Our skilled team has built numerous responsive web applications using various front-end frameworks and libraries, including React.js, Angular.js, and Vue.js.

We specialize in designing robust backend architectures for large-scale and secure systems, leveraging libraries in Python, Node.js, and .NET.

Additionally, we have successfully migrated multiple monolithic architectures to serverless or microservices-based solutions.

We have developed numerous cross-platform applications for iOS and Android using cutting-edge technologies like Flutter and React Native.

Our expert team specializes in building high-performance native Android and iOS apps that are secure, flawless, and optimized for iPhones, iPads, and all Android devices using Kotlin, Java, Swift, and Objective-C.

With extensive experience in wearable technology, we have successfully launched multiple applications on the Play Store and App Store, serving over 100K+ active users.

Additionally, we create Progressive Web Apps (PWAs) that adhere to industry best practices, delivering a native app-like experience—perfect for startups looking for cost-effective solutions.

Building highly-accurate models is one thing, deploying the models in a live production environment and assessing their performance on a continual basis is another. We have expertise in both. We are well-versed with tools such as Vertex AI Pipelines, TensorFlow-serving, TorchServe, KubeFlow pipelines, Amazon Sagemaker, MLFlow etc. to help us deploy the solutions.

The domains and use-case of the product bring new constraints and considerations when it comes to building AI models. For example, a solution to be used inside the firmware has to have limited compute and storage capabilities; Stocks prediction algorithms in a live prediction environment have to meet certain latency requirements. We anticipate such constraints and then build the models within such constraints. Our validation methodology also takes into account such constraints.

We have worked with large-scale “online” recommender systems with a few clients, that requires ingesting data and making inferences from that new data effective immediately.

We are familiar with various distributed processing solutions such as Apache Spark and Apache Hadoop.

Not every problem is suitable for Deep Learning, we believe, practice and experience that in our day-to-day work. Hence many of our projects still use conventional machine learning methods. We use both Generative Models (Naive Bayes, Bayesian Networks, Latent Dirichlet Allocation, GMMs (Gaussian Mixture Models) ,and HMMs (Hidden Markov Models)) and Discriminative Models (SVMs (Support Vector Machines), Decision-Tree Based Models), Logistic Regression, instance-based learning methods (nearest neighbor algorithms)). We use Sk-learn for most of our machine learning work these days, and hence we are experts in that.

Unsupervised (K-means clustering, Hierarchical clustering, Spectral clustering, Birch), and semi-supervised learning is something we routinely use.

We regularly utilize various deep learning techniques for the work across many interdisciplinary projects. CNN, LSTM, Transformers and attention-based mechanisms, GANs (Generative Adversarial Nets) and VAE (Variational AutoEncoders) and Diffusion-based models as generative techniques for many challenging problems.

We are experts with the toolsets typically used for Deep Learning such as Tensorflow, PyTorch, and Keras. We routinely implement State-of-theArt modeling methods into these frameworks for proprietary use.

We have handled quite a few projects involving Image and video segmentation, key event detection, anomaly detection from videos and images, Object detection from videos and images, and constructing 3-D point cloud using multi-view camera feeds (with known or not-known geometry).

We work on several reinforcement learning problems with state-of-the-art algorithms TD3 (Twin Delayed Deep Deterministic models), DDPG (Deep Deterministic Policy Gradients), DDQN (Double Deep Q-Learning) etc., and libraries (Deap, TF-RLAgents, Acme).

We have built various evolutionary algorithms / population-based frameworks several times based on the product requirements. We have worked with existing libraries such as Deap, TPOT, and Evojax.

We have worked with problems related to Machine Translation, Sentiment Analysis, Text content Classification/Categorization, and Information Retrieval.

We work with quite a few NLP products from scratch, and we have built customized pipelines for those that perform all the steps of pre-processing and product-appropriate parsing (stemming, lemmatization, stop-word removals, tokenization, embedding ) before the actual modeling/inference begins.

We are well-versed with various flavors of BERT models, and we regularly fine-tune them for use case-specific purposes.

We routinely use NLP-specific libraries such as NLTK, and Spacy.

• AI-based controlled environment system development
• Crop-specific AI models development and identification important traits
• Population structure correction and genome-wide association studies (GWAS)
• Transcriptome-wide association studies (TWAS)
• Quantitative trait locus (QTL) analysis
• Disease, stress resistance/susceptible biomarker identification
• Seed quality and purity assurance
• Soil health analysis and improvement
• Identification of climate resilience crops
• Animal breeding and trait selection

• Genome assembly construction
• Repeat masking, gene prediction, and pathway annotation
• Gene discovery and function prediction
• Short and long reads based coding and non-coding RNA-Seq analysis
• Bulk DNA/RNA-seq data and downstream analysis
• Variants calling and its functional annotations
• Genome editing and crispr-cas9 data analysis
• Metagenomics and metatranscriptomics analysis

• Drug design and lead optimization using disease specific AI models
• Lead compounds validation on target protein using molecular dynamics simulations
• Drug repurposing using machine learning
• Clinical trial design, prediction and optimization using ML/DL technique
• Personalized medicine
• Drug interaction and adverse event monitoring
• Predictive analytics
• Disease-specific biomarker identification
• Data curation and custom database design
• SAR/QSAR studies

• scRNA-seq data analysis and cell type annotations using DNN models and their benchmarking and evaluation
• scATAC-seq data analysis includes preprocessing, peak calling, clustering, and regulatory network inference
• scDNA-seq data analysis to detect DNA mutations, copy number variations, and genomic rearrangements at the single-cell level
• Spatial transcriptomics data analysis including raw spatial data, performs alignment, and quantifies gene expression
• Integration of multi-omics data analysis to reveal relationships between biomolecules and disease phenotypes

Over the years, we have developed many custom data visualization tools, for desktop apps (Qt, PyQT), webapps (R/Shiny, plotly, matplotlib, pyqtgraph, seaborn) or using off-the-shelf tools (Sigma, Looker, Tableau, Google Data Studio)

Often the requirements and project plans are based on what we see in the data. We contribute to various projects where valuable data insights and early stage explorations have shaped the project path, Moreover, we have saved various industries lots of time and money.

Our default analysis tool is Python-based notebooks (Colab and/or Jupyter notebooks) with a lot of Pandas and numpy based work.

We possess statistical rigour and know-how of the techniques to validate if the data is sufficient for supporting or rejecting a particular hypothesis, OR if the data is following a particular distribution or not.

How robust is the deployed model? Will it give similar output in similar situations? How sensitive is it to the input features? To which features is it the most sensitive? What is the risk if suddenly one feature stops giving values or suddenly starts becoming more noisy? Such questions are routine for us, and we have statistical artillery and know-how to address such questions.

Infocusp is able to make any software product from scratch. A few clients who work with us only give us basic guidelines and specifications (either in writing or through the meetings), and the Infocusp team members can build products based on the given information.

All the work is done through the best practices involved in software engineering, UI/UX designing, and machine learning.

• We deployed many cloud-native products/applications on AWS, GCP, and Azure clouds.
• We follow DevSecOps since we consider security as an integral part of software development.
• Our team of professionals developed a highly scalable infrastructure that is processing terabytes of data daily.
• We designed and implemented architecture for multi-region, high-availability, multi-tenants products that can serve millions of requests.

Each product that we work with churns and generates huge amounts of data (some examples include sensor data coming from fitness/health products, high-frequency financial data, and weather data across many locations of the globe for the past few years). We work on Terabyte scale pipelines and distributed services so that the data processing gets done within an adequate timeframe.

Based on the use-case (project scale, type of application, past work), we choose the right database solutions. We routinely use Mongo DB, SQL (PostGREs, mysql), Redis, DynamoDB, Neo4J, Memcache, InfluxDB for our work.

We developed many multi-tenants platforms which help organizations to reduce their cloud cost. We made sure that each tenant's data is accessible to their members only.

Developed orchestration system which takes resource(CPU,RAM, GPU, codebase etc) requirements from researchers and provides the infrastructure in the cloud which execute code and generates data.

We have worked with all the physiological signals such as ECG, EEG, BCG, PPG, EMG and Accelerometry contributing into FDA grade healthcare algorithms such as Heart rate detection, Blood Oxygen Saturation, Sleep Stage Classification, Brain wave decompositions and Precise Signal Synchronisations.

We work closely with clients to make sure we get better Signal to Noise Ratio irrespective of form of product be it wearable or non-wearable.

We have worked with seismic data, satellite imagery and sensory data to increase the accuracy of downstream estimation models as well as optimize compute performance.

We use scipy and customized C++ code for all of our signal processing work.