Our Services

Model accuracy alone is not enough. We build end-to-end AI products that include deployment, monitoring, feedback loops, and continuous improvement.

Our production stack includes:

• Vertex AI Pipelines
• TensorFlow Service & TorchServe
• KubeFlow Pipelines
• Amazon SageMaker
• MLFlow

These platforms power reliable rollout, versioning, observability, and automated retraining.

Every AI product introduces specific operational constraints. Firmware-level systems must run on limited compute, while financial inference systems need strict latency guarantees. We design for these constraints early and bake them into validation and release workflows.

We engineer large-scale recommender products that ingest fresh behavioral data and serve low-latency recommendations in near real time.

Our recommendation pipelines are built on distributed systems such as Apache Spark and Apache Hadoop for throughput, resilience, and scale.

Deep Learning is powerful, but not always the best fit for product constraints. We evaluate each use case by latency, cost, interpretability, and deployment footprint, and select the approach that performs best in production.

We utilize a comprehensive suite of Generative Models (Naive Bayes, Bayesian Networks, Latent Dirichlet Allocation, Gaussian Mixture Models, and Hidden Markov Models) as well as Discriminative Models (Support Vector Machines, Decision Trees, Logistic Regression, and Instance-based learning).

We routinely apply unsupervised and semi-supervised learning, including K-means clustering, hierarchical clustering, spectral clustering, and BIRCH for segmentation, anomaly detection, and product behavior modeling.

We regularly apply deep learning techniques across interdisciplinary projects, including CNNs, LSTMs, transformers with attention mechanisms, GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models for complex generative tasks.

We are experts in deep learning toolsets such as TensorFlow, PyTorch, and Keras. We routinely implement state-of-the-art modeling methods within these frameworks for production workloads.

We build computer vision components for real-world products, including image/video segmentation, key-event detection, anomaly detection, object detection, and 3D point-cloud reconstruction from multi-view camera feeds.

We engineer reinforcement learning systems using state-of-the-art algorithms such as TD3 (Twin Delayed Deep Deterministic Policy Gradients), DDPG (Deep Deterministic Policy Gradient), and DDQN (Double Deep Q-Learning), along with libraries such as DEAP, TF-Agents, and Acme.

We build evolutionary and population-based optimization frameworks aligned to specific product requirements. We have also worked with established libraries such as DEAP, TPOT, and EvoJAX.

We build NLP systems for machine translation, sentiment analysis, text classification/categorization, and information retrieval.

For NLP products, we build customized pipelines that perform all required pre-processing and product-appropriate parsing (stemming, lemmatization, stop-word removal, tokenization, and embeddings) before modeling and inference.

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.

We build bioinformatics and agri-intelligence products that convert genomics and field data into deployable decision systems.

• AI-based controlled environment system development
• Development of crop-specific AI models and identification of 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

Our pipelines are engineered for reproducibility, scalable compute, and product-ready outputs for genomics teams.

• Genome assembly construction
• Repeat masking, gene prediction, and pathway annotation
• Gene discovery and function prediction
• Short- and long-read-based coding and non-coding RNA-seq analysis
• Bulk DNA/RNA-seq data and downstream analysis
• Variant calling and functional annotation
• Genome editing and CRISPR-Cas9 data analysis
• Metagenomics and metatranscriptomics analysis

We deliver healthcare and pharma intelligence modules that support drug discovery, biomarker programs, and precision medicine workflows.

• 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 techniques
• Personalized medicine
• Drug interaction and adverse event monitoring
• Predictive analytics
• Disease-specific biomarker identification
• Data curation and custom database design
• SAR/QSAR studies

We build multi-omics analysis stacks that integrate single-cell and spatial data into actionable biological product insights.

• 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 analysis, including preprocessing raw spatial data, alignment, and gene expression quantification
• Integration of multi-omics datasets to reveal relationships between biomolecules and disease phenotypes

Global Brain engineers complex software products from the ground up. We deliver full-cycle product development—from concept definition and architecture to launch-ready platforms—with strong engineering quality and release discipline.

Our execution model combines modern software engineering, product-focused UI/UX design, and applied machine learning to ship reliable, scalable digital products.

We engineer cloud foundations for digital products with security, automation, and scale built in from day one.

• We deploy cloud-native products and applications on AWS, GCP, and Azure.
• We follow DevSecOps, treating security as an integral part of software engineering and release management.
• Our team builds highly scalable infrastructure that processes terabytes of data daily.
• We design multi-region, high-availability, multi-tenant platforms that can serve millions of requests with reliability.

Our product environments process massive and fast-changing data streams— including health sensor feeds, high-frequency financial events, and global weather records. We build terabyte-scale pipelines and distributed processing services to keep latency and throughput in production ranges.

We select storage architectures based on workload pattern, access requirements, and scale profile. Our engineering stack includes MongoDB, SQL (PostgreSQL, MySQL), Redis, DynamoDB, Neo4j, Memcached, and InfluxDB.

We build multi-tenant SaaS and PaaS products that improve cloud utilization while enforcing strict tenant-level isolation and access control.

Our orchestration engines capture workload requirements (CPU, RAM, GPU, code packages) and automatically provision runtime infrastructure for execution, experimentation, and data generation.

We engineer physiological signal processing modules for digital health products, covering ECG, EEG, BCG, PPG, EMG, and accelerometry. Our work powers FDA-grade algorithms including heart-rate detection, blood oxygen estimation, sleep stage classification, brain-wave decomposition, and precise signal synchronization.

Our product engineering process optimizes signal-to-noise performance across wearable and non-wearable devices, improving reliability in real-world usage.

We build geophysical signal processing pipelines for seismic streams, satellite imagery, and sensor networks to improve downstream model accuracy while optimizing runtime performance.

Our implementations combine SciPy and optimized C++ modules for high-throughput, low-latency processing in production environments.