DODIL Knowledge Base

Your company already has the answers — in PDFs, internal docs, tickets, emails, images, call recordings, and wikis. The hard part is making that knowledge available on demand, in the exact moment a customer or employee needs it.

DODIL helps you build a scalable knowledge base using a set of products powered by our Vector Stack:

• Vector Ingestion Pipeline (VNG): ingest, extract, chunk, enrich, and embed data
• Vector Database (VBase): store vectors + metadata and retrieve with vector & hybrid search
• Vector Intelligence (VI): quality signals like anomalies, drift, and clustering (coming soon)

The problem (in business terms)

Most organizations face the same reality:

• Knowledge is distributed across tools and teams
• Search is keyword-based, so it misses intent and synonyms
• Answers are buried inside long documents (not easy to locate)
• AI assistants are unreliable without accurate, up-to-date context

The result is measurable:

• Support and sales spend time searching instead of solving
• Onboarding and operations slow down
• Customers get inconsistent answers
• AI outputs can hallucinate when retrieval is weak

You can’t scale AI assistants without a scalable knowledge base.

What you can build

Here are common outcomes teams deliver with Knowledge Base on Demand:

Customer support knowledge base

• Reduce ticket time by retrieving the exact policy paragraph, troubleshooting step, or product spec.
• Power chat and agent tools with verified context (RAG).

Example: A customer asks about refunds. Instead of sending a full policy PDF, your assistant returns the relevant section, plus the right internal workflow.

Sales enablement and deal rooms

• Give reps instant answers from decks, pricing sheets, competitor notes, and contracts.
• Return exact snippets that can be copied into emails and proposals.

Example: “Does this plan include SSO?” → return the product sheet paragraph + pricing table row.

Internal ops and onboarding

• Make SOPs, runbooks, and HR policies searchable by meaning.
• Help new employees learn faster and reduce repeated questions.

Example: “How do I request access to production?” → return the steps + required approvals.

Compliance and audit support

• Retrieve the exact evidence and explanations across policies, logs, and documentation.
• Track data sources and keep results consistent.

Example: “Where is encryption-at-rest described?” → return the section from your security policy and architecture doc.

Why “vector” matters

Keyword search finds exact words.

Vector search finds meaning — even when the query uses different wording.

That unlocks:

• Semantic retrieval: match intent, not just text
• Chunk-level answers: return the right part of a document
• Multimodal support: apply the same approach to text, images, and more
• RAG-ready context: reliable grounding for LLM outputs

Vectors don’t replace filters and structured data — they make retrieval smarter and more consistent.

How it works

At a high level, you build the knowledge base in a repeatable pipeline:

1. Ingest raw inputs (files, URLs, text, streams)
2. Extract + normalize content
3. Chunk into retrievable units with stable IDs
4. Enrich with metadata (source, owner, permissions, timestamps)
5. Embed chunks into vectors
6. Store vectors + metadata
7. Retrieve via vector search or hybrid search + filters

DODIL provides these steps as first-class services so you don’t have to assemble and maintain the whole stack yourself.

The products behind it

Vector Ingestion Pipeline — VNG

A pipeline-first ingestion layer that turns real-world inputs into structured, searchable knowledge.

• ingest files and content at scale
• extract, normalize, chunk, and enrich metadata
• run jobs with progress tracking and repeatable outputs
• produce embeddings ready for storage and retrieval

Vector Database - VBase

A collection-oriented vector store and retrieval service that’s easy to operate.

• collections and schemas for organizing datasets
• index management for performance tuning
• load/unload for predictable latency
• vector search, filtering, and hybrid search

Vector Intelligence - VI (coming soon)

Quality and insight signals that help you trust and improve your knowledge base.

• anomaly analysis (unexpected changes, spikes, missing sources)
• drift detection (when content and embeddings stop representing reality)
• clustering (understand themes, duplicates, and knowledge gaps)

Where to go next

• Getting Started — build your first knowledge base workflow
• Vector Ingestion — ingestion, jobs, and pipeline outputs
• Vector Database — collections, indexes, load state, and search
• VI — quality signals and insights (coming soon)