Enterprise-Grade Data Analytics &
AI Insights with TechAhead

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 depending on data volume and complexity.

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

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

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

We apply FinOps practices: live usage monitoring, auto-suspend, right-sizing, and workload tiering—typically lowering TCO by 25–40%.

A warehouse stores curated, structured data for BI; a data lake stores raw structured/semistructured/unstructured data; a lakehouse combines both—open formats with ACID tables, governance, and BI/ML performance.

We implement data contracts, validation rules, anomaly detection, and SLA monitoring; issues trigger alerts and remediation playbooks.

Yes. We build streaming pipelines with Kafka/Kinesis and serve low-latency dashboards/models using materialized views and feature stores.

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

Typical outcomes include 10–20% inventory reduction, 5–8% conversion lift, and 15–30% productivity gains depending on use case and baseline.

We offer tiered support, 24×7 incident response options, monthly health checks, cost governance reviews, and a backlog for iterative enhancements.