AI Analytics
Dashboards.

AI-Powered Business Intelligence
Custom dashboards built on GPT-4, Claude, and Azure OpenAI that connect your data sources, process the data with AI, and deliver actionable insights in one place. 25+ dashboards deployed. Live in 6-10 weeks.
For operations leaders, data teams, and executives who need decisions backed by evidence, not instinct.

Comparison

Why Custom AI Dashboards Replace Traditional BI.

Power BI and Tableau visualize data. Our AI dashboards understand it. The difference is between a chart that shows what happened and a system that tells you what to do next.

Capability Traditional BI (Power BI, Tableau) Custom AI Dashboard
Data interpretation Manual — analyst required to read and explain charts AI generates written summaries and explanations automatically
Trend detection Relies on visual inspection and human pattern recognition AI identifies patterns continuously and surfaces them proactively
Anomaly alerts Requires manual threshold configuration per metric AI learns normal patterns and flags deviations automatically
Recommendations None — describes what happened, not what to do AI suggests specific actions with supporting data
Querying SQL, DAX, or complex filter configuration Plain English questions answered instantly
Reporting Scheduled exports requiring manual review and commentary AI-generated reports with commentary delivered automatically
License model Per-seat licensing with annual renewals Custom-built — you own it outright
Customization Limited to platform templates and widgets Every element built around your specific decisions
Setup time Weeks of configuration, still limited by platform constraints 6-10 weeks — fully custom, no platform constraints

Real Deployments

Dashboards Delivering Measurable Results.

Every AI dashboard is built around a specific business decision. These are real deployment patterns with measurable outcomes from similar engagements.

E-Commerce Operations

Multi-channel Revenue Dashboard

Unified Shopify, Amazon, and wholesale data with AI-generated daily summaries and inventory reorder recommendations.

Saved in manual reporting 15 hrs/wk
SaaS Executive Team

Revenue and Churn Intelligence

Connected Stripe, HubSpot, and Intercom into a single view with AI-powered churn prediction and expansion revenue identification.

Detection of at-risk accounts 23% earlier
Marketing Team

Multi-channel Performance Reporting

Automated branded dashboards pulling from Google Ads, Meta, and Google Analytics with AI-written monthly commentary.

Saved per monthly reporting cycle 8 hrs/client

AI Capabilities

Six AI Capabilities Built Into Every Dashboard.

This is not visualization. AI processes your raw data into answers before it reaches the dashboard. Every function runs automatically on your data refresh schedule.

01

Summarization

Condense thousands of data points into clear, readable summaries that update automatically. No more exporting to spreadsheets and writing commentary by hand.

  • Auto-generated narrative insights
  • Period-over-period comparison text
  • Executive-ready language
Example: “Revenue is up 12% this quarter, driven primarily by enterprise accounts in the Northeast region.”
02

Trend Analysis

Identify patterns over time that humans miss. The AI monitors your data continuously and surfaces trends as they develop, not after they have already played out.

  • Seasonal pattern recognition
  • Emerging trend alerts
  • Multi-variable correlation
Example: “Support tickets spike every Monday morning. Staffing adjustment recommended.”
03

Anomaly Detection

Flag unusual data points automatically the moment they appear. Instead of discovering budget overruns or traffic drops days later, you get alerted in real time.

  • Learned baseline thresholds
  • Real-time deviation alerts
  • Severity classification
Example: “Marketing spend increased 340% yesterday. This deviates from the monthly pattern.”
04

Recommendations

Suggest specific actions based on data patterns. The AI does not just describe what happened. It tells you what to do next, with supporting data behind each recommendation.

  • Data-backed action items
  • Impact estimates per recommendation
  • Priority-ranked suggestions
Example: “Based on conversion rates, increase budget allocation to Google Ads by 15%.”
05

Natural Language Queries

Ask questions in plain English and get immediate answers. Anyone on your team can query the dashboard without writing formulas, SQL, or building custom reports.

  • Conversational data access
  • Zero technical knowledge required
  • Instant chart generation from questions
Example: “What was our best-performing product last quarter by profit margin?”
06

Automated Reporting

Scheduled email summaries, Slack notifications, and PDF reports generated automatically. Configured to match your team’s cadence and delivered without anyone pressing a button.

  • Email, Slack, and in-app delivery
  • AI-written commentary included
  • Configurable schedules and recipients
Example: “Every Monday at 8am, your team receives a performance summary with highlighted action items.”

Integrations

Data Sources We Connect.

Every dashboard starts with clean, reliable data connections. We integrate with your existing platforms via secure API connections and build transformation layers to normalize data across sources.

Accounting and Finance

QuickBooks, Xero, FreshBooks, Stripe, PayPal

  • Revenue, expenses, cash flow
  • Invoice status and payment trends
  • Automated reconciliation data

CRM and Sales

Salesforce, HubSpot, Pipedrive, Zoho

  • Pipeline data and deal stages
  • Contact activity and engagement
  • Revenue forecasts and win rates

Marketing

Google Ads, Meta Ads, Mailchimp, SEMrush, Google Analytics

  • Campaign performance and spend
  • Conversions and attribution
  • Audience metrics and engagement

E-commerce

Shopify, WooCommerce, BigCommerce

  • Orders, revenue, product performance
  • Cart abandonment and conversion funnels
  • Inventory levels and reorder alerts

Project Management

Asana, Monday.com, Jira, ClickUp

  • Task completion rates and velocity
  • Resource utilization and blockers
  • Sprint and milestone tracking

Custom and Legacy

REST APIs, databases, Google Sheets, Excel, CSV imports

Process

From Discovery to Live Dashboard In Four Phases.

Your involvement: access to data source credentials, 2-3 stakeholder sessions for KPI definition and review, and feedback during testing. Total time commitment: approximately 4-6 hours.

1

Discovery and Data Mapping

Identify data sources, define KPIs, map user roles and access levels. We audit your existing reporting workflow and identify the highest-impact metrics for each stakeholder. Deliverable: dashboard specification with wireframes.

2

Data Pipeline and AI Layer

Connect all data sources via secure API. Build transformation layers to normalize data. Configure AI models for summarization, trend analysis, anomaly detection, and natural language queries on your historical data.

3

Dashboard Build

Build the custom interface, interactive visualizations, role-based views, and export capabilities. Every dashboard is responsive and works across desktop, tablet, and mobile. Deployed to staging for team review.

4

Testing and Deployment

Validate data accuracy against source systems. Test with real users across all roles. Refine AI output quality and notification thresholds. Deploy to production with user accounts, training, and documentation.

Technology

Technology Stack Behind Every Dashboard.

Built with the same custom AI architecture we use across all our AI services. Every component is selected for reliability, performance, and extensibility.

AI

OpenAI GPT-4

Natural language understanding, data summarization, written recommendations, and conversational query responses.

C

Anthropic Claude

Complex data analysis, long-context processing, and structured output generation for detailed financial and operational insights.

R

React and D3.js

Custom interactive visualizations, responsive layouts, drill-down capabilities, and real-time data rendering without third-party BI platform dependencies.

PG

PostgreSQL and Redis

Relational data storage for processed insights, caching layers for real-time dashboard performance, and time-series data handling for trend analysis.

Requirements

What We Need To Get Started.

Scope and investment depend on data sources, AI processing complexity, and visualization requirements. We provide a detailed estimate after the discovery session.

1

Data Source Access

API credentials or admin access to the platforms you want connected. If a platform does not support API access, we work with database exports or scheduled CSV feeds.

2

KPI Definitions

A list of the key metrics and decisions each stakeholder needs from the dashboard. We help define these during discovery if your team does not have them documented.

3

Stakeholder Availability

Two to three sessions during discovery and testing for KPI validation, wireframe review, and user acceptance testing. Approximately 4-6 hours total over the project lifecycle.

4

Hosting Preference

We offer managed hosting or deployment to your own infrastructure — AWS, Google Cloud, Azure, or internal servers. Both options include ongoing monitoring.

In Practice

Real Dashboards. Real Clarity.

Every dashboard we build replaces a manual reporting process. Here are three that changed how teams make decisions.

eCommerce — Executive Revenue Dashboard

The problem: An eCommerce company pulled data from Shopify, Google Analytics, Meta Ads, and Klaviyo into spreadsheets every Monday morning. The CEO spent 2 hours compiling a “weekly snapshot” that was outdated by the time it was shared. Different teams used different definitions of “revenue” and “conversion rate.”

What we built: Real-time executive dashboard combining all four data sources. Unified metrics with consistent definitions. AI-generated anomaly detection that highlighted unusual trends — revenue spikes, traffic drops, ad spend deviations — before anyone had to look for them. Daily email digests with the top 3 things to pay attention to.

The outcome: Monday reporting meetings dropped from 60 minutes to 15 minutes. The CEO caught a shipping cost anomaly within 24 hours that would have gone unnoticed for weeks. Cross-team arguments about metrics ended because everyone referenced the same dashboard with the same definitions.

SaaS — Customer Health Scoring

The problem: A B2B SaaS company with 400 accounts tracked customer health through quarterly business reviews — but QBRs only covered 20% of the customer base. The remaining 80% were invisible until they churned. Support tickets, usage data, and billing history existed in separate systems with no unified view.

What we built: AI-powered customer health dashboard that scored every account daily based on login frequency, feature adoption, support ticket sentiment, billing patterns, and contract renewal dates. Accounts were categorized as healthy, at-risk, or critical. Automated alerts notified CSMs when an account’s health score dropped below threshold.

The outcome: Churn prediction accuracy reached 84%. CSMs intervened on at-risk accounts an average of 45 days earlier. Net revenue retention improved from 95% to 103% within two quarters. The VP of Customer Success used the dashboard in board meetings to quantify customer health across the entire portfolio.

Marketing Team — Multi-channel Performance

The problem: A marketing team running campaigns across Google Ads, LinkedIn, Meta, email, and SEO compiled performance data in spreadsheets at month-end. Each channel had its own dashboard. Nobody could answer the simple question: “Which channel is driving the most qualified pipeline?” without 4 hours of data work.

What we built: Unified marketing performance dashboard with attribution modeling. AI summarized weekly trends in plain language — “LinkedIn CPL increased 22% this week driven by a creative fatigue signal on the top-performing ad.” Drill-down views for each channel. Executive summary view for leadership with pipeline contribution by channel.

The outcome: Monthly reporting time dropped from 16 hours to zero — the dashboard was always current. The team reallocated $18,000/month from underperforming channels to high-performing ones within the first quarter. Marketing-attributed pipeline grew 28% because budget decisions were made weekly instead of monthly.

Tell Us What Data You Need to See Clearly.

25+ AI dashboards deployed across executive, sales, marketing, and operations teams. Describe your data sources and the insights you need — we will design the dashboard.

  • Share your data sources, KPIs, and reporting needs
  • We design the architecture, data pipeline, and wireframes
  • Custom AI dashboard deployed in 6-10 weeks

FAQ.

Power BI and Tableau are visualization tools that require manual configuration, query writing, and interpretation. Our AI dashboards are custom-built applications where AI processes your data automatically — summarizing findings, detecting trends, flagging anomalies, and delivering written recommendations. You get intelligence, not just charts. The AI layer also supports natural language queries, so anyone on your team can ask questions in plain English without writing formulas or SQL.

Any platform with API access or database connectivity. This includes accounting tools like QuickBooks and Xero, CRMs like Salesforce and HubSpot, marketing platforms like Google Ads and Meta Ads, e-commerce platforms like Shopify and WooCommerce, and project management tools like Asana and Jira. For legacy systems or proprietary platforms, we build custom integration connectors.

The AI layer performs six core functions: summarization (condensing data points into readable insights), trend analysis (identifying patterns over time), anomaly detection (flagging unusual data points automatically), recommendations (suggesting specific actions based on data patterns), natural language queries (letting you ask questions in plain English), and automated reporting (generating scheduled summaries delivered via email or Slack). Each function runs automatically on your data refresh schedule.

Most AI dashboards take 6-10 weeks from discovery to deployment. Simpler dashboards with fewer data sources and standard visualizations can be delivered in 4-6 weeks. The timeline depends on the number of data source integrations, complexity of the AI processing layer, and the level of custom visualization required. We provide a detailed project timeline during the discovery phase.

We offer both managed hosting and deployment to your own infrastructure. With managed hosting, we handle server management, uptime monitoring, SSL, and backups. If you prefer your own infrastructure, we deploy to AWS, Google Cloud, Azure, or your internal servers. Both options include ongoing monitoring and maintenance to ensure data connections stay healthy.

Yes. Every dashboard includes role-based access controls with configurable permission levels. Typical setups include admin roles with full access and configuration rights, manager roles with department-level views, and viewer roles with read-only access. Each role sees only the data and insights relevant to their function. We can also create custom roles based on your organizational structure.

Yes. Every dashboard is designed to be extensible from day one. New data sources can be connected at any time without rebuilding the dashboard. We architect the data pipeline layer to accommodate additional integrations, so adding a new CRM, marketing platform, or custom database is a straightforward process that typically takes 1-2 weeks.

Scope and investment depend on the number of data sources, complexity of the AI processing layer, and level of custom visualization required. A focused single-department dashboard with 2-3 data sources has a different scope than an enterprise dashboard with multiple departments and advanced AI features. We provide a detailed scope and investment estimate after the discovery session. Take the AI Readiness Assessment to understand what level of investment makes sense for your situation.

Most clients see measurable ROI within 60-90 days of deployment. The primary savings come from eliminating manual reporting time (typically 10-15 hours per week), faster decision-making through automated insights, and identifying revenue opportunities or cost inefficiencies that were previously hidden in scattered data. Clients typically report 40-60% reduction in reporting time and 15-25% faster response to business changes.

All data connections use encrypted API calls with OAuth 2.0 authentication where supported. Data in transit is protected by TLS 1.2+ encryption, and data at rest uses AES-256 encryption. We follow the principle of least privilege for all access controls, and dashboards support role-based access to ensure team members only see data relevant to their function. We do not store raw data from your systems — we process it in real time and cache only the insights and aggregations needed for display.