🚀 Tenzor Insights

Turn raw data into business intelligence — instantly.

Tenzor Insights is an AI-powered business intelligence engine for CRM, ERP, and any structured data system.
It transforms activity logs, historical reports, and arbitrary JSON datasets into actionable insights, trend analyses, and decision-ready summaries — powered by OpenAI, Gemini, and Claude.

✨ Features

| Feature | Description | |---|---| | 🧠 AI-Powered Analysis | Transforms raw logs into executive-level business insights | | 📊 Trend Intelligence | Compares reports across time to detect business direction | | 🔀 Hybrid Intelligence | Analyzes any structured JSON data without a predefined schema | | ⚠️ Risk Detection | Identifies anomalies, bottlenecks, and data quality issues | | 📈 Growth Signals | Surfaces pipeline movement, conversions, and stagnation signals | | 🤖 Multi-Provider | Supports OpenAI (GPT), Google Gemini, and Anthropic Claude | | 🧩 Variant Modes | ADVISOR, OPERATOR, and EXECUTIVE analytical perspectives |

🧠 Modes

Tenzor Insights operates in three intelligent modes. Choose the one that fits your use case.

1. 📋 Analysis Mode

Analyze structured activity logs and generate business intelligence reports.

import { analyzeLogs } from "tenzor-insights";

const report = await analyzeLogs({
  mode: "ANALYSIS",
  provider: "gemini",
  apiKey: process.env.API_KEY,
  model: "gemini-2.0-flash",
  variant: "ADVISOR",   // "ADVISOR" | "OPERATOR" | "EXECUTIVE"
  logs: [ /* your activity logs array */ ],
  limit: 200            // optional — default: 200
});

What you get: Summary, priority scores, business impact, growth signals, risks, AI suggestions, notable users, trend indicators, and an executive insight.

2. 🔁 Compare Mode

Compare multiple time-period reports to detect business trends and direction.

import { analyzeLogs } from "tenzor-insights";

const report = await analyzeLogs({
  mode: "COMPARE",
  provider: "gpt",
  apiKey: process.env.OPENAI_API_KEY,
  model: "gpt-4.1-mini",
  variant: "EXECUTIVE",
  reports: [
    { period: { startDate: "2026-03-01", endDate: "2026-03-07" }, report: { /* week 1 report */ } },
    { period: { startDate: "2026-03-08", endDate: "2026-03-14" }, report: { /* week 2 report */ } }
  ]
});

What you get: Trend analysis across periods, improvements, declines, persistent issues, new risks, business trajectory verdict, confidence score, and recommendations.

3. 🔀 Hybrid Mode

Analyze any structured JSON dataset — no schema required.
Tenzor Insights will automatically infer the domain, detect entities and patterns, identify anomalies, and generate targeted business or operational insights.

import { analyzeLogs } from "tenzor-insights";

const report = await analyzeLogs({
  mode: "HYBRID",
  provider: "gemini",
  apiKey: process.env.API_KEY,
  model: "gemini-2.0-flash",
  data: [ /* any JSON array or object */ ],
  contextHint: "Optional: brief description of what this data represents"
});

What you get: Detected domain, entity map, key insights, patterns, time-based trends, anomalies, recommendations, data quality score, and a confidence score.

How Hybrid Intelligence Works

Hybrid mode executes a 5-phase intelligence pipeline internally:

Phase 1 → Understand the dataset (entities, fields, relationships)
Phase 2 → Infer the domain (sales, operations, HR, finance, marketing …)
Phase 3 → Detect patterns, time trends, growth/decline, and anomalies
Phase 4 → Generate high-quality, data-specific insights and recommendations
Phase 5 → Assign a confidence score (0.0 – 1.0) based on data richness

Supported domains detected automatically: sales · operations · hr · finance · marketing · product · logistics · general · unknown

💡 contextHint is optional but helps the engine orient faster. If the hint conflicts with the actual data structure, the engine will override it and explain the discrepancy.

📦 Installation

npm install tenzor-insights

⚙️ Configuration

| Option | Type | Required | Description | |---|---|---|---| | mode | string | ✅ | "ANALYSIS" | "COMPARE" | "HYBRID" | | provider | string | ✅ | "gpt" | "gemini" | "claude" | | apiKey | string | ✅ | API key for the chosen provider | | model | string | ✅ | Model name (e.g. "gpt-4.1-mini", "gemini-2.0-flash") | | variant | string | ❌ | "ADVISOR" | "OPERATOR" | "EXECUTIVE" — default: "ADVISOR" | | logs | array | ⚠️ | Required for ANALYSIS mode | | limit | number | ❌ | Max logs to process in ANALYSIS mode — default: 200 | | reports | array | ⚠️ | Required for COMPARE mode | | data | any | ⚠️ | Required for HYBRID mode — any JSON array or object | | contextHint | string | ❌ | Optional domain hint for HYBRID mode |

📥 Sample Input

Analysis Mode

[
  {
    "action": "CREATE",
    "entity": "lead",
    "timestamp": "2026-03-01T10:00:00Z",
    "actor": { "name": "John", "role": "Sales" }
  },
  {
    "action": "UPDATE",
    "entity": "quote",
    "timestamp": "2026-03-01T11:30:00Z",
    "actor": { "name": "Ali", "role": "Sales" }
  }
]

Hybrid Mode

Pass any structured dataset — the engine figures out the rest:

[
  { "order_id": "1001", "customer": "Jane", "total": 299.00, "status": "shipped", "created_at": "2026-03-01" },
  { "order_id": "1002", "customer": "Mark", "total": 149.50, "status": "pending", "created_at": "2026-03-02" }
]

📤 Sample Output

Analysis Mode Output

{
  "summary": "Low system activity detected with only 3 actions recorded, indicating potential sales stagnation.",
  "priorityScore": { "growth": 7, "risk": 2, "operations": 6 },
  "keyHighlights": [
    "One new lead created — positive pipeline signal.",
    "One quote updated — progress in an existing deal."
  ],
  "businessImpact": [
    "Extremely low activity volume suggests a bottleneck in the sales pipeline."
  ],
  "growthSignals": ["New lead created — potential future conversion."],
  "risksOrConcerns": [
    { "issue": "Critically low activity across all users.", "severity": "MEDIUM" }
  ],
  "aiSuggestions": [
    { "suggestion": "Investigate reasons for low system engagement.", "priority": "HIGH" }
  ],
  "ownerFocus": ["Drive consistent team utilization across all pipeline stages."],
  "notableUsers": ["Ali: 2 of 3 actions — single-user dependency risk."],
  "metrics": { "actionsCount": 3, "activeUsers": 2 },
  "trend": {
    "activityLevel": "LOW",
    "growthDirection": "STABLE",
    "riskLevel": "MEDIUM",
    "operationalHealth": "UNSTABLE"
  },
  "executiveInsight": "Current low activity levels present a direct risk to revenue growth if left unaddressed."
}

Hybrid Mode Output

{
  "detectedDomain": "operations",
  "summary": "Dataset contains 1 order record spanning a medical e-commerce workflow. The order lifecycle is complete but abnormal re-engagement after 77 days and late-stage marketing removal events are key concerns.",
  "entities": [
    {
      "type": "order",
      "count": 1,
      "keyFields": ["order_id", "order_total", "shipping_country"],
      "notes": "Single order with full lifecycle tracked via nested timeline."
    }
  ],
  "keyInsights": [
    {
      "insight": "Order completed full lifecycle from checkout to shipment within 4 days — efficient fulfillment.",
      "category": "operational"
    },
    {
      "insight": "Customer re-engaged 77 days post-shipment, triggering marketing removal — churn signal.",
      "category": "business"
    }
  ],
  "patternsDetected": [
    { "pattern": "Repeated member logins within short windows suggest engagement but possible confusion.", "significance": "MEDIUM" }
  ],
  "trends": {
    "hasTimestamps": true,
    "timeRange": "2026-01-18 to 2026-04-07",
    "direction": "STABLE",
    "peakPeriod": "2026-01-20",
    "notes": "Most activity concentrated in first 4 days; long gap before customer re-engagement."
  },
  "anomalies": [
    { "description": "ORDER_WELCOME_SMS sent with null order_id — broken event linkage.", "severity": "HIGH", "affectedRecords": 1 }
  ],
  "recommendations": [
    {
      "action": "Fix null order_id in SMS events to ensure accurate tracking.",
      "priority": "HIGH",
      "rationale": "Broken event linkage compromises downstream analytics and customer communication attribution."
    }
  ],
  "metrics": { "totalRecords": 1, "timelineEvents": 33 },
  "dataQuality": { "score": 0.72, "issues": ["Null order_id in 2 external events", "Empty reference_id fields"] },
  "confidence": 0.78
}

🤖 Supported Providers

| Provider | Parameter | Example Models | |---|---|---| | OpenAI | "gpt" or "openai" | gpt-4.1, gpt-4.1-mini, gpt-4o | | Google Gemini | "gemini" | gemini-2.0-flash, gemini-1.5-pro | | Anthropic Claude | "claude" | claude-3-5-sonnet, claude-3-haiku |

🎯 Use Cases

| Mode | Use Case | |---|---| | ANALYSIS | CRM log analysis, ERP activity monitoring, sales pipeline review, team performance tracking | | COMPARE | Week-over-week trend reporting, sprint retrospectives, monthly business reviews | | HYBRID | Order intelligence, support ticket analysis, logistics monitoring, HR data review, any custom JSON dataset |

🚀 Why Tenzor Insights?

Most analytics tools show you data. Tenzor Insights tells you what it means.

| Without Tenzor Insights | With Tenzor Insights | |---|---| | Raw logs | Executive-ready insights | | Numbers | Business context | | Activity data | Growth signals & risk flags | | Unknown JSON | Auto-detected domain intelligence |

🛣️ Roadmap

• 🔮 Predictive insights and forecasting
• 📊 Dashboard & BI tool integrations
• ⚡ Real-time alerting
• 📅 Scheduled intelligence reports (weekly / monthly)
• 🔗 Native CRM connectors (Salesforce, HubSpot)

📄 License

ISC © Greyloops