipping them safe.
Industry
Energy & AI-Driven SaaS
Services Delivered
Custom Platform, Intelligence Workflows, & Design System
Technologies Used
TypeScript, Vue.js, Laravel, OpenAI, Playwright, CI/CD Pipelines, PostgreSQL
The Challenge
Learnewable builds software tools that help renewable energy developers assess project viability by tracking community sentiment across public dockets, meeting minutes, and regulatory filings. When we engaged, their team needed to ship AI-powered features but was blocked by shared, unstable testing environments that could not be trusted to catch errors before production.
Learnewable's team faced a compounding failure on two fronts. Their users needed intelligent automation to replace hours of manual document parsing, but the underlying CI/CD infrastructure was too unstable to safely ship anything new. Shared testing environments allowed bugs to slip into production unchecked, creating a release paralysis at the exact moment the product roadmap demanded acceleration.
Diagnosis
Before building anything, we conducted a full architectural review and identified the sequencing problem: shipping AI features into an unstable deployment environment would compound the risk, not resolve it. We established a roadmap to stabilize the CI/CD pipeline first, building a fully isolated test environment that spun up clean for every run before engineering a single new feature.
The Solution
We executed a full-stack architectural deployment designed to automate their unstructured data intake and secure their deployment processes. First, we built a fully isolated, automated end-to-end testing pipeline. We engineered an environment lifecycle that spins up a clean application stack for every single test run, completely eliminating the shared-state variables that were causing their legacy tests to fail.
With the infrastructure stabilized, we engineered a bespoke stakeholder engagement portal from scratch using a modern, type-safe stack featuring Vue and TypeScript. Finally, we deployed multi-agent LLM pipelines — including an intake workflow that autonomously parses unstructured public meeting PDFs into structured sentiment data, and an AI web scraper that continuously monitors regulatory shifts to detect market risks without human intervention.
The Outcome
- [1]
Hours of manual work, automated
Public meeting minutes, comment dockets, and regulatory filings that once required a human to read and parse are now processed autonomously. The intake that used to bottleneck permitting approvals runs without intervention.
- [2]
Engineering team ships without fear
Every deploy is now backed by a fully isolated test environment that spins up clean for every run. Bugs that previously slipped into production are now caught before they leave the pipeline.
- [3]
Testing became the deployment gate
What was once an optional, often-skipped step is now the mandatory checkpoint that governs every release. The engineering culture shifted from manual guesswork to systematic trust in their own infrastructure.
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