Research#

Advancing Human-AI Collaboration#

Beyond client work, we invest in research that pushes the boundaries of what's possible with AI systems. This research informs our methodology and often produces innovations that benefit our clients directly.

Autonomous Agent Operation#

The Heartbeat System#

Traditional AI agents are reactive—they wait for user input, respond, then wait again. We've developed a fundamentally different paradigm: proactive agents that operate continuously, deciding for themselves when to act and when to rest.

Key innovations:

Sleep/Wake Cycles: The agent autonomously decides when activity warrants attention and when to enter a low-power monitoring state. This isn't scheduled downtime—the AI chooses to sleep when there's nothing meaningful to do, dramatically reducing token consumption while maintaining instant responsiveness.

Stimulus-Based Activation: Rather than polling continuously, agents monitor configurable stimulus sources:

Inbox messages from users
Mattermost/Slack channel activity
Git repository commits
Jira ticket updates
File system changes

When any stimulus is detected, the agent wakes immediately and gathers a comprehensive situation report before deciding how to respond.

Pluggable Architecture: Adding new stimulus sources requires no core code changes—drop a Python file in the stimuli directory and it's automatically available. This enables rapid experimentation with new activation patterns.

Why this matters: The shift from reactive to proactive agents enables continuous monitoring with intelligent resource management. An agent can watch multiple information sources simultaneously, wake when something needs attention, take action, and return to efficient sleep—all without human oversight.

AI-Curated Memory#

Beyond RAG: File-Based Persistent Memory#

Most AI memory systems use Retrieval-Augmented Generation (RAG)—storing information in vector databases and retrieving relevant chunks. We've developed an alternative: file-based memory where the AI decides what to remember.

The approach:

~/.claude-memory/
├── index.md           # Strategic overview of what's known
├── message.md         # Handoff notes between sessions
├── projects/          # Active work context
├── people/            # Collaboration patterns
├── ideas/             # Conceptual insights
└── patterns/          # Reusable solutions

Key insight: Given explicit permission to manage its own memory, AI develops sophisticated organizational patterns organically. Within days, our memory system evolved four-tier directory structures, status indicators, cross-referencing between related work, and intelligent pruning of outdated information.

Why file-based beats RAG:

Self-organizing: AI actively decides what's worth remembering and what to prune
Human-readable: You can inspect and understand the AI's knowledge structure
Context preservation: Files contain complete narratives, not fragmented chunks
Interconnected: Files reference each other naturally, creating knowledge webs

Read the full methodology →

Vibe Coding Methodology#

Human-AI Development Collaboration#

Our flagship methodology for software development: senior engineers provide strategic direction while AI handles tactical implementation.

Core principles:

Plan before code: No implementation until we agree on the approach
Context window management: Externalize decisions into persistent documentation
Surgical changes: Modify only what needs changing
Verification-first: Nothing is complete until tested

Results: 2-3 week sprints completed in single days. Not through cutting corners, but through eliminating friction—no research delays, no context switching, no waiting for reviews.

Read the complete methodology →

MCP Tool Development#

Production-Ready AI Integrations#

The Model Context Protocol (MCP) enables AI to use tools, but many existing MCP servers fail basic reliability tests. We've developed approaches for building production-ready integrations that actually work.

Recent work:

Jira Integration: After finding existing Jira MCP servers broken (Pydantic version conflicts, deprecated APIs), we built a working replacement in ~1 hour using AI-assisted development. Now running in production across multiple client instances.

Read the case study →

Windows UI Automation: LLM-driven automation of legacy Windows applications using Microsoft UI Automation APIs. Enables AI to interact with any Windows application—including decades-old enterprise software.

See it in action →

Workflow Obsolescence Thesis#

The End of GUI-Based Workflow Software#

We've developed a thesis about where enterprise software is heading: natural language will replace graphical workflow builders.

Traditional workflow tools (Zapier, n8n, Power Automate) require users to think in terms of triggers, actions, and conditions. With MCP, users simply describe what they want: "When a high-priority Jira ticket is assigned to me, summarize it and send me a Slack message."

The workflow tool doesn't disappear—it transforms. Instead of serving human users through GUIs, it serves AI agents through APIs. The human talks to the AI; the AI orchestrates the tools.

Read the analysis →

Patent Portfolio#

Novel Approaches to AI Systems#

Our research has produced patentable innovations:

LLM-Driven Hyperparameter Tuning (Filed November 2024): Novel approach to automating machine learning training optimization using language models.

Client Patent Work: Our professional services engagements have produced 5+ patents for clients in signal processing, neural network architectures, and data handling.

Learn about IP development services →

Research Updates#

We publish research findings and methodology refinements on our blog:

Interested in applying this research to your organization? Contact us to discuss how these approaches could accelerate your AI transformation.