Enterprise RAG

private-data controls vector databases (Pinecone, Qdrant, pgvector) indexing company ERP, Notion, and databases securely.

Core Capabilities & Engineering Deliverables

• Hybrid Dense/Sparse Vector Search with Cohere / BGE Reranking
• Connectors for Notion, Google Workspace, Confluence, ERP & SQL
• Compliance-Oriented LLM Pipeline with Configurable Retention
• Semantic Cache Layer to Slash OpenAI/Anthropic API Bills by 60%
• Strict Access Control & Multi-Tenant Document Partitioning

How to Evaluate Enterprise RAG

Enterprise RAG connects approved knowledge to a conversational or workflow interface while respecting the quality and access limits of the underlying sources. We design ingestion, permissions, retrieval, citations, evaluation, and content ownership together.

Start with the operating decision

A useful Enterprise RAG engagement begins with the decision the organization needs to improve, rather than a preselected tool or deliverable. That means identifying the people affected, the current handoffs, the information they rely on, and the consequences of getting the work wrong. This gives the team a practical basis for deciding what should be designed, configured, integrated, or left out of the first release.

Questions that shape the scope

• Identify authoritative sources and content owners.
• Preserve access controls through retrieval and display.
• Measure answer quality with representative questions.

For Enterprise RAG, these questions prevent a proposal from becoming a list of fashionable features. They help separate a near-term release from later work, establish who can approve tradeoffs, and surface dependencies such as source data, third-party systems, brand review, legal review, or customer support.

A delivery plan people can operate

The strongest outcome is not simply a completed asset or deployment. It is a working process that the client can understand and maintain. For Enterprise RAG, that normally means documented assumptions, named owners, review points, access boundaries, and an agreed method for handling changes. The delivery approach should make it clear what is being tested, what evidence will be reviewed, and what happens when the real-world workflow differs from the initial brief.

How to assess fit before committing

Before selecting a Enterprise RAG partner, ask for the proposed discovery method, the information needed from your team, the choices that will be made together, and the limitations that could affect delivery. A credible plan should distinguish confirmed facts from assumptions. It should also explain how feedback will be incorporated, how sensitive information will be handled, and which decisions remain with your organization.

What to bring to discovery

A productive Enterprise RAG discovery session does not require a finished specification. It does require access to the people who understand the work, examples of the current process, existing assets or systems, and clarity about the decision that will mark the project as useful. Bringing examples of edge cases and known frustrations is especially valuable because they expose the constraints that a high-level brief often misses.

Making progress visible

For Enterprise RAG, progress should be visible through concrete artifacts: a clarified workflow, a reviewed design or plan, an implemented component, a test result, or an approved release decision. Regular review is not ceremony. It creates a place to challenge assumptions while a change is still affordable, and it gives the client a record of what has been agreed as the work evolves.

After the initial release

The first Enterprise RAG release should create evidence for the next decision. Teams can use feedback, operational data, support questions, and adoption patterns to decide whether to refine the current scope, connect an adjacent capability, or stop investing in a feature that did not prove useful. This is how a scoped engagement becomes a sustainable improvement cycle rather than a one-time deliverable.

Where this capability connects

Enterprise RAG depends on adjacent capabilities. Planning those connections early avoids a polished deliverable that cannot be adopted, measured, or supported. The right sequence may include research, design, implementation, security review, content, integrations, analytics, or team enablement. The required combination depends on the customer journey and the systems already in place.

Related capabilities: AI Agents · OpenClaw & n8n · Multi-Agent AI

Commercial Delivery Standards & Assurance

• Hybrid Dense/Sparse Vector Search with Cohere / BGE Reranking
• Connectors for Notion, Google Workspace, Confluence, ERP & SQL
• Compliance-Oriented LLM Pipeline with Configurable Retention
• Semantic Cache Layer to Slash OpenAI/Anthropic API Bills by 60%
• Strict Access Control & Multi-Tenant Document Partitioning

Transparent Package Tiers & Delivery Timelines

Frequently Asked Technical Questions

Does RAG make an AI system factually perfect?

No. RAG can improve grounding by supplying relevant sources, but source quality, retrieval quality, prompting, model behavior, and evaluation still determine how dependable an answer is.

What is included in the Enterprise RAG project delivery?

All deliverables include custom architecture, complete Git repository source code access, automated testing, deployment staging, and 100% intellectual property ownership backed by our Delaware bilateral NDA.

What are the typical delivery timelines for Enterprise RAG?

Estimated delivery timelines range from 5 to 28 business days depending on the selected package tier and statement of work.

How does Blunt Soft assure quality and post-launch support?

Every deliverable is verified through strict functional testing, security checks, and includes a 30-day post-launch bug warranty with defect remediation.