Descriptive, diagnostic, predictive, prescriptive, and real time analytics. BI dashboards, data warehouses, ML pipelines, and AI agents that answer your questions in natural language.
Analytics Track Record
LiveFrom raw data to dashboards to production ML. One team owns the ingest, transform, model, and serve layers.
Historical reporting, KPI dashboards, and ad hoc exploration of your data warehouse. The foundation for every other analytics workload.
Root cause analysis, drill down dashboards, and statistical correlation across business metrics. Find the lever, not just the symptom.
Forecast revenue, churn, demand, and risk. Machine learning models productionized end to end with explainability and monitoring.
Optimization, recommendations, and decision support. Move from forecasts to specific actions your team can take this quarter.
Streaming pipelines on Kafka and Kinesis for live dashboards, fraud detection, and operations monitoring at sub second latency.
Snowflake, Databricks, BigQuery, Redshift architecture. Lakehouse patterns combining the flexibility of lakes with the performance of warehouses.
Ingestion, transformation, and serving pipelines. Airbyte, Fivetran, Airflow, dbt. Developed for reliability and observability.
LLM agents that answer business questions in natural language. RAG over your warehouse. Text to SQL with grounding and guardrails.
Legacy data warehouse to cloud lakehouse migration. Strangler fig pattern for gradual cutover without disrupting reporting.
Where analytics moves the needle on revenue, retention, operations, and risk.
Subscription cohort analysis, retention modeling, expansion forecasting, and pricing optimization for SaaS and product companies.
Churn prediction, lifetime value scoring, segmentation, and journey analytics across acquisition, conversion, and retention.
Attribution modeling, campaign ROI, audience segmentation, and creative performance across paid and organic channels.
Supply chain optimization, demand forecasting, inventory planning, and route optimization for logistics and manufacturing.
Real time fraud detection, AML monitoring, credit risk scoring, and anomaly detection for FinTech and banking.
Clinical outcome analysis, population health analytics, claims processing, and provider performance dashboards.
Feature adoption, funnel optimization, experimentation analysis, and user behavior tracking across web and mobile.
HR analytics, attrition prediction, performance correlation, and workforce planning with privacy aware models.
BI tools, cloud data warehouses, ETL frameworks, streaming engines, and ML platforms. We adopt your stack if it makes sense, otherwise we bring ours.
BI and Visualization
Data Warehouses
ETL and Orchestration
Streaming
ML and AI
Cloud and Storage
Ten verticals with documented compliance fluency for regulated analytics workloads.
HIPAA aware clinical and claims analytics.
Risk, fraud, and regulatory analytics.
Pricing, inventory, customer 360.
Product, revenue, and growth analytics.
Demand and route optimization.
Predictive maintenance and quality.
Audience and content analytics.
Yield management and demand forecasting.
Learner analytics and outcome modeling.
Grid analytics and consumption forecasting.
Pick the model that fits your scope, budget, and risk tolerance.
Defined scope
Fixed budget, fixed timeline, locked scope. Best for one off dashboards and well defined warehouse projects.
Evolving scope
Pay for actual hours. Best when business questions evolve and you need to iterate on models and dashboards.
Sustained delivery
A named data team allocated full time. Data developers, analysts, ML engineers, and BI engineers as needed.
Ongoing support
Monthly retainer for dashboard maintenance, ad hoc analysis, and ongoing model retraining.
Each phase has documented deliverables and a senior data engineer accountable for sign off.
Stakeholder interviews, KPI definition, data source inventory, and gap analysis between current and target state.
Warehouse architecture, data model, dimensional or data vault design, and security and compliance planning.
Ingestion, ETL, transformation, and orchestration. Bronze silver gold medallion layers in dbt with tests.
Power BI, Tableau, or Looker dashboards. ML model development, training, and validation on holdout data.
Business stakeholder UAT, dashboard performance tuning, model accuracy validation, and refinement.
Production deploy, observability dashboards, runbooks, and stakeholder training delivered.
SLA backed support, model retraining cadence, new question intake, and quarterly architecture reviews.
Independently observable outcomes from senior data development and ISO 9001 certified processes.
Analytics Developers
Senior data engineers, analysts, ML engineers, and BI engineers under one roof.
Dashboards Shipped
In Power BI, Tableau, Looker, and custom React dashboards across 35+ countries.
First Dashboard Live
Kickoff to first working dashboard in 4 weeks. KPIs validated, stakeholders aligned.
Code and Model Ownership
You own the pipelines, models, dashboards, and infrastructure. No vendor lock in.
Decipher Zone replaced our 3 day reporting cycle with a real time Snowflake plus Power BI stack in under 12 weeks. The dashboards we built then are still answering questions for our exec team two years later.
Christian Fea
Chief Technology Officer · Monarch
Direct answers on services, costs, timelines, BI tools, migrations, AI integration, and compliance.
Descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, real time streaming analytics, data warehousing and lakehouse, ETL and data development, AI and generative analytics, and data modernization and migration. Every engagement is end to end from raw data to dashboards or production ML services.
Descriptive analytics tells you what happened, with historical KPI dashboards. Diagnostic tells you why it happened, through root cause analysis. Predictive analytics forecasts what will happen, including churn, demand, and revenue models. Prescriptive analytics recommends what to do next, with optimization and decision support. Most engagements combine multiple types.
A first working dashboard typically goes live in 4 to 6 weeks. A full data warehouse plus BI implementation runs 3 to 6 months. ML model productionization adds another 2 to 4 months depending on data readiness. We share a detailed timeline after the discovery and data audit phase.
Costs vary from 15,000 USD for a focused dashboard build to 250,000 USD or more for full data warehouse plus ML implementation. Cost drivers include data source count, transformation complexity, dashboard depth, ML model count, and compliance requirements. We share a detailed estimate after discovery.
BI: Power BI, Tableau, Looker, Metabase, Superset. Data warehouses: Snowflake, Databricks, BigQuery, Redshift, Azure Synapse. ETL: dbt, Airflow, Prefect, Fivetran, Airbyte. We pick based on your team, scale, and existing investments.
Yes. We migrate from Teradata, Oracle Exadata, SQL Server, and on premise Hadoop to Snowflake, Databricks, BigQuery, or Redshift. We use the strangler fig pattern with parity testing to ensure existing reports continue working during cutover.
Yes. We build streaming pipelines on Kafka, AWS Kinesis, Apache Flink, and Spark Streaming for live dashboards, fraud detection, and operations monitoring at sub second latency.
Yes. We build text to SQL agents, RAG over data warehouse, conversational analytics, and embedded LLM features. GPT-4, Claude, and Gemini integrated with grounding, guardrails, and cost controls.
Encryption at rest and in transit, role based access control, audit logs, data masking, and least privilege IAM. For regulated industries we implement HIPAA, PCI DSS, SOC 2, GDPR, and HITRUST controls. Threat modeling at architecture phase.
Yes. Monthly retainer covers dashboard maintenance, new question intake, ML model retraining, data source additions, and quarterly architecture reviews. SLA backed support included.
You own 100 percent of the source code, pipeline configuration, ML models, dashboards, and infrastructure setup. We assign all IP at kickoff. Code is delivered to a client owned repository continuously.
Yes. We work with clients across the US, UAE, Saudi Arabia, Europe, UK, and APAC. Delivery is from India with communication overlap aligned to your business hours. Analytics platforms we have built serve clients in 35+ countries.
Related Capabilities
Explore other stacks, hire models, and capabilities we ship to production for clients in 35+ countries.
Power BI, Tableau, Looker dashboards.
LLM, RAG, agents, computer vision in production.
LLM apps, fine tuning, multimodal generation.
Bespoke web, mobile, SaaS, AI builds end to end.
Django, FastAPI, data, ML, automation.
Spring Boot 3, JDK 21, microservices, Kafka.
NestJS, Express, real time APIs and services.
A 30 minute call with a senior data engineer, a free data audit, and a written estimate within 3 business days.
Share your scope. A senior developer reviews it, walks you through the trade-offs, and sends a written summary after the call. NDA before any details are discussed.
30 minute call. Written summary after. No pitch deck.