AI & Data Engineering Services.

Structured/unstructured data, APIs, events, logs, batch imports

Tech: Kafka, Kinesis, AWS Glue, Airflow, Lambda

Outcomes: scalable, reliable ingestion

Cloud-native storage, S3 lakehouse, Redshift, Snowflake, Athena

Outcomes: unified, AI-ready datasets

Cleaning, validation, feature extraction, enrichment

Tools: Spark, Glue, Lambda, dbt

Outcomes: reliable, business-aligned data

Schema enforcement, PII masking, lineage, compliance

Outcomes: trusted, auditable datasets

Model training, hyperparameter tuning, NLP, vision, forecasting

Frameworks: PyTorch, TensorFlow, Scikit-learn

Outcomes: deployable, accurate ML models

ChatGPT-style agents, document intelligence, workflow automation

Tools: GPT-4o/5, Claude 3, LangChain, LlamaIndex

Outcomes: enterprise-grade AI, automated workflows

Embeddings, hybrid search, source verification, hallucination reduction

Outcomes: accurate, reliable AI responses

Complex workflow automation, dynamic decision-making, self-reflection loops

Outcomes: 5–10x efficiency, reduced manual effort

Chat, classification, embeddings, streams, multi-doc analysis

Outcomes: faster responses, lower GPU cost, high concurrency

Continuous training, drift detection, rollback, versioning, feature stores

Tools: MLflow, Kubeflow, Airflow, SageMaker Pipelines

Outcomes: reliable, long-term model health

Dashboards, KPIs, product insights, forecasting

Platforms: Tableau, Power BI, QuickSight, Looker

Outcomes: faster, data-backed decisions