Technical Deep Dive16 min read
ReAct Framework for Manufacturing AI
Pure LLMs can generate text, but manufacturing needs AI that retrieves real data, executes calculations, and produces verifiable results.
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Technical Deep DiveNovember 28, 202518 min read
Why Pure LLMs Fall Apart in Manufacturing—and How Rishi's Multi-Model AI Architecture Turns Insight Overload Into Instant, Actionable Answers
Operational teams spend hours hunting for answers across dashboards. Rishi delivers instant, accurate insights through specialized agents, RAG, and guardrails built for manufacturing precision.
The Hidden Cost: Hours Lost in Dashboard Chaos
Every day, operational teams face the same frustrating reality: the knowledge they need exists, but finding it is a time-consuming nightmare. Plant managers, process engineers, and quality analysts spend hours hunting for answers across:
- 6-12 different dashboards for historian data, PLC logs, SPC sheets
- Manual CSV exports into Excel for correlation analysis
- Stitching reports from multiple industrial platforms
- Cross-referencing cycle time, quality metrics, alarms, and stoppages
- Manually rechecking numbers before making critical decisions
The core problem: Knowledge exists, but it's scattered across systems, formats, and interfaces. Getting answers is slow, tedious, and error-prone. GenAI promised instant insights—but pure LLMs aren't built for structured data, manufacturing semantics, or analytics precision.
Rishi was built specifically to close this gap.
Why Pure LLMs Fail at Simple Manufacturing Questions
Every plant asks variations of the same questions:
- "Why did Line 3 slow down yesterday?"
- "What caused the spike in defects on SKU 127B?"
- "Which machines will hit maintenance thresholds first?"
- "Show me throughput drift across the last 10 shifts."
A generic LLM cannot answer these questions reliably. Here's why:
Cannot Read or Calculate From Raw Production Data
Cycle time scatter? PID controller logs? Weld signals? LLMs hallucinate when forced into analytical reasoning with numerical data. They can describe patterns but cannot compute them accurately.
Does Not Understand Factory Semantics
Stations, rework loops, buffer logic, upstream/downstream relationships—these manufacturing concepts are not in generic LLM training data. They lack the domain knowledge needed for contextual understanding.
Cannot Navigate Complex Data Systems
Ask ChatGPT to "pull downtime data, correlate with stop events, and group by SKU"—and it invents outputs. It cannot actually execute queries or access real production systems.
Cannot Build Code, Graphs, or Dashboards Grounded in Real Data
It can describe a graph, but cannot produce a correct one without structured context and validation. There's no connection between the LLM's output and actual production metrics.
LLMs can talk, but manufacturing needs AI that thinks, computes, retrieves, correlates, and guarantees correctness.
Rishi's Solution: One Intelligent Layer, One Accurate Answer
Rishi acts as a single intelligent layer over all your production data:
Raw machine data
Structured historian signals
Industrial platform events
Shift logs and manuals
Users no longer jump between dashboards. They simply ask: "Rishi, what's the root cause of yesterday's downtime on Line 4?"
Rishi's Process:
- Retrieves the right data using RAG (Retrieval-Augmented Generation)
- Executes analytical queries via ReAct framework
- Calls relevant domain-specific agents
- Runs calculations (cycle time variance, drift patterns, correlation)
- Generates visualizations and graphs
- Optionally creates code, dashboards, or exports
- Returns a grounded, verifiable answer
What once took hours now takes seconds.
Supervisor-Agent Architecture: Domain Intelligence, Not Generic Reasoning
Rishi orchestrates a hierarchy of specialized agents, each expert in their domain:
Downtime Agent
Understands stoppage patterns, OEE loss, and root-cause trees
Quality Agent
Inspects FPY, defect clusters, and SPC drift patterns
Throughput Agent
Models takt time, bottlenecks, and WIP distribution
Process-Drift Agent
Identifies cycle-time deviations and tool wear signatures
Data Engineering Agent
Generates SQL and Python for custom reports
Visualization Agent
Builds graphs, dashboards, and data exports
A Supervisor Agent routes requests, validates steps, and ensures correctness. This structure prevents hallucinations and ensures every answer is grounded in domain-specific logic.
ReAct: Turning LLM Guesses Into Deterministic Workflows
Rishi uses the ReAct (Reason + Act) framework so the system operates deterministically:
1
Reasons
Identifies what data or tool it needs for the query
2
Acts
Queries pipelines, executes code, retrieves tables
3
Reasons Again
Interprets actual numbers from real data
4
Generates Output
Provides grounded answer with charts and exportable datasets
Example: Plot cycle-time drift for Machine 12 and correlate with stop events
Pure LLM:
Hallucinates a generic response
Rishi:
- Executes SQL on historical data
- Computes statistical trends
- Generates accurate graph
- Explains insights with data backing
- Provides exportable dataset
This is AI that performs, not invents.
RAG + LangGraph: Ensuring Accuracy at Scale
RAG for Manufacturing
Rishi retrieves from multiple structured and unstructured sources:
Structured time-series data
Semi-structured logs
SOPs and manuals
Industrial platform tables
KPI calculation templates
Graph schemas of production lines
Result: Factual accuracy, deterministic data retrieval, and zero hallucinated numbers.
LangGraph for Reliable Multi-Model Flows
Rishi uses LangGraph to orchestrate complex workflows:
- Build stateful workflows that maintain context
- Chain LLM reasoning with analytic models
- Validate intermediate results before proceeding
- Retry on error with fallback strategies
- Route tasks to the correct specialized agent
- Manage tool-calling and data dependencies
- Combine multiple models (LLM + ML + statistical engines)
End result: A resilient, fault-tolerant AI system designed for industrial-grade reliability.
Guardrails: Eliminating Hallucinations at the Root
Rishi embeds guardrails at every layer to ensure data integrity and output accuracy:
Schema Validation
Ensures data structures match expected formats
Data Range Checks
No negative cycle times or impossible values
Required Field Enforcement
All critical data points must be present
Strict Tool-Calling Rules
Agents only access authorized tools
Self-Critique Loops
System validates its own outputs before delivery
Confidence Scoring
Transparency about certainty levels
Cross-Model Verification
Multiple agents confirm critical findings
Supervisor Validation
Final reasoning checks by supervisor agent
If data is missing or uncertain, Rishi doesn't "guess"—it alerts the user with context about what's needed.
Rishi's Capabilities That Transform Manufacturing Operations
Instant Answers
One natural-language question → one correct answer. No dashboard hunting.
Automated Graph Creation
Cycle-time plots, downtime Pareto, heatmaps, throughput curves—all AI-generated.
Code Generation
Rishi produces SQL, Python, or scripts to accelerate engineering tasks.
Dashboard Building on Demand
Tailored dashboards for specific questions, not generic templates.
Data Export & Report Generation
CSV exports, shift reports, KPI summaries, and correlation datasets on demand.
High-Volume Data Pipeline
Built to handle millions of OT datapoints per hour with optimized query performance.
Real-World Example: Pure LLM vs. Rishi
User Question: "Why did FPY drop on Line 2 yesterday?"
Pure LLM Response
"FPY can drop due to operator training, machine failures, material issues..."
→ Useless. Generic. Fabricated.
Rishi Response
- • Pulls defect log: 142 defects, mostly torque failures on Station 7
- • Correlates with cycle-time drift: +11% variance
- • Notes tool-wear pattern from Z-axis temp data
- • Detects anomaly starting 14:12 across SKU-287 series
- • Generates graph showing defect clustering
- • Exports dataset for further analysis
- • Suggests probable cause: tool degradation
- • Provides recommended corrective actions
→ Correct. Actionable. Data-backed.
This is the future of operational intelligence.
The Bottom Line
Manufacturing teams don't need more dashboards, more spreadsheets, or more reports. They need one place to ask a question and get a correct, data-backed answer instantly.
Rishi delivers that through a multi-model, agent-driven, guardrailed AI architecture that understands the physics, logic, and data structures of real factories—not hypothetical ones.
Rishi isn't a chatbot.
Rishi is an AI-native operational partner for production lines.
Everything You Need to Know
How is Rishi different from ChatGPT or other generic LLMs?
Generic LLMs can chat but cannot access, compute, or validate manufacturing data. Rishi uses specialized agents, RAG for data retrieval, ReAct for deterministic workflows, and guardrails to ensure every answer is grounded in real production data with zero hallucinations.
What types of manufacturing data can Rishi analyze?
Rishi handles structured time-series data, historian signals, industrial platform events, PLC logs, semi-structured shift logs, unstructured manuals and SOPs, and millions of OT datapoints per hour through optimized data pipelines.
How does Rishi prevent AI hallucinations?
Rishi embeds guardrails at every layer including schema validation, data range checks, required field enforcement, self-critique loops, confidence scoring, cross-model verification, and supervisor agent validation. If data is missing, Rishi alerts the user instead of guessing.
Can Rishi generate code and custom dashboards?
Yes. Rishi's Data Engineering Agent can generate SQL and Python code for custom reports, while the Visualization Agent builds tailored dashboards, graphs, and data exports (CSV, shift reports, KPI summaries) based on your specific questions.
What is the ReAct framework and why does it matter?
ReAct (Reason + Act) is a framework that makes Rishi deterministic rather than<bos>-is-probabilistic. It reasons about what data is needed, acts by querying systems and executing code, reasons again with actual data, and generates grounded answers. This turns guesses into verified results.
How quickly can Rishi analyze production issues?
What previously took hours of dashboard hunting and manual correlation now takes seconds. Rishi retrieves relevant data, performs analysis, correlates patterns, generates visualizations, and delivers actionable insights in real-time.
What are specialized agents and why are they important?
Specialized agents (Downtime, Quality, Throughput, Process-Drift, Data Engineering, Visualization) are domain experts focused on specific manufacturing challenges. A Supervisor Agent coordinates them to solve complex problems that require multiple types of expertise, preventing the generic responses typical of single-model systems.
Can Rishi handle high-volume production data?
Yes. Rishi's data pipeline is built for industrial scale, handling millions of OT datapoints per hour with scalable ingestion, intelligent semantic modeling, schema-aware time-series storage, and optimized query performance for fast, accurate responses.
About the Author
AK
Atul Khiste
Head of Product
Atul is a seasoned product leader with deep experience in manufacturing and technology. He has built and scaled innovative SaaS products into multi-million dollar businesses through data-driven decision-making and a strong focus on customer needs, consistently delivering successful launches and significant business impact.
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