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Platform & Data for the AI Era

DevOps, data pipelines, and cloud infrastructure in one engineering practice—so your apps scale, your data flows in real time, and your AI agents have something reliable to work with.

DevOps, Data & Intelligence

The AI-first enterprise runs on two foundations: reliable platform engineering and clean, fast data. At TESARK, we ship GitOps-driven CI/CD, infrastructure-as-code on AWS and GCP, real-time streaming with Kafka, modern lakehouse and warehouse layers (Snowflake, ClickHouse, BigQuery), and vector-ready pipelines that feed RAG and agent workflows. From FinOps and observability to RBAC and encryption-at-rest, we build the infrastructure your products and AI systems depend on.

Core Capabilities

  • Platform Engineering & GitOps: Kubernetes, Terraform, and GitOps workflows (ArgoCD-style pipelines) that automate deployments, enforce policy, and keep environments consistent from dev to prod.
  • Real-Time ETL/ELT & Streaming: Kafka-backed event pipelines and Airflow-orchestrated jobs that move data in minutes—not days—with idempotent transforms and monitoring built in.
  • Lakehouse & Warehousing: Snowflake, ClickHouse, and BigQuery architectures optimized for analytics, dbt-driven transformations, and cost-efficient query performance at scale.
  • Data Governance & Security: RBAC, encryption-at-rest and in-transit, audit logging, and compliance-ready data access patterns for regulated industries.
  • AI-Ready Data Layers: Embedding pipelines, vector stores (pgvector and dedicated vector DBs), and feature stores that keep RAG and agent systems fed with fresh, governed data.

Frequently Asked Questions

Between ETL and ELT, which is better?
Most teams today use ELT—load raw data into Snowflake or BigQuery first, then transform with dbt—leveraging warehouse compute instead of maintaining heavy extract pipelines.
How do you ensure data security during migration?
Encrypted transfers, least-privilege IAM, staged cutovers with rollback plans, and audit logging throughout. Sensitive fields can be tokenized or masked before they reach the target environment.
Do you build vector and embedding pipelines for RAG?
Yes. We design chunking strategies, embedding jobs, vector storage (pgvector or dedicated vector DBs), and refresh schedules so your AI agents and search systems stay current with governed data.
Get a Technical Consultation
ENGINEERING_STACK
AWS logo AWS
GCP logo GCP
Kubernetes logo Kubernetes
Docker logo Docker
Snowflake
ClickHouse
Apache Kafka
Airflow
dbt
Terraform
Python

Strategic Use Cases

FinTech Data Unification

Scenario: A scaling FinTech had data siloed across 12 third-party services, making consolidated reporting impossible.

Outcome: Built a Snowflake lakehouse with dbt transforms and real-time ingestion. Reporting dropped from 48 hours to under 10 seconds.

E-commerce Real-time Personalization

Scenario: A global fashion brand relied on batched data updates every 24 hours, limiting personalization quality.

Outcome: Implemented Kafka streaming with sub-500ms event processing, driving a 12% lift in average order value through instant recommendations.

AI RAG Data Layer

Scenario: An enterprise wanted internal document Q&A but lacked clean, chunked, and embedded data for retrieval.

Outcome: Shipped an embedding pipeline with pgvector storage, scheduled refresh jobs, and RBAC—powering a production RAG agent with governed access to 100K+ documents.

Cloud Migration & FinOps

Scenario: A legacy on-prem stack was costly to maintain and couldn't scale for peak traffic or new AI workloads.

Outcome: Migrated to Kubernetes on AWS with Terraform IaC, cutting infra spend 35% while improving deployment frequency from monthly to daily.