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Lineate RAG (Retrieval-Augmented Generation) Services

Challenge

Businesses are wary of employees using public LLM tools due to data privacy concerns. Yet they still need AI-driven insights from their private enterprise data.

Solution

We leverage our deep expertise in enterprise data to connect proven off-the-shelf LLMs with your internal data securely. The result is a domain-specific, private LLM tailored to your business needs.

How We Do It?

Data Integration & Preparation

We gather and unify relevant documents, databases, and files, then clean and structure them into smaller, searchable chunks. This ensures the knowledge base is accurate, consistent, and ready for AI use.

Vector Database Setup

We convert the text into semantic embeddings and store them in a high-performance vector database. This enables fast, precise retrieval of relevant information.

Retrieval Component

When a user asks a question, it’s transformed into an embedding and matched against the database. The system retrieves the most relevant content, ensuring responses are grounded in trusted data.

Generation Component

The retrieved content is combined with the user’s query and passed into a Large Language Model. The model then generates context-rich answers supported by the client’s own data.

Integration & Deployment

Finally, we integrate the RAG system into the client’s environment, provide user-friendly interfaces (chat, APIs, dashboards), and ensure it’s production-ready with monitoring and ongoing updates.

Proven RAG solutions

Our Internal Knowledge Retrieval

Problem: With teams spread across two continents, employees struggled to find information on guidelines, past projects, and industries. Knowledge was scattered across Jira, Google Drive, SOWs, and docs.

Solution: Unified all sources into a single knowledge base with a RAG-powered AI chat interface.

Result: Employees can now instantly ask, “What projects have we done in healthcare?” and get accurate answers with source links in seconds.

Guest Review AI Analysis for Hospitality Client

Problem: Client managed thousands of guest reviews across hotel properties, but insights were buried in unstructured text, making manual analysis slow and inconsistent.

Solution: Centralized all reviews into one database and built a RAG-powered AI solution for automated summarization and conversational insights.

Result: The client can now uncover guest sentiment and actionable feedback in seconds using natural-language prompts.

Healthcare Recommendations for Teens

Problem: A publisher’s healthcare database for teens was hard to search, with users struggling to find relevant advice in long articles.

Solution: Built an AI-powered search and chat that delivers age-appropriate answers with grounded quotes and links from the publisher’s database.

Result: Teens can now ask natural questions like “How do I deal with stress before exams?” and get clear, trustworthy guidance instantly.

Leveraging AI to Create Relevant Business Functionality | CEO Ben Engber Explains

2025-09-11

At Lineate, we’re often asked how our approach compares to out-of-the-box AI solutions. The answer is simple: we leverage and extend AI and machine learning models to create relevant business functionality. Most generative AI models people talk about are trained on the internet at large and rely on that generic knowledge, plus whatever you provide in a prompt, to generate answers. For anything beyond very basic use cases, organizations need additional integration and configuration to make AI truly business-aware.

Mapping the Entire Software Development Lifecycle for AI Disruption

2025-09-08

As we embarked on our mission to challenge AI to beat us at every aspect of software development, we quickly realized that most companies are thinking too narrowly about AI's disruptive potential. While others focus on AI as a coding assistant, we needed to reconceptualize the entire value chain and define a methodology for systematically replacing ourselves.

What Is the Biggest Myth About AI?

2025-09-17

When some communication tools offer to use AI to compose responses, it can make you cringe. Not because the responses aren’t good, but because it seems to imply that the hard part of communication is just generating words. Lineate CEO, Ben Engber, explains why this matters.

AI-Driven Ad Campaign Automation: LLMs Enabling Fully Automated Product Ad Placement

2024-10-15

Nowadays, AI is widely used in AdTech for dynamic ad personalization and traffic filtering. But with recent advances in AI offer the promise of doing more. We believe that in the coming year, AI will also be used for ad campaign configuration.

Why IDP Can Fail and How to Make It Work for Your Business

2025-07-17

For years, companies have tried to bring order to the chaos of paperwork. Documents appear in every part of the business, from onboarding and compliance to billing and support, which eventually slows processes and stretches teams thin.

Our Clients

Why Us?

Enterprise Data Expertise

20+ years of experience in data integration and system architecture.

Security & Compliance

Proven experience with HIPAA, GDPR, and financial data regulations.

End-to-End Service

Continuous monitoring, testing, and optimization keep systems accurate, high-performing, and reliable as needs evolve.

Tailored Solutions

We adapt RAG architecture to your specific compliance, data, and workflows—no one-size-fits-all templates.

Industries We Support

Where private, accurate AI makes a difference.

FinTech

Secure, compliant access to financial knowledge bases.

Healthcare

Private AI for patient data and clinical research.

AdTech & Publishing

Fast search and knowledge retrieval across massive datasets.

Enterprise IT & SaaS

Streamlined internal wikis, onboarding, and support automation.

Ready to Unlock Your Data?

Unlock fast, accurate, and secure AI-powered answers from your enterprise data with Lineate’s RAG services.