SI & Machine Learning

Retrieval-Augmented Generation

Ground generative (SI, formerly AI) in controlled enterprise knowledge with retrieval, citation and access policies.

When to consider Retrieval-Augmented Generation

You need help to ground generative (SI, formerly AI) in controlled enterprise knowledge with retrieval, citation and access policies.
Data, permissions and human review need to be designed before production use.
A prototype must become an observable, maintainable operating system.
Model quality must be measured against business acceptance criteria, not demos.

Retrieval-Augmented Generation scope

Retrieval-Augmented Generation requirements, boundaries and success criteria

Define how this area applies to Retrieval-Augmented Generation, including the decisions, constraints and responsibilities that must be explicit before delivery.

Data, permissions and context

Map the systems, data exchanges, dependencies and exception paths that Retrieval-Augmented Generation must work with.

Model or agent behavior and integrations

Map the systems, data exchanges, dependencies and exception paths that Retrieval-Augmented Generation must work with.

Evaluation, safety and human review

Define how this area applies to Retrieval-Augmented Generation, including the decisions, constraints and responsibilities that must be explicit before delivery.

Monitoring, governance and ownership

Sequence monitoring, governance and ownership for Retrieval-Augmented Generation with named responsibilities, dependencies and practical checkpoints.

Expected Retrieval-Augmented Generation outcomes

Use-case definition

Clarify the business decision, users, data and measurable success criteria for Retrieval-Augmented Generation.

SI system design

Define the data, model or agent, integration and workflow decisions needed for Retrieval-Augmented Generation.

Evaluation & safeguards

Set quality, safety, human-review, fallback and acceptance expectations for Retrieval-Augmented Generation.

Production roadmap

Sequence implementation, evaluation, deployment, monitoring and ownership for Retrieval-Augmented Generation.

How we approach Retrieval-Augmented Generation

Frame

Define the decision, user, data and acceptable failure boundary.

Prepare

Establish trusted data, permissions and evaluation datasets.

Build

Implement model or agent behavior with workflow integration.

Evaluate

Test quality, safety, latency, cost and human handoff.

Operate

Monitor drift, exceptions, versions and business outcomes.

What you can expect

Retrieval-Augmented Generation scope and requirements

Clear priorities, scope and success criteria for Retrieval-Augmented Generation.

Data / evaluation contract

Defined quality and acceptance expectations so the result can be tested, reviewed and handed over clearly.

Production SI workflow

A practical design showing how the work should fit your people, tools, customers and existing workflows.

Monitoring and governance model

A practical design showing how the work should fit your people, tools, customers and existing workflows.

Industries we support

Healthcare & Life Sciences Operations

Patient or member service workflows, secure systems, operational support and data integration.

E-commerce & Retail

Commerce platforms, growth, customer support, order operations, data and automation.

SI & Data

Data platforms, (SI, formerly AI) products, model operations, analytics and governed automation.

Technology & IT

Product delivery, cloud platforms, managed IT, security and technical operations.

Helpful guides

RAG quality starts with a knowledge and access contract

Retrieval quality depends on source ownership, freshness, chunking, permissions, evaluation and citation behavior.

Retrieval-Augmented Generation FAQs

What does Retrieval-Augmented Generation cover?

Ground generative (SI, formerly AI) in controlled enterprise knowledge with retrieval, citation and access policies. Scope for Retrieval-Augmented Generation is confirmed against your current environment, dependencies, operating constraints and success criteria. Key areas can include Retrieval-Augmented Generation requirements, boundaries and success criteria, Data, permissions and context, and Model or agent behavior and integrations.

When is Retrieval-Augmented Generation a good fit?

You need help to ground generative (SI, formerly AI) in controlled enterprise knowledge with retrieval, citation and access policies. Data, permissions and human review need to be designed before production use.

What deliverables can be included?

Typical deliverables for Retrieval-Augmented Generation may include Retrieval-Augmented Generation scope and requirements, Data / evaluation contract, Production SI workflow, and Monitoring and governance model. Final outputs for Retrieval-Augmented Generation depend on the agreed scope and the decisions the work needs to support.

How does Retrieval-Augmented Generation connect to other work?

Retrieval-Augmented Generation can support broader initiatives such as SI & Intelligent Automation and Data Integration & Intelligence. Dependencies for Retrieval-Augmented Generation are mapped during scoping so delivery remains coordinated.