AI-CHATBOT / ENTERPRISE AI
Work directly with engineers who help connect your data, applications, and AI agents. Build a secure foundation for intelligent workflows in an environment your organization owns and controls.
Plan Your Engineering ProjectStart with your systems, your AI goals, and a focused use case.
YOUR SYSTEMS CONNECTED TO INTELLIGENT AGENTS
Forward-Deployed Engineering
Connect systems, establish context, and support AI agents
Your systems. Your data. Your AI foundation.
Conceptual illustration, not a product screenshot.
THE OPPORTUNITY
AI agents become more useful when they can access relevant information from the systems your teams rely on. That information may be spread across learning platforms, customer systems, human resources applications, databases, and document repositories.
Forward-Deployed Engineering is a hands-on professional service. Engineers work directly with your team to map your systems, build integrations, and establish a governed memory layer that can give AI agents the context they need.
The solution is designed around your organization’s technology, permissions, and goals. Your team receives the code, configurations, and documentation needed to operate and extend the environment.
Illustrative engineering and integration workflow
Applications and databases
Users and access rules
Business and learning needs
MCP servers and connectors
Memory and retrieval
Security and governance
Authorized access to context
Purpose-built workflows
Monitoring and improvement
KEY CAPABILITIES
Connect the systems, information, and safeguards that help AI agents work within your organization’s environment.
Identify the applications, data sources, users, and permissions involved in your chosen AI use case.
Build MCP servers and connectors that give authorized agents a consistent way to access systems and tools.
Create a foundation for retrieving relevant organizational information and providing context to AI agents.
Design access controls, monitoring, and governance around your organization’s requirements.
THE MODEL CONTEXT PROTOCOL
The Model Context Protocol, or MCP, is an open standard that helps AI applications connect to external tools, services, and information sources.
Our engineers can build MCP servers that expose approved resources and tools to your AI agents. Each connection is designed around the systems and access policies relevant to your project.
Provide a common way for compatible agents to access information from different applications and repositories.
Focus: useful access to information.
Design connections around authenticated users, roles, permissions, and the requirements of each system.
Focus: governed access.
Reuse approved connectors and shared infrastructure as your organization develops additional agents and workflows.
Focus: a foundation that can grow.
SYSTEMS AND INTEGRATIONS
Your project can include a combination of existing systems and custom connections. The appropriate integrations depend on your applications, available interfaces, and access requirements.
Connect compatible systems such as Banner, PeopleSoft, and Colleague to support access to approved student information.
Connect platforms such as Canvas, Blackboard, Brightspace, and Moodle to make approved course information available.
Connect compatible CRM systems such as Salesforce, Slate, and HubSpot to support relevant organizational workflows.
Integrate approved information from systems such as Workday and Oracle, based on your use case and permissions.
Work with identity systems such as Microsoft Entra ID, Okta, and Google Workspace to support authentication and role-based access.
Connect approved content from sources such as SharePoint, OneDrive, and cloud storage for retrieval by authorized agents.
THE MEMORY LAYER
A memory layer helps organize and retrieve information from connected systems. It can provide AI agents with context such as approved documents, course information, deadlines, policies, and other data relevant to a request.
Bring information from approved sources into a structure that supports contextual retrieval across systems.
Use a knowledge graph and search index to help agents find information related to a user's question or task.
Plan how information is synchronized and updated so connected data remains useful as source systems change.
SECURITY AND GOVERNANCE
Secure integrations require more than connecting systems. They need clear rules for identity, permissions, sensitive information, and ongoing oversight.
Apply access rules based on the authenticated user’s role and the permissions configured for each connected system.
Configure rules to mask or exclude sensitive fields when they are not needed or authorized for a particular request.
Record and monitor relevant system activity to support security reviews, troubleshooting, and organizational policies.
Design encrypted connections between components and apply access controls appropriate to your environment.
Address applicable privacy obligations, consent requirements, and retention policies as part of project planning.
Run integration components in your own cloud or on-premises infrastructure, according to your architecture requirements.
WAYS TO USE FORWARD-DEPLOYED ENGINEERING
Start with a use case that brings together information from multiple systems and provides a clear benefit to your users.
Give authorized student-facing agents access to relevant course materials, deadlines, and institutional policies.
Support teaching assistants that help instructors prepare study materials and address common course questions.
Help authorized staff find policies, review information, and prepare summaries from connected systems.
Connect approved workplace documentation and systems to support employee questions and internal workflows.
Make approved handbooks, policies, training materials, and other documents easier for agents to find and cite.
Connect agents to approved tools and information across systems to support coordinated, multi-step processes.
WHO WE WORK WITH
Successful AI integrations require input from the people who understand your systems, policies, data, and users. Our engineers work alongside your stakeholders throughout the project.
Plan system connections, deployment architecture, identity, monitoring, and operational responsibilities.
Identify useful workflows, define user needs, and evaluate whether the solution supports organizational goals.
Review access rules, sensitive information, audit needs, and applicable organizational requirements.
WHAT YOUR ORGANIZATION RECEIVES
Project deliverables are designed to help your organization operate, maintain, and extend its AI integrations.
Source code for the MCP servers and system connectors developed for your project.
Infrastructure and deployment files that support operation in your organization’s chosen environment.
Information about connected data, tool definitions, interfaces, and integration tests.
Project-specific access rules, redaction settings, and other policy configurations.
Operational documentation and monitoring tools to help your team manage connected services.
Where included in the project, agent tools, prompts, and evaluation materials to support future use cases.
OWNERSHIP AND CONTROL
Your organization should be able to understand and manage the technology supporting its AI initiatives. The engineering engagement is designed around ownership and knowledge transfer.
Deploy in your own cloud or on-premises environment, including an air-gapped network where appropriate for the project.
Retain the code, configurations, and project artifacts developed for your organization.
Receive documentation and a handoff so your team can operate and extend the solution, with ongoing support if needed.
GETTING STARTED
Begin with a clear use case and a defined set of systems. Expand the solution as your organization’s needs develop.
Identify your goals, systems, data sources, users, and security requirements.
Select a focused use case and agree on the integrations, milestones, and project scope.
Develop and test the solution, evaluate results, and prepare your team to manage the environment.
FREQUENTLY ASKED QUESTIONS
Forward-Deployed Engineering is a hands-on professional service in which engineers work directly with your team to design, build, and deploy AI integrations around your organization’s systems and requirements.
A memory layer is a foundation for organizing and retrieving information that AI agents can use as context. It can connect information from approved systems and apply access policies relevant to each request.
MCP stands for Model Context Protocol. It is an open standard that helps compatible AI applications connect to external information sources, tools, and services.
Depending on the project, integrations can include learning management systems, student information systems, CRM platforms, ERP and human resources applications, identity systems, databases, and document repositories.
Connections can be designed with authentication, role-based permissions, field-level controls, data masking, and audit logging. The specific controls depend on your requirements and implementation.
Yes. Components can be deployed in your own cloud or on-premises infrastructure. Air-gapped deployment can also be considered based on your project and technology requirements.
An initial deployment may take approximately four to eight weeks, depending on the systems involved and the complexity of the project. Scope, security reviews, and integration requirements can affect the timeline.
Your organization receives the source code, configurations, deployment materials, and other agreed deliverables developed for the project. Documentation and handoff support help your team manage the solution.
Yes. After the initial project, additional support can be discussed for new integrations, improvements, additional workflows, or ongoing management.
LET'S GET STARTED
Explore how Forward-Deployed Engineering can help your organization bring its data, applications, and AI agents together.
Discuss Your Engineering ProjectWe can discuss your systems, AI use cases, security needs, and deployment requirements.
