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Analytics

Predictive Analytics Consulting and AI Reporting for Operations

Operational data lives in a CRM, an ERP, a scheduling tool, three spreadsheets, and a phone system. We assemble it into dashboards that update themselves, forecasts and alerts that arrive before the problem, reports written automatically, and an assistant that answers questions about the business with the numbers behind the answer.

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Who This Is For

Built for leaders who assemble reports by hand

  • Operations and finance teams building weekly reports from exports and spreadsheets.
  • Multi-location or multi-company operators who need consistent KPIs across units.
  • Businesses with call, ticket, and document volume that nobody analyzes.
  • Leaders who want to ask a question about the business and get an answer with sources, not a request to the analyst.

What We Build

From scattered data to decisions

Operational dashboards

KPIs assembled from CRM, ERP, scheduling, telephony, and spreadsheets into dashboards that refresh automatically.

Forecasts and anomaly detection

Demand, capacity, cash, and workload forecasts, with alerts when a metric moves outside its expected range.

Automated reports

Weekly and monthly reports written by AI from the data, with variances explained and exceptions highlighted for review.

Analytics assistant

Questions in plain language answered from your data with the query and sources shown, for leaders and managers.

Conversation analytics

Calls, chats, and tickets analyzed for intents, outcomes, sentiment, and issues, feeding operations and product decisions.

Data foundation

Pipelines, a warehouse or PostgreSQL models, definitions, and quality checks so every number means one thing.

Reporting is a workflow, and it can be automated

The Friday report takes a day because it lives in exports and spreadsheets. AI changes two things: the assembly can be automated end to end, and the narrative, what changed and why, can be written from the data with exceptions highlighted for a person to check. The result is visibility without the manual work, and time for the decisions the numbers point to.

What we build

A data foundation with agreed definitions and quality checks; dashboards that refresh from your systems; forecasts and alerts for demand, capacity, and workload; automated weekly and monthly reports; an analytics assistant that answers plain-language questions with the query and sources shown; and conversation analytics over calls, chats, and tickets.

Trust in the numbers

Every metric has one definition, every answer shows its sources, forecasts are scored against actuals, and pipelines are monitored for freshness and quality. Your data stays in your database or warehouse with access controls, and models can run privately.

What you measure

Hours returned from report assembly, time to answer a business question, forecast accuracy, alert precision, and the decisions changed by earlier visibility, reviewed monthly.

How It Works

How an analytics deployment runs

  1. Questions and sources

    The decisions you make weekly, the metrics behind them, and where the data lives today.

  2. Data foundation

    Pipelines and models built with definitions and quality checks, reviewed with the owners of each source.

  3. Dashboards, reports, assistant

    Delivered in increments, each validated against the manual numbers before it replaces them.

  4. Operate and expand

    Monitoring of pipelines and data quality, new sources and questions added, monthly review of usage.

Stack & Integrations

Analytics stack and sources

PostgreSQL, data warehouses, Redis CRM, ERP, scheduling, and telephony APIs Spreadsheets and shared drives Power BI, Looker, Metabase Google Analytics 4 OpenAI, Anthropic Claude, AWS Bedrock, self-hosted models Python, Node.js, Docker, Kubernetes, AWS

Industries

Analytics by industry

Engagement Models

Engagement models

Analytics assessment

Decisions, metrics, sources, and gaps mapped, with the first dashboards and reports scoped.

Build

Fixed-scope delivery of the data foundation and the first dashboards, reports, and assistant.

Managed analytics

Ongoing pipeline monitoring, data quality, new sources and questions, and monthly reviews.

FAQ

Questions about AI analytics

Do we need a data warehouse first?

Not necessarily. Many businesses start with PostgreSQL models over their existing systems and spreadsheets; a warehouse comes when volume and sources justify it.

Can the assistant be trusted with numbers?

It answers from defined metrics and shows the query and sources behind every answer, so results are checkable. Definitions are agreed with your team before launch.

How accurate are the forecasts?

Forecast accuracy is measured against actuals from the start and reported; alerts are tuned to avoid noise. We show the track record rather than promising precision.

Can it analyze our calls and tickets?

Yes. Conversation analytics classify intents, outcomes, and issues across calls, chats, and tickets, which is often the richest untapped data in a service business.

Where does the data live?

In your database or warehouse, in your cloud account or ours, with access controls and retention rules. Models can run privately.

Related

Next Step

Bring one workflow. Leave with a production plan.

Tell us where calls, tickets, documents, or approvals pile up. We map the workflow, size the impact, and propose a deployment you can measure.

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