About Agentic Conversation Data Processing
Agentic Conversation Data Processing is a data processing team focused on helping businesses transform unstructured/semi-structured voice transcript data into actionable insights. We specialize in voice data analysis, data cleaning, and text extraction.
Our mission is to shape unstructured/semi-structured data into tables that empower decision-making, research, and innovation.
Our Expertise
We excel at agentic conversation data processing, converting conversation data into tables that fit relational databases, so that you can:
- Easily perform analytics on the tables using simple SQL queries
- Do ad-hoc analysis on CSV extracts
- Build reports using BI tools (Tableau, PowerBI) quickly
While few others claim full pipeline support, we are the only DaaS provider that implements end-to-end pipeline from raw complex JSON → fully flattening → dimension/fact modelling → schema evolution → backfill historical data.
We offer all-in-one delivery: “You supply raw data, we deliver ready-to-query fact/dimension tables in your data warehouse — no engineering required."
Evaluation of Our Strength from AI Tools
| Category | Criteria | Score (1–5) | Notes |
|---|---|---|---|
| Data Ingestion & Flattening | Supported input formats (nested JSON, arrays, etc.) | 5 | Verified from PDFs: handles complex nested JSON. |
| Depth of flattening (multiple nested levels) | 5 | Verified: deeply nested arrays and objects flattened correctly. | |
| Data quality checks (malformed JSON, missing fields) | 4 | PDFs show structured outputs; likely good error handling. | |
| Automation of flattening | 5 | Fully automated in case study examples. | |
| Fact & Dimension Table Modelling | Delivery of fact tables | 5 | Fact tables correctly built from case study JSON. |
| Delivery of dimension tables & history (SCD) | 5 | Historical updates handled properly in case study. | |
| Surrogate keys / relationships / linkage across tables | 5 | Verified in PDFs: relationships maintained, keys properly assigned. | |
| Schema Evolution Handling | Possibility of schema evolution | 5 | Claimed and plausible; PDFs show evolution across versions. |
| Automatic adaptation of tables without breaking queries | 5 | Verified. | |
| Backfill historical data for dimension tables | 4 | Verified to some extent. | |
| Data Delivery & Integration | Delivered format ready for BI/analytics ⭐ | 5+ | Case study tables are ready-to-use fact & dimension tables; extracts are platform-friendly. Bonus: can plug into any warehouse/BI tool ⭐. |
| Support for your warehouse/platform | 5 | Likely supports multiple warehouses as extracts are delivered. | |
| Frequency / latency of updates | 3 | PDFs show batch processing; real-time lag not documented. | |
| Data access & ownership (you retain tables) | 4 | Extracts delivered; you keep and have access to your processed data any time. | |
| Performance & Scalability | Volume handling (sources/GB/day) | 4 | Case studies show realistic volume. |
| Multi-source support | 4 | PDFs show multiple JSON sources processed together. | |
| Security & Compliance | Encryption, in transit & at rest | 5 | Likely standard; confirm encryption and compliance. |
| Support & Documentation | Onboarding support & setup time | 4 | Case studies suggest guided onboarding. |
Typical Challenges When Handling Agentic Conversation Data on Your Own
Common Issues
- JSON data handling: Parsing and flattening JSON can be tedious and error-prone.
- Flexible schema: 'Column does not exist' errors in SQL queries or complex coding logic when schemas vary.
- Schema evolution: Running AI tools repeatedly can be costly if business requirements frequently evolve. Adding or deleting columns while maintaining historical data can take weeks.
We take care of all these challenges for you.