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# A leading food services provider enables self-service analytics across its business with Sigmoid AnalyticsBot
**URL:** https://www.sigmoid.com/case-studies/a-leading-food-services-provider-enables-self-service-analytics-across-its-business-with-sigmoid-analyticsbot/
Date: 2026-10-01
Author: Joha Momin
Post Type: page
Summary: GenAI Analytics / Food Services / Sigmoid AnalyticsBot A leading food services provider enables self-service analytics across its business with Sigmoid AnalyticsBot...Read More...
---
GenAI Analytics / Food Services / Sigmoid AnalyticsBot
# A leading food services provider enables self-service analytics across its business with Sigmoid AnalyticsBot
Customer support and senior leadership teams held a wealth of operational data across locations and customer segments, yet could not use it without SQL expertise. **Sigmoid AnalyticsBot**, built on AWS with **Amazon Bedrock and Claude 3.5 Sonnet**, now turns plain-English questions into clear summaries and charts, with an average query turnaround of **under 5 minutes**, down from potentially days. Teams can now check metrics across key dimensions such as locations and customer segments in a single conversation, while technical resources focus on more complex analytical work.
**********
Sigmoid AnalyticsBot
Interactive Case Study
Senior leadershipWhich sites served the most meals last month?
**Harbor Stadium** led all sites in August, followed by Airport Terminal 2.
Harbor Stadium**
Airport Terminal 2**
Riverside Hospital**
Northgate University**
SQL by Claude 3.5 SonnetRun on Snowflake
Follow-upHow does that compare with last year?
Built on AWS with Amazon Bedrock
### Business Scenario
Most teams lacked the **SQL skills needed to reach their own data**
Across a large food services operation, the questions that matter most change daily: how a location is performing, which customer segments are growing, where service issues are emerging. The answers existed in the company's database, yet reaching them meant writing SQL, tuning queries and then interpreting the resulting charts. Customer support and senior leadership depended on technical teams for every request, and each new or follow-up question restarted the process. Insight arrived slowly, and technical resources were consumed by routine requests.
### Key Challenges Addressed
C1
Customer support and senior leadership teams could not use the data stored in the database because they lacked SQL expertise.
C2
Understanding the data and interpreting graphs required advanced analytical skills, creating a barrier to broader usage.
C3
Researching the data meant crafting ad-hoc queries by hand, a time-consuming and repetitive process for every new question.
One Business Question: Before vs After
Same question, very different path to the answer
Before: request-drivenPotentially days
Ask
Wait for technical team
Write & tune SQL
Interpret chart
Every follow-up question starts the cycle again.
After: conversational<5 min average
Ask in plain English
Claude writes & checks SQL
Summary + chart
Follow up
Follow-ups continue the same conversation, with context retained.
### Sigmoid's Solution
Built on AWS with **Amazon Bedrock and Claude 3.5 Sonnet**, Sigmoid AnalyticsBot combines RAG, metadata-aware retrieval and natural-language-to-SQL capabilities to make enterprise data accessible through conversational queries. AWS Lambda processes metadata and schema information, while the RAG layer enriches prompts with business context, few-shot examples and domain-specific knowledge. Claude 3.5 Sonnet generates and validates SQL, with an **AI-powered self-correction loop** handling query errors before execution against Snowflake. The resulting data is transformed into **natural-language insights and visualizations**, with conversational memory, caching and Amazon CloudWatch supporting an efficient and observable experience.
How a Question Becomes an Answer
Built on AWS
Core
platform
Amazon Bedrock
Claude 3.5 Sonnet
AWS Lambda
Snowflake
1Ask a question
A support agent or leader types a question in plain English, or a follow-up to the last answer.
Powered by
**Chat interface
**Guardrails
2Add business context
AnalyticsBot finds the relevant tables, business terms and proven example questions, so the query reflects how the business talks.
Powered by
**AWS Lambda: metadata & schema
**RAG knowledge base
**Few-shot examples
3Write the query
Claude reads the enriched question and writes the SQL needed to answer it. No one on the business side touches code.
Powered by
**Claude 3.5 Sonnet
**Amazon Bedrock
4Check and fetch
If a query fails, Claude reads the error, corrects the SQL and retries, then runs it on the company's data.
Powered by
**Self-correction loop
**Snowflake
Up to 2 automatic retries
5Deliver the answer
Results come back as a plain-language summary and a chart, with the SQL available for anyone who wants to check it.
Powered by
**Insight & chart generation
**Conversational memory
Always on: keeps every answer fast, safe and observable
Running
underneath
Query cache
Conversational memory
Guardrails
Amazon CloudWatch
**AWS service
**Claude on Amazon Bedrock
**Client data platform
**Sigmoid AnalyticsBot
### Key Solution Capabilities
#### Ask in natural language
Users ask questions the way they would ask a colleague, and AnalyticsBot returns data insights along with a comprehensive overview.
#### Answers anyone can read
Clear summaries and visual representations of the data remove the need for advanced analytical skills to interpret results.
#### Follow-ups without starting over
Conversational memory carries context between questions, so ad-hoc exploration flows naturally into deeper insight.
### Sigmoid AnalyticsBot, Live
Pick a question below and watch the conversation unfold. Alongside each reply, the **answer trail** shows what happens behind each reply: business context retrieved, SQL written by Claude, errors corrected automatically, and repeated questions served straight from the cache. **Try them in order ↓**
Sigmoid AnalyticsBot Connected to Snowflake
New conversation
Answer trail
Ask a question to see how the answer is built
Generated SQL
```
-- appears here once Claude writes the query
```
**Suggested questions** from customer support and leadership teams
Ask about sites, customer segments, sales or service quality…
↑
### Business Impact
<5min
Average Query Turnaround
Down from potentially days
Customer support and senior leadership teams now get answers through a simple conversation, with **quick check-ins of metrics across key dimensions** such as locations and customer segments, and no dependency on SQL expertise.
### Days → Minutes
#### Faster Time-to-Insight
Questions that once waited in a request queue are answered within the same conversation, including every follow-up.
### Self-Service
#### Analytics Without SQL Expertise
Natural-language analytics puts the database within reach of non-technical teams, with summaries and charts that are easy to interpret.
### About Sigmoid
Sigmoid helps companies modernize data, transform business processes, and make better decisions using AI, to gain competitive advantage. Enterprises are able to unlock new revenue opportunities and improve operational efficiency by partnering with Sigmoid to build bespoke GenAI and agentic AI solutions powered by strong data foundations and contextual knowledge.
[www.sigmoid.com](http://www.sigmoid.com)
### Let's Get Started
Give every team self-service access to enterprise data: conversational analytics on AWS with Amazon Bedrock and Claude, with answers in minutes instead of days.
[Contact Us](/cdn-cgi/l/email-protection#1f727e6d747a6b7671785f6c76787270767b317c7072)
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