Artificial Intelligence

Every company has data. Lots of it. Sitting in ERP systems. CRM databases. IoT sensors. Spreadsheets. Cloud apps. But here’s the problem: Data that just sits there doesn’t help anyone.

You don’t need more data. You need data that thinks. Data that acts. Data that learns from what happened yesterday and tells you what to do tomorrow.

That’s what LeapLytics builds.

We connect to your systems. We turn your data into intelligence. And we create a loop where every output makes the next decision smarter.

What We Actually Do

LeapLytics provides three core services:

AI-as-a-Service
Intelligent assistants, predictions, and automation—powered by your own data. Not generic AI. Your AI.

Simulation-as-a-Service
Test decisions before you make them. Run what-if scenarios. See outcomes before they happen.

Full Data Pipelines
We connect to your systems, clean your data, and make it ready for AI and simulation. No data engineering headaches for you.

The result? You get from raw data to smart decisions—without building an entire tech team.

The Cycle That Powers Everything

Most AI projects work like this: Put data in. Get answer out. Done.

That’s a dead end.

LeapLytics works differently. We built a cycle that runs at every level of your business:

Step 1: Data comes in
From your ERP, CRM, IoT devices, documents, APIs—anywhere.

Step 2: The model layer processes it
This could be an AI assistant answering questions. A simulation testing scenarios. A translation engine. A forecasting model. Whatever intelligence you need.

Step 3: Output is created
A prediction. A recommendation. A visualization. An action taken automatically.

Step 4: That output gets stored
On your side. In your systems. Ready to use.

Step 5: The stored output becomes new input
And the cycle starts again.

This isn’t a one-time analysis. It’s a continuous loop. Every decision feeds the next one. Every output makes the system smarter. Every cycle brings you closer to what’s actually happening in your business.

This cycle appears everywhere—in every use case, at every level, across every system we touch.

How the Platform Works

LeapLytics is built in layers. Each layer has a job. And each layer connects to the cycle.

Your Systems (Where Data Lives)

This is where everything starts. Your existing systems:

ERP systems (SAP, Oracle, Microsoft Dynamics)
CRM platforms (Salesforce, HubSpot)
Document management systems
Databases and data warehouses you already own
IoT devices and machine data
External sources (APIs, open data, web)

We don’t ask you to change your systems. We connect to them.

Ingestion and Integration Layer

This is where we pull your data in and make it usable.

Connectors that link to your ERP, CRM, documents, IoT, and APIs
ETL/ELT pipelines that move data in batches or real-time streams
Data harmonization that cleans, standardizes, and fixes quality issues

Your data comes from dozens of places in dozens of formats. We turn it into one clean, connected source.

The cycle here: Raw data comes in. It gets cleaned and structured. That structured data feeds the next layer. Feedback from AI models tells us what data needs improvement. We refine the pipelines. Better data comes in next time.

Data and Storage Layer

Clean data needs a home. This layer stores everything in the right format for the right use.

Data lake and warehouse for your curated business data
Vector database for documents, knowledge bases, and logs (what AI assistants search)
Feature store for ML model inputs (pre-calculated, ready to use)
Time-series and event store for IoT data, processes, and anything time-based
Output store for predictions, simulation results, and chat logs

The cycle here: Data gets stored. Models use it. Models create outputs. Outputs get stored. Those outputs become inputs for the next round of analysis.

AI and Simulation Services (The Model Layer)

This is where intelligence happens. Your data becomes answers.

LLM Services

Question-and-answer assistants that know your business
Co-pilots that help employees work faster
Content generation based on your data

Machine Learning Models

Scoring (which customers will churn? which leads are hot?)
Forecasting (what will sales look like next quarter?)
Classification (what type of issue is this support ticket?)

Simulation Engines

Scenario planning (what happens if we raise prices 10%?)
What-if analysis (what if demand doubles?)
Technical simulations (how will this machine perform under stress?)

Optimization

Decision suggestions based on constraints and goals
Resource allocation recommendations
Process improvement insights

The cycle here: Data feeds models. Models produce outputs (predictions, simulations, answers). Those outputs get stored. Stored outputs become training data or context for future models. Models get smarter. Outputs get better.

Interaction and Orchestration Layer

Intelligence is useless if people can’t use it. This layer makes AI accessible.

Conversation orchestrator that manages chat flow and context (so assistants remember what you talked about)
LLM tool and function calling (so AI assistants can actually DO things—query databases, trigger workflows, update records)
Workflow engine for multi-step processes (approvals, escalations, automated sequences)

This is where AI stops being a toy and starts being a tool. Your assistants don’t just answer questions. They take action.

The cycle here: User asks a question or triggers a workflow. The orchestrator calls the right models and tools. Output is delivered. User feedback (did this help? was this right?) flows back. The system learns what works.

Customer Frontends (Where People Interact)

This is what your team actually sees and uses.

Web apps and business applications with embedded AI
Chat interfaces in browsers, Microsoft Teams, Slack, or custom apps
BI dashboards and reports powered by real-time intelligence
Simulation GUIs for running scenarios and seeing results visually

We meet your people where they work. No new tools to learn. AI shows up inside the apps they already use.

The cycle here: Users interact with frontends. Their actions and feedback become data. That data feeds back through the entire stack. The experience improves. The intelligence sharpens.

Platform Capabilities That Span Everything

Some things don’t fit in one layer. They run across the entire platform.

Security and Identity Management

Your data stays yours. We implement role-based access, encryption, and authentication so only the right people see the right data.

Data Governance and Catalog

Know what data you have, where it came from, and how it flows through the system. Metadata management and lineage tracking included.

MLOps

Models need care. We handle the model registry, monitoring, and retraining so your AI stays accurate over time.

Observability

See what’s happening. Metrics, logs, tracing, and cost monitoring across the entire platform. No black boxes.

Compliance and Auditability

Regulated industry? We track everything. Full audit trails for every decision, prediction, and action.

Billing and Usage Tracking

Know what you’re using and what it costs. Transparent tracking across all services.

Why LeapLytics

We Connect to Any Data Source

ERP. CRM. IoT. Documents. APIs. Databases. Whatever you have, we plug into it. No rip-and-replace. No migration projects. We work with what you’ve got.

Our AI Takes Action

Most AI tools answer questions. Ours do things. Through tool and function calling, our assistants can query your systems, update records, trigger workflows, and execute decisions. Not just insights—impact.

Simulations Run on Real Data

Forget toy models with fake numbers. Our simulations use your actual enterprise data. When you ask “what if,” you get answers you can trust.

One Platform for Everything

ML models. Large language models. Simulations. All using the same data layer. No silos. No duplicate pipelines. One unified system.

Clear Business Outcomes

We don’t sell technology for technology’s sake. We deliver:

Faster decisions because insights are instant
Better decisions because they’re based on complete data
Automated decisions because routine choices don’t need humans
Tested decisions because you can simulate before you commit

Use Cases

Forecasting and Predictive Analytics

Know what’s coming before it arrives. Demand forecasting. Sales predictions. Resource planning. Cash flow projections. The cycle continuously refines forecasts as new data comes in.

Intelligent Assistants for Internal Teams

Give your employees an AI that knows your business. Answer questions about policies, products, customers, or processes. Pull data from multiple systems in one conversation. Take action directly from chat.

Process Optimization and What-If Simulations

Test changes before you make them. What happens if we change suppliers? Adjust pricing? Reallocate resources? Run the simulation. See the outcome. Decide with confidence.

Anomaly Detection

Spot problems before they become disasters. Monitor IoT sensors for equipment failures. Watch transactions for fraud patterns. Track processes for unusual behavior. Get alerts when something doesn’t look right.

Document Intelligence

Turn unstructured documents into structured knowledge. Extract information from contracts, invoices, reports, and emails. Make it searchable. Make it actionable.

Customer Intelligence

Understand your customers better than they understand themselves. Churn prediction. Next-best-action recommendations. Sentiment analysis. Personalization at scale.

How It All Comes Together

Picture this:

Your ERP data flows into LeapLytics. It gets cleaned and stored. An ML model predicts which orders will be late. That prediction shows up in a dashboard your operations team uses. They take action. The results of that action flow back as new data. The model learns. Next week’s predictions are more accurate.

Meanwhile, your sales team asks an AI assistant about a customer’s history. The assistant pulls data from your CRM, checks recent support tickets, and summarizes everything in seconds. The rep closes the deal. That outcome becomes training data. The assistant gets better at helping the next rep.

Your finance team runs a simulation: what if raw material costs increase 15%? They see the impact on margins across product lines. They adjust pricing strategy before the cost increase even happens.

Every interaction. Every decision. Every outcome. It all feeds the cycle. The system learns. Your business gets smarter.

Ready to Start?

You have the data. We have the platform.

Let's build the cycle that turns your information into intelligence.