Source: https://daphnislabs.com/services/ai-data-pipelines-vector-db

# AI data pipelines and vector databases

Handle updates and deletions across documents, records and vector stores.

[Plan My Data Pipeline](https://daphnislabs.com/contact?service=ai-data-pipelines-vector-db) [See Deliverables](https://daphnislabs.com/services/ai-data-pipelines-vector-db#deliverables)

01 **Sources**

02 **Parse**

03 **Chunk**

04 **Embed**

05 **Index**

06 **Query**

Sources

Parse

Chunk

Embed

Index

Query

## Relevant technologies

-   Qdrant
-   Pinecone
-   Weaviate
-   PostgreSQL
-   Python
-   Hugging Face

[All technologies](https://daphnislabs.com/#technologies-we-use)

## What Data Can We Prepare?

[Explore services](https://daphnislabs.com/services)

-   ### Document Collections
    
-   ### Business Records
    
-   ### Product Catalogues
    
-   ### Knowledge Updates
    
-   ### Restricted Collections
    
-   ### Index Migrations
    

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## What You Actually Get

-   **Source Connectors**
-   **Parsing Jobs**
-   **Chunking Configuration**
-   **Embedding Jobs**

-   **Index Schema**
-   **Sync Checkpoints**
-   **Data Quality Report**
-   **Operations Runbook**

## A policy changes. Search catches up.

9:41 •••

**Knowledge index**

The travel allowance guide has a new version.

### The current source appears

Search returns the v4 allowance passage with its document link.

Version + permissions retained

### Index receipt

**Document**

Travel guide

**Current**

v4

**Previous**

v3 · excluded

**Access**

Finance group

Example record

Illustrative animation · Sample scenario

### Read this example

The travel allowance guide has a new version.

1.  **Revision received.** Travel guide v4 replaces v3. Its access group remains Finance. _Change detected._
2.  **Only the change is indexed.** New passages are embedded. The superseded version is excluded. _Updating searchable content._
3.  **The current source appears.** Search returns the v4 allowance passage with its document link. _Version + permissions retained._

A source revision updates the index without losing version or access metadata.

Scripted illustration using sample information, not a client case study or a live system.

## What Does It Take to Build?

[Get a custom estimate](https://daphnislabs.com/contact?service=ai-data-pipelines-vector-db)

Pilot

Production

Enterprise

-   ### Pilot
    
    -   One source collection
    -   Sample ingestion
    -   Initial retrieval checks
    
    [Get an Estimate](https://daphnislabs.com/contact?service=ai-data-pipelines-vector-db)
-   Recommended
    
    ### Production
    
    -   Scheduled or event-based sync
    -   Multiple source formats
    -   Live query workload
    
    [Plan My Data Pipeline](https://daphnislabs.com/contact?service=ai-data-pipelines-vector-db)
-   ### Enterprise
    
    -   Several collections or tenants
    -   Large-scale backfills
    -   Migration requirements
    -   Ongoing data operations
    
    [Talk to Us](https://daphnislabs.com/contact?service=ai-data-pipelines-vector-db)

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**Founded in**

2013

**Projects delivered**

550+

**Client countries**

43+

**Global offices**

3

Engineering teams New Delhi · Dubai · Cyberjaya

Proof [Case studies](https://daphnislabs.com/case-studies)

Stack [Technologies we build with](https://daphnislabs.com/#technologies-we-use)

[AI Development](https://daphnislabs.com/services/ai-development) [Agentic AI Automation](https://daphnislabs.com/services/agentic-ai-automation) [LLM App Development](https://daphnislabs.com/services/llm-app-development) [AI for Internal Operations](https://daphnislabs.com/solutions/ai-for-internal-operations) [AI for SaaS](https://daphnislabs.com/solutions/ai-for-saas)

## FAQs

### Do we always need a vector database?

No. Keyword search, structured queries or a hybrid approach may fit the workload. We compare retrieval needs, filters and operating requirements before selecting a store.

### What happens when a source is deleted?

Deletion handling should remove or invalidate the corresponding indexed content and track completion. Retention and backup behavior also need to follow the agreed policy.

### Can we change embedding models later?

Yes, but the change may require re-embedding and a new index. We plan compatibility checks, comparison queries and a cutover process rather than mixing incompatible vectors.

### How do permissions reach the index?

The pipeline needs stable source identifiers and access metadata. The query layer must apply the relevant filters; indexing a permission field alone does not enforce access.

### Can you process scanned or poorly structured files?

We assess extraction quality on representative samples. OCR or specialised parsers may be needed, and low-quality input may still require review.

### How is freshness measured?

We can track source change time, successful processing time, failed jobs and the indexed version. The acceptable delay is agreed for each source and use case.

## Which sources should be searchable first?

Bring representative files, source locations and your update requirements.

[Plan My Data Pipeline](https://daphnislabs.com/contact?service=ai-data-pipelines-vector-db) [AI Opportunity Finder](https://daphnislabs.com/ai-opportunity-finder)