Stop drowning in dashboards. We build AI systems that surface insights, flag anomalies, and auto-generate reports, so your team focuses on decisions, not data collection.
We map every data source, databases, APIs, spreadsheets, warehouses, and assess quality, freshness, and completeness before building anything.
We work with your team to define the KPIs that matter, build calculation logic, and design the anomaly detection thresholds.
We build the ML pipeline for anomaly detection, trend forecasting, and natural language insight generation on top of your cleaned data.
Insights land in Slack, email, or a custom dashboard on your schedule, with one-click drill-down into the underlying data.
What data sources can you connect?
Any source with an API, database connection, or file export, SQL databases, data warehouses (BigQuery, Snowflake, Redshift), SaaS APIs (Stripe, HubSpot, Shopify), spreadsheets, and custom internal systems.
How is this different from Power BI or Looker?
BI tools show you what happened. Our AI layer tells you what's wrong, what's changing, and what to do, in plain language. We often build on top of your existing BI tool rather than replacing it.
How long until we see real insights?
Most clients see their first automated insights within 3–4 weeks of project start. The first two weeks are data mapping and quality work, getting this right is what makes the AI layer reliable.
Who owns the models and data after the project?
You own everything. All models, pipelines, and configurations are handed over at launch with full source access. We don't lock you into proprietary infrastructure.
Core capabilities delivered in every AI Analytics & Insights engagement.
We connect to your APIs, databases, event streams, and SaaS tools and build ingestion pipelines that process data as it arrives rather than in overnight batches. Anomalies surface within minutes, not the following morning.
Statistical and ML-based anomaly detectors flag unusual patterns, unexpected revenue spikes, churn signals, and operational deviations the moment they appear in your data, with configurable sensitivity thresholds that match your business tolerance.
Time-series forecasting models using Prophet, ARIMA, and gradient boosting generate forward-looking predictions for revenue, demand, inventory, and operational capacity. Forecasts update automatically as new data arrives each day.
LLM-generated narrative summaries explain what happened, what was unusual, and what the likely cause is in plain English. Stakeholders get the insight without needing to learn SQL, interpret dashboards, or join a data review meeting.
Weekly and monthly reports assemble themselves from structured data inputs, narrative summaries, and visualizations, then deliver directly to Slack, email, or a shared document without any manual compilation or analyst time.
We build dbt models, Snowflake pipelines, and semantic layers that translate raw data into the business metrics your team actually needs. Replaces one-off SQL queries with governed, reusable metric definitions that any team member can trust.
We work with teams across these functions and industries.
Sales and marketing organizations that need to know which channels, campaigns, and segments are driving results in real time, not a week after the month closes. We connect CRM, ad platform, and billing data into a unified revenue analytics pipeline.
Teams that need to react to exceptions, inventory shortfalls, or delivery delays as they happen. Real-time monitoring with automated alerts replaces the daily status meeting with immediate, specific notifications on what needs attention.
Finance organizations that still assemble monthly reports manually from multiple system exports. We automate the data consolidation, variance analysis, and narrative generation so your team focuses on strategic insights, not spreadsheet assembly.
SaaS companies that need real-time visibility into product usage, error rates, churn indicators, and feature adoption. We build the analytics infrastructure that connects event data to the business metrics leadership needs for product decisions.
Anomaly detection systems surface issues 4–6 hours earlier than manual monitoring processes on average
Monthly report preparation time reduced from 2–3 days to 2–3 hours with automated assembly and narrative generation
7-day revenue forecast accuracy reaches 87–93% with properly trained and validated time-series models
Data team capacity freed by 40–50% when recurring reporting is fully automated, redirecting effort toward analysis
AI analytics is the practice of applying machine learning and large language models to your business data to surface insights, detect problems, and generate reports automatically. The key distinction from traditional business intelligence: AI analytics is proactive, not reactive. Instead of you querying the data to find what happened, the system monitors continuously and tells you when something worth your attention has occurred.
The difference between discovering on Friday that revenue dropped on Tuesday, and getting a Slack message on Tuesday afternoon with the specific segment and probable cause.
Anomaly detection monitors your key metrics in real time and alerts when something deviates significantly from expected behavior. A well-designed statistical model with appropriate business context can have an initial system monitoring your key metrics within two to three weeks of project start, without requiring a large ML team.
Forecasting adds a forward-looking layer: instead of only knowing what happened, you know what is likely in the next 7, 14, or 30 days. Revenue forecasting, demand planning, staffing projections, and inventory management all benefit from time-series models that update automatically as new data arrives. Accuracy depends heavily on data quality, which is why every engagement starts with a data audit.
Data quality is the most common reason AI analytics projects disappoint. Every engagement begins with a data audit that profiles your sources, identifies quality problems, and quantifies how data issues will affect model accuracy. This phase typically surfaces problems such as missing event types, silent logging failures, and inconsistent date handling that were unknown to the engineering team before the audit.
The analytics infrastructure we build is designed to grow with your organization. We use:
Every layer of the stack is documented so that your internal team can extend it independently after the initial engagement.