Data Analytics Company

Predictive analytics uses historical data and statistical/machine-learning models to estimate future outcomes (e.g., churn, demand, risk) so teams can plan and intervene earlier.

Common use cases include customer segmentation, demand forecasting, churn/propensity modeling, fraud detection, market analysis, and operational optimization.

A data warehouse is a centralized store for integrated, curated data that supports analytics and BI. It enables consistent metrics, faster queries, and governed access.

Structured data is tabular and query-friendly (e.g., databases); unstructured data includes text, images, and audio requiring NLP/CV to analyze.

ETL extracts data from sources, transforms it into analysis-ready formats (cleaning, joins, validation), and loads it into a warehouse or lakehouse for BI and ML.

Yes. We connect to CRMs, ERPs, databases, and SaaS tools via secure APIs/connectors so data flows automatically and processes remain in one place.

Healthcare, finance, retail, manufacturing, logistics, and education benefit through compliance reporting, supply-chain visibility, personalization, and cost control.

Typical timelines: 12–16 weeks for focused analytics; large, multi-domain programs can take several months. Agile sprints deliver value in increments.

Start with a discovery call. We assess goals and data, define KPIs, and propose a roadmap covering architecture, tools, and quick-win use cases.

Costs vary by scope and data complexity. Indicative ranges: $30k–$60k for a pilot, $80k–$200k+ for enterprise rollouts.

Most clients see a first production dashboard or ML model in 8–12 weeks; full rollout typically completes in 12–16 weeks.

Yes. You retain ownership of data, models, and code. We build in your cloud tenancy or on-prem to ensure control and portability.

Yes. We deploy in your VPC/on-prem with encryption, RBAC, audit logs, and compliance with SOC 2, HIPAA, and GDPR.

Clouds: AWS, Azure, GCP. Platforms: Snowflake, BigQuery, Databricks, Redshift, Power BI, Tableau, and Looker.

We apply FinOps practices such as usage monitoring, auto-suspend, right-sizing, and workload tiering.

Role-based access, row/column-level security, encryption, audit trails, lineage, PII masking, and policy automation.

Our analytics specialists work from California, Noida (Delhi-NCR), and Dubai. We assign teams based on your region and project complexity, with overlap hours for real-time collaboration. Most North American clients work primarily with our US-based architects, while our India team handles heavy data engineering during your off-hours to accelerate timelines.

You'll see your first working dashboard or predictive model within 8-12 weeks. We start with discovery workshops to map your data sources and business questions, then build a Modern Data Platform on Snowflake or BigQuery. Our Agile sprints deliver incremental value every two weeks, so you're making data-driven decisions before the full system rolls out. Clients using our Analytics-as-a-Service offering can go live in just six weeks since we handle infrastructure and hosting.

The cost of building a business application is shaped by several variables, such as the complexity of features, system architecture, external integrations, security expectations, and scalability requirements.

Typical investment ranges include:

• MVP: US $50,000 – $100,000 (core features to validate business value)
• Medium-scale applications: US $100,000 – $250,000 (advanced functionality, integrations, and scalability)
• Large / Enterprise-grade solutions: US $250,000 – $500,000 (complex architectures, high security, and enterprise integrations)

We partner with you to align technical solutions with business objectives, providing clear cost estimates and a structured roadmap. Our focus on scalability, security, and performance ensures long-term application success. Feel free to schedule a call to discuss your requirements and define a customized development plan.