Generative AI applications
Design and develop applications that use large language models to support business workflows, knowledge access, content generation and intelligent assistance.
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AI Engineering
Build AI systems that work beyond the prototype.
Turn AI opportunities into useful enterprise applications. We help design and engineer GenAI solutions, RAG systems, AI agents and intelligent workflows with the integrations, evaluation and operational foundations needed for real-world use.
The engineering challenge
A working demo is only the beginning.
Enterprise AI applications need to fit into real workflows, work with relevant data, integrate with existing systems and remain reliable as usage grows.
01
Prototype solutions that lack a clear path to production.
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AI responses that are inconsistent, ungrounded or difficult to evaluate.
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Disconnected applications that do not integrate with enterprise workflows.
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Uncontrolled agent actions, operational complexity and unpredictable costs.
Our capabilities
Engineering across the enterprise AI stack.
We work across AI application design, model integration, data retrieval, agent workflows and the engineering foundations required to operate these systems.
Retrieval-Augmented Generation (RAG)
Build knowledge-grounded AI applications that retrieve relevant information from enterprise sources and generate responses with supporting context and citations.
AI agents and copilots
Develop AI assistants and agentic workflows that interact with approved tools, APIs and enterprise systems within defined permissions and operational boundaries.
Workflow automation
Integrate AI capabilities into existing business processes to reduce repetitive work, support decision-making and improve workflow efficiency.
Enterprise integrations
Connect AI applications with enterprise data sources, APIs, identity systems and existing software environments through thoughtful integration design.
Productionization and optimization
Address evaluation, reliability, observability, latency, cost and performance to help AI applications move beyond prototypes toward production operation.
How we engineer
Designed for real-world enterprise requirements.
Our engineering approach considers more than model functionality. We pay attention to the qualities that make AI applications useful, manageable and dependable.
Grounded outputs
Design applications that use relevant context and appropriate validation rather than relying on unverified model responses.
Controlled execution
Keep tools, integrations and agent actions within clearly defined permissions and operational boundaries.
Measurable quality
Use evaluation approaches and monitoring to understand application quality and identify areas for improvement.
Operational readiness
Consider deployment, reliability, observability, cost and maintainability as part of the engineering process.
From concept to operation
A practical engineering lifecycle.
The delivery approach depends on the use case, complexity and existing environment. A typical engagement may include the following stages.
01
Understand and scope
Clarify requirements, users, workflows, data sources and success criteria.
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Design and validate
Select an appropriate architecture and validate key technical assumptions.
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Build and integrate
Develop the application, connect required systems and implement appropriate controls.
04
Evaluate and improve
Assess quality, reliability, performance and operational behavior.
Engineer with purpose
Have an AI solution you want to bring to life?
Let's explore the use case, architecture and engineering approach needed to turn your idea into a practical solution.