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Retrieval Agent Manager (RAM)

Enable fast, accurate and context-aware GenAI responses from unstructured enterprise data. Save time, increase compliance, and reduce risk, all while enhancing trust and transparency.

The Unstructured Data Challenge

Over 80% of enterprise data is locked in unstructured formats across diverse documents—and it’s growing rapidly. Relying on labor-intensive information retrieval processes is inefficient, time-consuming, error-prone, and exposes organizations to compliance risks.

Why Retrieval Augmented Generation is Not Enough

While Retrieval-Augmented Generation (RAG) offers a way forward, traditional implementations are code-heavy, fragmented, and inflexible. They depend on technical teams, create vendor lock-ins, and lack agentic capabilities for automation, leaving workflows slow, manual, and difficult to scale across departments.

Why Retrieval Agent Management?

RAM simplifies a traditionally complex architecture, allowing you to scale AI adoption without compromising flexibility or precision. Quickly unlock enterprise knowledge, reduce manual data extraction, and overcome the common limitations of standard RAG implementations.

How we Support ECOAs

Intuitive no-code experience
Trustworthy and transparent AI
Modular, plug-and-play architecture
Enterprise-ready integration
Autonomous agent orchestration
Flexible deployment options

Intuitive no-code experience

Empowers non-technical users to build and manage workflows, supporting diverse document types, multilingual OCR, and usage monitoring.

Quality & Test Automation

Delivers citation-backed, sourced responses with built-in evaluation and human-in-the-loop oversight for transparency, traceability, and trust.

Modular, plug-and-play architecture

Supports leading model providers and vector databases with no vendor lock-in, including native integration with file systems and Git for seamless onboarding.

Enterprise-ready integration

Delivers fast, efficient AI through model acceleration techniques and integrates into enterprise systems via robust APIs and secure, scalable deployment.

Autonomous agent orchestration

Orchestrates agents that retrieve, reason, and act across systems, automating complex, high-value workflows with precision, integration, and parallel execution.

Flexible deployment options

Provides robust support for on-premises deployments, giving you full control over your data with easy extension to cloud or hybrid environments.

35%

Boost in customer satisfaction with  no-code AI chatbots and virtual assistants

40%

Increase in content accuracy over traditional AI models

50%

Decrease in research time in enterprise applications

35%

Boost in customer satisfaction with  no-code AI chatbots and virtual assistants

40%

Increase in content accuracy over traditional AI models

50%

Decrease in research time in enterprise applications

How KMS Integrates SAS RAM

No matter your industry, there’s a tailored RAM solution that can save you time, democratize institutional knowledge, and enhance customer satisfaction. Here’s how KMS helps enterprises deploy their custom RAM solution effectively:

Architecture & Strategy

Align data, security, and enterprise workflows for scalable, production-ready deployments.

Data & Platform Engineering

Build secure pipelines for unstructured enterprise data—across data lakes, ECM, ERP/CRM, and EHRs.

GenAI Application Development

Develop custom copilots, chatbots, and AI assistants, embedded into business workflows.

Enterprise Integration

Connect RAM to existing applications via APIs and microservices, supporting seamless, secure authorization and adoption

Operations & Optimization

Apply DevOps/MLOps for CI/CD and monitoring. Track response quality, performance, and accuracy to continuously optimize.

Security, Compliance & Governance

Implement enterprise-grade security and privacy controls, industry compliance, and data governance.

How SAS RAM Makes an Impact

Accelerate time to value

No-code AI and optimized performance enable chatbots and virtual assistants to deliver more relevant responses, boosting customer satisfaction by up to 35%.

Enhance trust

Verified grounding, human oversight, and built-in evaluation improve reliability, achieving up to a 40% increase in content accuracy over traditional AI models.

Scale efficiently

Flexible AI supports multiple LLMs, vector databases, and API integrations, reducing integration effort without requiring system re-architecture.

Automate decisions

Autonomous agents streamline workflows, cutting research time in enterprise applications by 50% and making decision cycles more efficient.

Frequently Asked Questions

What is unstructured data?

Unstructured data refers to information that does not follow a predefined schema or tabular format. Examples include PDFs, contracts, policies, emails, call transcripts, images, clinical notes, regulatory guidance, and knowledge base articles. This type of data represents the majority of enterprise information but is difficult to search, validate, and operationalize at scale.

Large language models do not inherently understand or verify enterprise data. Without a structured retrieval and validation layer, GenAI systems can hallucinate, return outdated information, or expose sensitive data. Enterprises require traceability, access controls, and governance that raw GenAI models do not provide on their own.

RAG is an architecture that enhances GenAI by retrieving relevant enterprise content at query time and grounding model responses in trusted data sources. This approach improves accuracy, relevance, and transparency compared to standalone language models, but traditional RAG implementations are often:

  • Code-heavy and difficult to maintain

  • Fragmented across multiple tools and vendors

  • Dependent on technical teams for updates and scaling

  • Limited in automation and agent coordination

  • Weak in governance, evaluation, and human oversight

These constraints slow GenAI adoption and make it difficult to scale RAG beyond isolated use cases.

SAS Retrieval Agent Manager is an enterprise-ready RAG solution that enables fast, accurate, and context-aware GenAI responses from unstructured enterprise data—delivered by KMS Technology. It simplifies complex RAG architectures while maintaining flexibility, transparency, and control.

 RAM:

  • Provides a no-code experience for building and managing retrieval workflows

  • Supports multiple LLMs, vector databases, and data sources without vendor lock-in

  • Delivers citation-backed, traceable responses with built-in evaluation

  • Includes human-in-the-loop oversight for trust and compliance

  • Orchestrates autonomous agents to automate complex workflows

This allows organizations to move from experimentation to enterprise-scale GenAI adoption.

RAM is designed for enterprises that need to operationalize GenAI across regulated, data-intensive environments such as BFSI, healthcare, and industrial sectors—where accuracy, transparency, and governance are non-negotiable.

Who is RAM designed for?

RAM is designed for enterprises that need to operationalize GenAI across regulated, data-intensive environments such as BFSI, healthcare, and industrial sectors—where accuracy, transparency, and governance are non-negotiable.
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