DeepPavlov

Load custom NER model trained on financial entities

Ingest communication logs via REST API

Identify sensitive entities and link them to internal compliance IDs

Flag high-risk patterns for human review

Generate structured JSON report of all findings.

Convert internal docs into a SPARQL-queryable knowledge graph

Deploy DeepPavlov KBQA pipeline

Receive natural language questions from employees

Translate questions to SPARQL queries

Return direct, cited answers from the documentation.

Implement DeepPavlov's cross-lingual intent classifier

Detect user intent regardless of input language

Route to language-specific response templates

Use Entity Linking to maintain consistency across languages

Update all language models simultaneously using the same configuration.

Deploy DeepPavlov Medical-NER model

Process clinical notes to identify names, dates, and locations

Mask identified entities with generic labels

Verify de-identification accuracy against a gold-standard set

Output cleaned data for research.

Use Intent Classification to identify 'Product Search'

Use Slot Filling to extract 'color: blue', 'type: dress', 'price_limit: 50'

Query product database with structured parameters

Return ranked results to the user interface.

Implement a 'Dream' agent with skills for different departments

Identify user query department (e.g., DMV, Tax)

Maintain session context across multi-step applications

Provide real-time guidance based on the current step of the user journey.

Ingest social media streams via webhooks

Apply DeepPavlov Sentiment Analysis model to each post

Aggregate sentiment scores by entity (product name, CEO)

Trigger alerts for significant negative shifts in sentiment.