Intelimek

A complete digital twin is not built from sensor data alone. While measured data is essential, real process understanding also depends on the physics that governs the system and the actual behavior observed during operation. Intelimek builds digital twins that integrate governing physics, comprehensive process data, and visual understanding into a single, explainable framework that engineers can trust.
Our approach follows a structured four-step workflow that turns complex process behavior into practical decision support across development, scale-up, and manufacturing.

Harnessing Physics and AI
for Superior Process Engineering

Intelimek combines the laws of physics with advanced AI to provide explainable, validated results in process design. This integration enhances prediction accuracy and enables informed decision-making, optimizing efficiency and quality in dynamic environments.

1. Model the Process

We begin by building a solid physics-based foundation that captures how the process should behave under different conditions.

This step includes:

Literature research and first-principles understanding of the process
Development of physics models using CFD, FEA, and DEM
Material characterization and calibration using experimental and visual methods
Validation and tuning of models against measured and observed behavior

CFD Modeling

DEM Modeling

FEA Modeling

Our expertise includes implementing coupled solvers – CFD-DPM, CFD-DEM etc as well as implementation of additional physics using the solver APIs (development kits).

These models describe the underlying mechanisms of flow, heat transfer, stress, mixing, compaction, or drying. This physics layer forms the reference against which all data and AI models are developed.

Explore Success Stories

2. Automate Data Generation and Integration

Once the physics foundation is established, we automate the generation
and integration of all relevant data.

This includes:

Automation of geometry preparation, meshing, solver setup, and execution
Scalable execution of simulations on HPC or cloud infrastructure
Automated post-processing and extraction of engineering insights
Integration of process and sensor data such as vibration, temperature, pressure, torque, ultrasonics, and control signals
Ingestion of SCADA and historian data from manufacturing systems
Integration of visual data from cameras, thermal imaging, and video streams

This automated pipeline brings together synthetic data from physics models and measured and observed data from real processes into a consistent and comprehensive dataset.

3. Develop Physics Guided AI Models

With a unified data foundation in place, we develop AI models that are guided by physics and informed by real process behavior.

Our work includes:

Selection and combination of appropriate ML techniques such as regression, classification, ANNs, RNNs, and CNNs
Multi-model strategies that leverage physics-based, process, and visual data together
Physics informed neural networks when spatial and temporal predictions are required

Rather than predicting only single summary KPIs, these models capture spatial distributions, temporal evolution, and sensitivity to operating conditions. This enables deeper insight into variability, risk, and performance drivers.

4. Democratize Through Applications

A digital twin creates value only when it is accessible to the teams who need it.

This includes:

Automate model refinement as new data becomes available
Enable engineers to interact with models without requiring simulation or AI expertise
Deploy digital twins as secure, intuitive web applications
Support decisions across development, scale-up, transfer, and manufacturing
This ensures advanced models move from specialist tools to everyday engineering workflows.

Agentic AI Guidance for Everyday Engineering Use

Digital twins can be physics-heavy and data-intensive, and many teams want a copilot-style assistant to navigate models and extract insights faster. Intelimek’s agentic AI framework delivers this while keeping security and deployment constraints in mind. It supports guided application navigation and domain queries, helping users interact with digital twins confidently and consistently.

Business Value and Outcomes

Intelimek’s digital twins are designed to deliver measurable business impact.
They help teams:

Reduce experimentation time and cost by replacing physical trials with virtual studies

Explore process design space efficiently and with confidence

Assess process variability and risk under changing conditions

Optimize operating windows for maximum throughput and quality

Tune processes faster during scale-up and technology transfer

By combining physics, data, and AI within a single framework, Intelimek enables faster decisions, lower development risk,
and more reliable process performance.

Why Intelimek?

Intelimek combines a team of experts with deep industry knowledge in steel, pharma, food, and healthcare. Our experience in developing and automating process models that integrate seamlessly with AI ensures tailored solutions for specific challenges.

With a proven track record of delivering effective results, we are a trusted partner for organizations seeking to optimize their processes. Our commitment to enhancing productivity makes us a credible choice for navigating complex environments.

Our Values

Digital Twin – What You Need

Digital Twins enhance comprehension of physical system behaviours and offer optimization tools. While those derived solely from data are incomplete, incorporating real-world data introduces variability. Understanding the operational dynamics and responses of systems requires both physical principles and real-world data. A thorough Digital Twin merges these aspects, combining theoretical frameworks with actual system behaviours.

Modeling Physics of Systems

Inclusion of Physics of the System brings in the context to derive the insights about system response to the changing operating conditions. Intelimek brings in the expertise to develop process models using CAE & Scientific Computing techniques. The models are integrated into Digital Twin workflow seamlessly.

Integration of Physical Parameters

Actual system parameters are collected via sensors and IIOT platform. Data models are build based on the measured data. Actual parameters are used as inputs to the physics and data models for analysis and prediction of system performance.

AI Powered Engineering

AI is integrated with IIOT data as well as CAE & Scientific Computing techniques to develop the AI powered models. AI increases accuracy and speed of the response.

Complete Digital Twin

A Complete Digital Twin is the one that includes models based on the Physics of the System, Measured data and parameters, and enhanced with AI.

Join Us On The Journey

We are building the future of process digital intelligence, one practical solution at a time. If you believe in making science and data work together for better outcomes, we would love to work with you. Whether you are a manufacturer exploring digital twins, a partner advancing industrial innovation, or a talent passionate about engineering and AI, this journey is for us to shape together.

At Intelimek, our mission is to “Inteligize the Making,” a commitment to transforming the manufacturing industry through innovative technology and deep expertise

Quick Links

Our Services

Process Modeling
Materials Calibration
Digital Twins
Physics + AI Models

Contact Details

Intelimek LLC
111 Middlesex Turnpike #1051 Burlington,
MA 01803 United States

consult@intelimek.com

Intelimek

Digital Twins for Manufacturing
and Process Engineering

Digital Twins for Manufacturing
and Process Engineering

Harnessing Physics and AI
for Superior Process Engineering