Applied AI &
Ongoing Delivery
We help teams turn AI from a prototype into a reliable part of their business. That means integrating it with the tools they already use, making it safe to operate at scale, and building the feedback loops that allow it to improve over time. As companies move from experimentation to adoption, we support the systems, safeguards, and structure needed to keep AI accurate, useful, and aligned with your goals and values.
AugLoop: Embedded AI With a Human-in-the-Loop
AugLoop is our approach to building custom AI systems that combine automation with human oversight. Each loop is designed around your objectives, workflows, and tools so that every agent can reason through tasks, retrieve context, and take action, while people in your organization guide the strategy, curate content, and correct or approve results.
Key features of the AuLoop approach:
Custom-designed agent loop based on your goals, tools, and workflows
Human-in-the-loop checkpoints to guide, approve, or correct AI outputs
Integrated memory and context retrieval to inform decisions over time
Interfaces for feedback, refinement, or re-training
Designed for evolving tasks, such as research, decision support, or content generation
Model Context Protocol (MCP): Scale AI without rewriting your stack
For software companies with existing APIs, we offer an AI Service Layer called the Model Context Protocol (MCP) Server to make it easier for your system to connect with external partners. MCP is an interface layer for making AI work across messy systems. Instead of rebuilding everything to accommodate a model, we develop a lightweight server that handles context by interpreting requests, transforming data, and connecting to legacy APIs or third-party tools. This lets your AI agent operate within your real-world constraints, sharing context, triggering actions, and scaling across teams without breaking what is already in place.
We design each loop specifically for your use case, whether it's research, decision support, content generation, or operational automation. The result is a system that gets smarter over time and stays aligned with what matters to your team.
Key features of the MCP approach:
Creates a structured interface between AI and your existing systems
Translates and adapts data between internal tools, APIs, and third-party platforms
Routes requests and responses based on context, not just static inputs
Supports secure, role-based access to sensitive operations
Reduces the need for one-off integrations or major infrastructure changes
