IDinsight builds pseudo-agent Text-to-SQL pipeline for WhatsApp-based data access (Ask-a-Metric)
IDinsight's simple pipeline for Ask-a-Metric produced insufficiently accurate LLM responses, suffered from brittle prompt engineering where improving one query set hurt another, and had a tightly coupled sequential architecture that made iteration slow.
The CrewAI agentic pipeline answered all test questions correctly but was prohibitively slow and expensive for production: response times exceeded one minute per query and cost approximately USD 0.3 per query, both well above the production targets of under 30 seconds and under USD 0.03.
The pseudo-agent pipeline achieves response times under 15 seconds and costs below USD 0.02 per query while maintaining accuracy, combining the low cost and quick response time of the simple pipeline with the better accuracy of the agentic approach.
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Frequently asked questions
What did this team achieve with this AI workflow?
The pseudo-agent pipeline achieves response times under 15 seconds and costs below USD 0.02 per query while maintaining accuracy, combining the low cost and quick response time of the simple pipeline with the better a…
What tools did this team use?
LLMs, FastAPI, CrewAI, Langchain, RAG, WhatsApp.
What results were reported?
CrewAI response time per query: more than a minute; CrewAI cost per query: approximately USD 0.3; Target response time: less than 30 seconds; Target cost per query: less than USD 0.03 (source-reported, not independently verified).
What failed first in this deployment?
The CrewAI agentic pipeline answered all test questions correctly but was prohibitively slow and expensive for production: response times exceeded one minute per query and cost approximately USD 0.3 per query, both we…
How is this back office ops AI workflow structured?
User submits natural language question → Identify tables and columns → Guardrails check → SQL query execution → Answer returned to user.