— AI Analytics & Dashboards

Ask your data anything.

Custom AI-powered dashboards we build for businesses that have data — and don't have a data team. Type plain-English questions, get instant answers. Auto-detect anomalies. Predict what's coming. Surface root causes. Built on Apache Superset, Amazon QuickSight, and modern LLMs.

Build one for your team
your-analytics.jsimple.io · live data
Ask in plain English
What's our turnover rate this quarter compared to last year?
AI Answer · 1.2s
Q4 turnover
14.2%
3.8pts vs Q4 last year
YoY trend
+37%
well above industry avg (9%)
Voluntary share
82%
Mostly resignations, not layoffs
Monthly turnover · last 12 months ● Anomaly detected — Oct
JanFebMarAprMayJunJulAugSepOctNovDec
The framework

From hindsight to foresight.

Most dashboards stop at "here's what happened." Ours don't. Built around a four-stage AI loop: see what's happening, understand why, predict what's next, take action.

01 / See
Stunning dashboards.

Raw data turned into visual charts and scorecards your leadership team actually wants to look at. Real-time, customizable, branded to your business.

02 / Understand
NLP search.

Type plain-English questions and get instant answers grounded in your data. No SQL, no analyst queue, no waiting on the BI team.

03 / Predict
Anomaly detection.

AI watches your metrics 24/7. Detects deviations before they become problems. Sends alerts based on the thresholds you care about.

04 / Act
Root-cause AI.

When something's off, the AI tells you why — not just what. Correlations, contributing factors, suggested actions. Built for leaders who need to move fast.

In practice

Spot it. Understand it. Fix it.

An example of what an AI-detected anomaly looks like in practice — and how the system surfaces the likely root cause without you having to chase it down.

your-analytics.jsimple.io · workforce module 1 anomaly detected
Monthly turnover · last 12 months
FY26
● Oct spike
Turnover spike — Operations team
Detected Oct 14 · 2.4× the rolling 6-month average

Operations team departures jumped from a baseline of 1.8/month to 4.4 this month — well outside normal variance.

AI Root-cause analysis

Strong correlation with the compensation review delayed from Sept to Q1. 6 of 9 departing Operations staff had requested salary conversations in the prior 90 days. Recommend: pull comp review forward, schedule retention conversations with the 4 highest-tenure remaining team members.

Predicted flight risk
Operations High · 38%
Engineering Med · 17%
Sales Med · 14%
Customer Success Low · 6%
Finance & Admin Low · 4%
The example above shows a workforce use case because it's the easiest to read in a screenshot — but the same engine runs on financial, operational, customer, and revenue data. Currently in production with a major US healthcare provider and a national service company across different metric categories.
Where it fits

Built for whatever data you have.

Workforce metrics, financial trends, operational KPIs, customer behavior — the AI search and anomaly layers work on any tabular data source you can connect. A few common starting points:

— 01 / Workforce & People
Turnover, hiring, engagement, comp.

100+ pre-built scorecards covering attrition, time-to-hire, diversity, performance distribution, comp benchmarking. The most-deployed module — currently live in production at two organizations.

Example questions
What's our new hire retention rate?
Top termination reasons in 2026 by department?
Compare gender diversity across management levels.
— 02 / Finance & Revenue
Revenue, margins, cash, AR/AP.

Real-time revenue trends, gross margin by product line, accounts receivable aging, cash runway, expense anomalies. For finance leaders who don't want to wait until month-end to see what's happening.

Example questions
What's our revenue per customer by region?
Show me expense outliers this quarter.
Which products have margin trending down?
— 03 / Operations & Service
SLAs, throughput, quality, cost.

Operations dashboards for service businesses, manufacturers, and distributed teams. Throughput vs SLA, quality defects, cost-per-unit, capacity utilization — with auto-alerts when anything drifts.

Example questions
Which sites are missing SLA most often?
Show me throughput by shift, last 30 days.
Where are our biggest defect clusters?
— 04 / Customer & Growth
Churn, LTV, funnel, satisfaction.

Customer cohorts, churn signals, LTV by segment, NPS trends, funnel conversion at every stage. Built for revenue and CS leaders who need to see which customers are slipping before they're gone.

Example questions
Which customers are most likely to churn?
Compare LTV by acquisition channel.
Where are we losing prospects in the funnel?
Built on

Modern tools. Honest engineering.

We build on proven open-source and cloud-native foundations — not a proprietary black box. You own your data, your dashboards, and your stack.

— Visualization
Apache Superset · Amazon QuickSight

Best-in-class BI engines for the dashboards themselves. Open standards. Your data stays in your environment.

— Natural language
LLM-powered query layer

Modern language models translate plain English to SQL against your schema — with verification before any query runs. No data leaves your environment for model training.

— Anomaly & prediction
Statistical + ML models

Time-series anomaly detection, regression-based forecasting, correlation analysis. The right model for the job — not "AI" as marketing varnish.

— Integration
Snowflake · Postgres · Mongo · S3

Connects to whatever data sources you already have. REST, GraphQL, gRPC. Custom adapters in days, not weeks.

— Hosting
Your VPC · or ours

Deploy inside your AWS, Azure, or GCP — or use our SaaS infrastructure. SOC 2 pipelines either way.

— Security
Private LLM endpoints · static masking

No raw OpenAI calls on sensitive data. PII masked during training and inference. Encrypted at rest and in transit.

Have data. Need answers.

Bring us your spreadsheets, your databases, your messy CSVs — and we'll build the dashboards your leadership team will actually open.