Augmented Analytics and AI Reporting
Augmented analytics lets people ask questions of their own business data in plain language and get an answer with the query behind it. We connect AI models to the data you already have, with a person reviewing anything that drives a decision.
AI analyst
Plain-language questions
ANALYTICS COPY
- ERP
- CRM
- Operations data
SOURCE SYSTEMS
What Is Augmented Analytics?
Augmented analytics uses AI to help people query and explain business data without writing SQL or waiting for a new report. A person asks a question, the AI turns it into a query and runs it, then returns the result with the query it used.
Conversational analytics is the most common form. An engineer types "What's the lead time impact if we source this component from Vendor X instead of Vendor Y?" and gets an answer. Agentic analytics goes further, splitting a question into steps across several sources.
AI analytics vs. traditional BI
Traditional BI answers the questions someone built a dashboard for. AI analytics answers the next question, the one nobody has built a report for yet. It doesn't replace your BI stack. It sits on top of the warehouse and the reports you already trust.
WHEN IT FITS
Signs You're Ready for AI Analytics
AI on top of your data helps most when questions wait on people. These signs show up first.
Questions wait days for an answer
A manager asks the BI team for a number, and the answer arrives a week after the decision.
Analysis starts with manual exports
Analysts pull data from several systems into spreadsheets by hand before they can answer one question.
What-if questions go unasked
Testing a scenario, such as switching a supplier, takes so much manual work that nobody tries it.
Dashboards answer yesterday's questions
Every new question turns into a new report request.
Performance data sits in separate tools
Sales, operations, or quality data lives in disconnected systems, so nobody can see patterns across them.
Odd numbers go unnoticed
A spike or a drop sits in a report for weeks because nobody had time to look.
OUR APPROACH
How We Build AI Analytics
We build AI analytics on the data you already have and show the work behind every answer. Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
AI bolted onto a vendor tool
- Data copied to another vendor's cloud
- Answers with no query to check
- Direct access to production systems
- Another portal nobody logs into
AI built on your data
Your data stays in your environment
AI models read a copy of your data in an environment you control, not another vendor's platform.
Every answer shows its work
The AI returns the query it ran, so an analyst can check the answer before anyone acts on it.
No access to production
The AI queries a reporting copy or a sandbox, never the production database, and it can't change or delete data.
Inside the tools people use
Answers arrive in Power BI, email, or Teams, so nobody needs another portal.
OPTIONS
Types of AI Analytics
AI analytics comes in a few common forms, and most companies start with one.
| TYPE | WHAT IT DOES | WHEN IT FITS |
|---|---|---|
| Natural-language questions | Turns a question into SQL, runs it, and returns the result | Many people need answers, and few can write SQL |
| Multi-step analysis | An agent splits a question into steps across several data sources | Questions span systems, such as sourcing, inventory, and orders |
| Anomaly detection | Flags numbers that fall outside the normal pattern | Problems hide in data nobody has time to review |
| Predictive analytics | Forecasts a future value from historical data | Planning depends on demand, sales, or lead times |
| Power BI Copilot | Microsoft's assistant writes summaries and answers questions inside Power BI reports | Your reports already run on Power BI with a clean semantic model |
How we choose
If your reports run on Power BI and the semantic model is clean, Copilot may be enough. When questions cross systems, we build a custom agent. Where a forecast is the real need, see machine learning.
WHAT WE BUILD
AI Analytics We Build
Our AI analytics work covers the data connection, the AI layer, and the place the answers show up.
Plain-language questions over your data
A chat interface where staff ask about orders, inventory, or sales and see the query behind each answer. For a global RF and microwave manufacturer, engineers ask what-if questions about parts and suppliers.
Multi-agent analysis across systems
Agents that split a question into steps, query several sources, and apply your business rules. See AI agent development.
AI reporting and summaries
AI reporting that writes a summary for each person from their own performance data. For a national residential flooring retailer, an LLM writes coaching for each sales rep from four data domains.
Forecasts inside your reports
Our predictive analytics services forecast demand, sales, or lead times and put the forecast in the reports people already read. See machine learning.
Power BI Copilot readiness
Copilot answers only as well as the semantic model under it, so we clean up the model and its names first. See business intelligence and Power BI.
Data foundations for AI
When data is scattered, AI analytics starts with one place to look, such as a unified database or a data warehouse.
HOW IT WORKS
How an AI Analytics Project Runs
An AI analytics project runs in five steps, from the questions people ask today to answers they use every week.
STEP 1
Collect the questions
We gather the questions people ask most and how long each takes to answer today.
STEP 2
Map the data
We find where each answer lives and set up a reporting copy the AI can query.
STEP 3
Build a first version
We connect the AI to one data domain and test it against answers your analysts trust.
STEP 4
Add review and limits
We set what the AI can query, log every question, and route decisions to a person.
STEP 5
Expand
We add data sources and questions in stages as people start relying on it.
Checking the AI's answers
Language models can give a different answer to the same question, and they can be confidently wrong. We test against answers your analysts already know and keep a person in the loop for decisions.
What your team gets at handoff
Whatever we build, you own. Services we can provide at handoff include knowledge transfer and documentation covering the data connections and the business rules. Existing BI teams can often run it without new specialist hires.
RESULTS
AI-Powered Analytics Results
For a global RF and microwave manufacturer, JBS Dev built a multi-agent AI platform with an estimated 50 to 70% reduction in time spent on manual engineering analysis. For a national residential flooring retailer, JBS Dev unified four disconnected systems for AI coaching analysis.
85-90%
Forecast accuracy on Amazon SageMaker
Manufacturing
Generative AI Sales Forecasting for a Manufacturer
Challenge: Traditional statistical forecasts kept falling short, causing excess inventory or missed sales.
Result: A generative AI time-series model on Amazon SageMaker reached 85 to 90% forecast accuracy.
Read the manufacturing forecasting case study100M+
Records scored for loan risk
Lending
Real-Time Loan Risk Analytics for a Lending Analytics Company
Challenge: Users needed loan performance reports across the market and for each portfolio.
Result: A multi-tenant platform on AWS EMR, Glue, and Redshift processes more than 100 million records, and its machine learning engine scores each loan's risk.
Read the loan analytics case study"JBS feels more like an extension of our internal team than a traditional vendor relationship."
Senior IT Leader, AmeriVet Partners
TECHNOLOGY
How AI Analytics Connects to Your Data
An AI analytics system has three parts. There's the data it can read, the AI that turns questions into queries, and the people who review the answers. The AI can write SQL, GraphQL, or Amazon Athena queries, depending on where the data lives.
We've built AI analytics on Microsoft Azure with Azure AI Services and on AWS with Amazon Bedrock.
YOUR DATA
ERP and CRM
Analytics copy
Data warehouse
THE ANSWER LOOP
AI agent
Turns questions into queries
People
Review the answer and the query
Does the data have to be clean?
No. You don't need perfect or clean data to start, and you don't need a cleanup project first. The AI does need one place to look, so scattered data usually starts with a unified database.
Keeping AI costs predictable
Every question uses model tokens, and AI that runs around the clock gets expensive. We pick a model sized to the job and run it only when someone asks.
COST AND TIMELINE
What Drives AI Analytics Cost and Timeline
AI analytics cost and timeline depend mostly on how many data sources the AI needs and how much review its answers need. Six factors drive most estimates:
- 01Number of data sources
- 02State of the data and data model
- 03Number and kind of questions
- 04Review and access controls
- 05Where answers are delivered
- 06Model usage costs
How the timeline is set
Timelines depend on the data sources and the questions, and the assessment sets them. We start with one data domain and the questions people ask most, so a working version reaches users before every source is connected.
How cost is set
We don't quote a number before we understand the data and the questions. The assessment turns the factors above into an estimate for the build and for ongoing model usage. If a new dashboard or a BI query would answer the question, we'll tell you.
ENGAGEMENT MODELS
Ways to Work With Us
Each model stands alone or leads into the next. Compare all the ways to work with us.
Assessment & Strategy
For companies that need clarity before committing to a build.
Start here if you're not sure what to change first. We review the system component by component, weigh difficulty against business impact, and recommend a path for each part, including what to leave alone. You get a roadmap you can act on with us or on your own.
- Technical assessment
- Architecture review
- AI opportunity mapping
- Modernization roadmap
- Prioritized recommendations
Best when: You need to understand the opportunity, risk, architecture, or roadmap before investing in a larger build.
Projects
For companies ready to build or modernize a defined system.
For a defined scope, senior engineers lead delivery from architecture through release. We work in stages, write tests for core components, and ship through CI/CD. You own everything we build, plus knowledge transfer and documentation tailored to your team.
- Modernization projects
- Custom applications
- Cloud platforms
- Data systems
- Integrations
- AI agents
Best when: You have a clear business problem, workflow, or platform that needs to be built, modernized, or connected.
Teaming / Managed Delivery
For companies that need experienced delivery capacity.
When you need more than a single project, services we can provide include architectural guidance for your existing team, hands-on development, QA, and ongoing support. Senior US-based engineers lead our builds, and nearshore support is an option for ongoing work.
- Senior-led teams
- Embedded engineering
- Ongoing development
- Knowledge transfer
- Flexible support
Best when: You need reliable engineering capacity, technical leadership, or ongoing delivery support without adding unnecessary management overhead.
WHY JBS DEV
How to Choose an AI Analytics Partner
Before you hire for AI analytics consulting, ask these four questions. Here is how we answer them.
- Where does our data go?
- It stays in an environment you control. We connect AI models to a copy of your data there, rather than sending it to another vendor's platform, and the AI doesn't need access to production systems.
- Can we check the AI's answers?
- Yes. Every answer comes with the query that produced it, and we test the system against answers your analysts already trust. For decisions that matter, a person reviews the result first.
- Will it work with our BI stack?
- Usually, yes. AI analytics sits on top of the warehouse and BI tools you already run, such as Power BI, Snowflake, Amazon Redshift, or a SQL database. Your dashboards stay.
- Where is the team?
- Senior US-based engineers lead our builds, with nearshore support available for ongoing work. From the first call, you will know exactly who is building and supporting your system.
JBS Dev is a senior-led custom software development company, founded in 1999, that builds, modernizes, and integrates enterprise systems around the technology clients already own.
FAQ
AI-Powered Analytics FAQs
What do augmented analytics services include?
They cover connecting AI to your business data, letting people ask questions in plain language, showing the query behind each answer, and adding reviews and access controls. Ours also cover the data foundation when it's missing, and delivering answers into the tools your teams already use, such as Power BI, email, or Teams.
What is conversational analytics?
Conversational analytics lets people ask a question about business data in plain language, such as which suppliers have the longest lead times, and get an answer back. The AI turns the question into a database query, runs it, and returns the result, with the query shown so an analyst can check it.
What is agentic analytics?
Agentic analytics uses AI agents that break a question into steps. An agent can query several systems, apply business rules, compare results, and flag numbers that look wrong. It suits questions that cross systems, such as sourcing, inventory, and orders. A person still reviews the answer before it drives a decision.
Can AI analytics work with Power BI?
Yes. Power BI Copilot adds AI summaries and questions inside Power BI reports, and it works best on a clean semantic model with clear names and row-level security. When questions reach systems outside Power BI, a custom AI agent can query them and send results back into the reports people already read.
How accurate are AI-generated answers?
They are often right but not always, and the same question can get a different answer twice. That's why we show the query behind each answer, test the system against results your analysts already trust, and keep a person reviewing anything that drives a decision. Business rules live outside the model.
Is our data safe with AI analytics?
The architecture is designed to limit what the AI can see and do. It queries a reporting copy instead of production systems, can't change or delete data, and follows your existing access controls. The data stays in an environment you control, and you own the data and everything we build.
How much does AI-powered analytics cost?
It depends on the number of data sources, the state of the data, the number and kind of questions, review and access controls, where answers are delivered, and model usage. The assessment turns those factors into an estimate for the build and for ongoing usage, before you commit to anything.
What's the difference between AI analytics and predictive analytics?
AI analytics answers questions about what happened and why, in plain language. Predictive analytics forecasts what will happen next, using models trained on your history. Predictive analytics consulting starts by checking whether your data has enough clean history to forecast from. An agent can also run a forecast when someone asks a what-if question.
