Signalpilot-AI customer intelligence

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SignalPilot — Turn Customer Noise Into Your Next Product Decision

Product teams receive thousands of reviews, support tickets, surveys, and feedback messages, but the difficult part is not collecting feedback — it is deciding what actually matters and what to build next.

SignalPilot is an AI-powered customer intelligence and product decision engine that turns raw customer feedback into evidence-backed product decisions.

Instead of simply generating a summary, SignalPilot processes feedback through a six-agent LangGraph pipeline:

Data Analyst → Feedback Intelligence → Customer Segmentation → Root Cause → Product Strategy → Critic

The system automatically analyzes sentiment, discovers recurring themes, segments affected customers, identifies root causes, prioritizes problems, and generates product recommendations. A dedicated Critic/Fact-Checker agent validates the generated claims against the underlying feedback.

Why we built it

We were inspired by a common product problem: teams often have plenty of customer feedback but still rely on manually reading spreadsheets, intuition, or a few highly visible complaints when deciding what to prioritize.

We wanted to build something that answers:

"What should we build next, and can we prove why?"

That led to SignalPilot.

How it works

The workflow is:

Raw feedback → AI analysis → Themes → Problems → Evidence → Recommendations → Roadmap

Users can upload a CSV containing reviews, support tickets, survey responses, or other customer feedback.

SignalPilot then:

• Performs automatic sentiment analysis across feedback.
• Discovers recurring themes using clustering and NLP.
• Identifies customer segments affected by each problem.
• Calculates transparent severity and priority scores.
• Uses RAG-powered semantic search to retrieve relevant customer evidence.
• Generates evidence-backed product recommendations.
• Provides a Copilot interface for asking questions about the dataset.
• Lets product teams organize recommendations into a Now / Next / Later roadmap.

Evidence, not vibes

A core design principle of SignalPilot is that AI-generated insights should be traceable to the underlying customer voice.

Problems and recommendations are linked to supporting feedback, allowing a product manager to inspect the evidence behind an insight instead of blindly trusting an LLM-generated statement.

For example, instead of simply saying:

"Customers are unhappy with the application."

SignalPilot can surface a more actionable insight such as:

"App crashes are a high-severity problem affecting 124 feedback items."

The product team can then inspect the supporting customer feedback behind that conclusion.

What we learned

Building SignalPilot taught us that making an AI system useful is about much more than connecting an LLM to a prompt.

We had to design a pipeline that combines traditional data processing, NLP, vector search, clustering, agent orchestration, deterministic scoring, and AI reasoning.

We also learned the importance of fallbacks and observability. Our embedding pipeline supports a local TF-IDF + SVD fallback when a transformer model cannot be downloaded, allowing the application to remain usable in restricted environments.

Challenges

One of the biggest challenges was preventing generated insights from becoming disconnected from the actual dataset.

We addressed this by combining RAG retrieval with explicit evidence references and a dedicated Critic agent. Priority and severity scores are also calculated using transparent formulas rather than asking an LLM to invent numerical scores.

Another challenge was making the system usable rather than just technically impressive. We therefore built a complete workflow from ingestion through analysis, recommendations, roadmap planning, and conversational exploration.

Built with

Frontend: React, TypeScript, Vite, Tailwind CSS, Recharts Backend: FastAPI, Python, MongoDB/mongomock AI/ML: LangGraph, Sentence Transformers, FAISS, TF-IDF, SVD, KMeans, VADER Architecture: Multi-agent AI pipeline + RAG + evidence retrieval

What's next

Future versions could integrate directly with tools such as Jira, Linear, Zendesk, Intercom, and Slack, allowing SignalPilot to continuously ingest customer feedback and automatically connect validated insights to the product development workflow.

The long-term goal is simple:

Make customer feedback a continuous, evidence-driven product decision system rather than a spreadsheet teams occasionally read.

Built With

• ai
• analytics
• artificial
• data
• faiss
• fastapi
• generative
• intelligence
• langgraph
• learning
• machine
• mongodb
• natural-language-processing
• product
• python
• rag
• react
• sentence
• transformers
• typescript