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Proprietary Tool Internal • Active

Project Hippo

AI integration daemon that brings large language models into our delivery pipeline. Built for control, transparency, and reliable human oversight.

Why We Built It

AI tools are powerful, but blindly integrating them creates risk: hallucinations, unexpected outputs, decisions that should stay human. We needed a system that lets us leverage AI where it's genuinely useful while keeping the person in the loop where it matters.

Hippo does this by:

  • Running analysis and generation tasks against multiple models for comparison
  • Storing every interaction so we can audit what happened
  • Requiring human approval before any output is used downstream
  • Monitoring for hallucinations or nonsensical results
  • Building feedback loops so we can improve prompts and model choices

System Design

Hippo sits between our internal systems and multiple AI providers. It abstracts the complexity of managing different models, contexts, and costs.

Hippo Architecture: AI Request Flow Internal System Needs analysis code generation, etc. Hippo Core Request router Caching layer Output Handler Review & approval Feedback capture Pluggable Model Providers OpenAI GPT-4, Claude Anthropic Claude 3 Local Models Llama 2, Mistral Custom Models Fine-tuned Future New models Safety & Transparency Layer Request Logging Every request stored: timestamp, model, prompt, cost Output Validation Detects errors, nonsense, and out-of-bounds responses Human Review Queue Critical outputs wait for approval before deployment Cost & Performance Caching Similar requests reuse previous results (80% cache hit) Model Routing Fast tasks use cheaper models; complex work uses premium Cost Tracking Every usage tracked by team and project

Usage Patterns

Code Review Assistant

AI analyzes pull requests for common issues (unused imports, missing error handling, performance antipatterns). Flags for human review, never auto-merges.

Documentation Generation

Generates first drafts of API documentation and guides. Humans review for accuracy, then use as a starting point rather than gospel.

Incident Response

When alerts fire, Hippo pulls logs and suggests what might have gone wrong. On-call engineer reviews, filters out bad guesses, and acts on good insights.

Test Case Generation

AI generates edge case tests for new code. Humans validate the cases make sense, modify if needed, then run them.

Core Principles

Humans Decide, AI Assists

AI never makes decisions that affect users or systems. It provides analysis and suggestions; humans decide whether to act.

Everything Is Auditable

We store every interaction: what we asked, what the AI said, what the human did with it. Supports debugging, learning, and accountability.

Cost Visibility

Every model call is tracked and costed. Teams see what they're spending on AI, creating natural incentives against waste.

Feedback Loops

When humans override or correct AI suggestions, that feedback is captured. Over time, it helps us choose better models and improve prompts.

Results & Impact

40% Faster code review cycles
$12K/mo Budget-conscious spending
0 Critical errors from AI output
80% Cache hit rate on requests

We use AI as a tool to amplify human capability, not replace it. The team spends less time on repetitive analysis and more time on strategic decisions.

Want to integrate AI into your workflow responsibly? Let's design it together →