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AI & ML Development

Architecting
Intelligent Systems

Go beyond simple automation. We engineer end-to-end AI/ML solutions that predict trends, optimize operations, and create competitive advantages.

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Efficiency Gaps

Is Your Data
Working For You?

Most enterprises are data-rich but insight-poor. Without specialized AI/ML engineering, high-value opportunities remain hidden in your datasets.

Reactive Decisions

Relying on backward-looking reporting instead of forward-looking predictive models leads to missed market shifts.

Operational Friction

Manual document processing, redundant workflows, and inefficient resource allocation draining enterprise profit.

Customer Churn

Failing to personalize user experiences or predict churn before it happens results in stagnant growth.

Fraud Vulnerability

Static rules-based systems are easily bypassed by sophisticated modern fraud patterns and cyber threats.

Siloed Insights

Valuable insights trapped in unstructured data formats like images, audio, and documents remain inaccessible.

Performance Bottlenecks

Inefficient supply chains and production lines failing to optimize due to a lack of real-time intelligent forecasting.

Our ML Spectrum

Production-Grade
ML Solutions

We engineer high-performance machine learning systems that transform complex data into autonomous decision-making and predictive foresight.

Predictive Analytics

End-to-end forecasting models that predict market trends, customer behavior, and equipment failure before they occur.

Computer Vision Systems

Automated visual inspection, facial recognition, and object detection for security, healthcare, and manufacturing QA.

NLP & Cognitive APIs

Extracting meaning from unstructured text through custom sentiment analysis, NER, and multi-language translation engines.

Custom Neural Networks

Developing bespoke deep learning architectures using PyTorch and TensorFlow for your proprietary business logic.

Robust MLOps Pipelines

Automating the ML lifecycle from data ingestion to model deployment, monitoring, and continuous retraining.

Anomaly Detection

Real-time identification of outliers and fraudulent patterns in financial transactions and network traffic.

Our AI/ML Tech Stack

We leverage a diverse and powerful ecosystem of frameworks and infrastructure to build robust ML solutions.

ML Frameworks

  • PyTorch
  • TensorFlow
  • Scikit-Learn
  • JAX / Keras

Computer Vision

  • OpenCV
  • YOLO / Detectron2
  • MediaPipe
  • Roboflow

Data Engineering

  • Pandas / NumPy
  • Apache Spark
  • Airflow
  • Snowflake / dbt

Cloud AI Services

  • AWS SageMaker
  • Azure Machine Learning
  • Google Vertex AI
  • Weights & Biases

Natural Language

  • Hugging Face
  • SpaCy / NLTK
  • LangChain
  • Gensim

Vector Search

  • Pinecone
  • Milvus
  • FAISS
  • ChromaDB

Visualization

  • Plotly / Dash
  • Grafana
  • Streamlit
  • Jupyter Hub

Deployment & MLOps

  • Docker Containers
  • Kubernetes (EKS/GKE)
  • FastAPI / Node.js
  • Kubeflow / MLflow

Why Trust Constelly for
AI & ML Engineering?

We deliver production-ready AI solutions that prioritize high-precision accuracy, extreme performance, and ethical data handling.

Production-First Accuracy

Our models are stress-tested against real-world data noise to ensure they maintain 95%+ precision in production.

Explainable AI (XAI)

We don't build "black boxes." Our solutions include interpretation layers so you understand WHY decisions are being made.

Continuous ML Retraining

Our MLOps pipelines automatically detect model drift and trigger retraining to prevent accuracy decay over time.

95%+

Avg. Model Precision

40%

OpEx Reduction

10M+

Daily Predictions

5x

Scaling Capability

Frequently Asked Questions

Any data-driven business can benefit. Common sectors include Finance (fraud detection), Retail (recommendation engines), Healthcare (diagnostic AI), and Manufacturing (predictive maintenance).
We prioritize data sovereignty. Your data is processed within secure VPC environments, and we implement strict data masking and isolation protocols to prevent leaks.
AI is the broad concept of machines being able to carry out tasks in a way that we would consider "smart." Machine Learning (ML) is an application of AI that gives systems the ability to learn and improve from experience.
A Proof of Concept (PoC) can typically be built in 4-6 weeks. Production-ready deployments with full MLOps integration usually take 3-5 months.
MLOps (Machine Learning Operations) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.
Yes, we specialize in building "AI-first" features for legacy systems through microservices and API-driven architectures.
The quality and quantity of data depend on the use case. We can perform a Data Maturity Audit to help you identify what you have and what you need to collect.
ROI is measured through several KPIs: reduction in manual labor hours, improvement in prediction accuracy over previous methods, decreased operational costs due to automation, and increased revenue from personalized customer targeting.
We are experts in the Big Three: AWS (SageMaker), Google Cloud (Vertex AI), and Microsoft Azure (Azure ML). We also support hybrid and on-premise deployments for highly sensitive data requirements.
Not necessarily. While data is fuel for AI, techniques like Transfer Learning and Zero-Shot learning allow us to build high-performance models using smaller, high-quality datasets. We help you audit your data to determine the best approach.

Unlock the Power of Your Data

Leverage advanced Machine Learning algorithms to predict trends and optimize operations. Turn raw data into actionable business intelligence.

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