Custom Data
Engineering Services

Enterprise LLMs underperform not because they lack parameters but because the data flowing into them is misaligned, mislabeled, or lost in storage. We design retrieval pipelines, context chunking logic, streaming outputs that feed LLM fine-tuning, reinforcement learning systems, and intelligent agents.

Lagging pipelines cause delays in fraud detection, market pricing, and operational forecasting. We build real-time data streams for fraud engines, ML feedback loops, logistics automation, and AI applications, deployed via Kafka, Pulsar, or cloud-native infrastructure.

You can’t fix what you can’t see. We build lineage-aware observability systems that surface schema drift, freshness issues, and silent failures across ingestion and ML inputs—before they skew outcomes or trigger compliance risk.

Fast retraining, consistent metrics, and defensible models require versioned, cataloged data features. We build centralized feature stores with semantic tags, version tracking, lineage, and reuse logic—fully aligned with model pipelines and governed access.

The only way to move fast in AI is to build on stable ground. We engineer role-based access controls, audit trails, schema registries, and contract enforcement that meet GDPR, HIPAA, SOC 2, and internal compliance standards—without slowing down teams.

We design and build modular, cloud-agnostic infrastructures that reduce vendor lock-in, compress compute cost, and support region-specific data policies. Our data engineering services company builds directly on Snowflake, BigQuery, Redshift, or your hybrid stack.

Stable pipelines don’t just run—they manage dependencies, reruns, alerts, retries, and lineage. We implement orchestration frameworks (Airflow, Dagster, dbt, or custom logic) that simplify complexity and prevent silent failures from going unnoticed.

Centralized teams break at scale. We build decentralized data platforms that support federated ownership, contract-first development, domain-specific pipelines, and shared observability. Every domain can publish, manage, and document its data products—without silos or chaos.

Due to risk, interdependencies, and undocumented logic, migrating from legacy systems can stall for years. We design low-risk transition plans, build shadow pipelines, map dependencies, and execute seamless cutovers that reduce downtime and preserve continuity.

Executives need more than raw pipelines—internal data APIs that serve business teams with scoped, reusable, discoverable data products. We define, version, publish, and monitor these assets to enable strategic reuse and standardization across the organization.

We build modern lakehouses with structured zones, schema enforcement, data discovery tools, and semantic access layers so that analysts get the correct data every time, without asking three teams and waiting two weeks.

We rebuild legacy ETL into production-ready ingestion pipelines—modular, idempotent, testable, and observable. These pipelines are designed to reduce fragility, improve traceability, and eliminate the need for manual patches every time the schema changes.

Insufficient data looks fine—until the dashboard lies. We build automated validation systems that test business rules, contract schemas, and anomaly detection points at ingestion, transformation, and delivery—so you know what’s real before making decisions.