01
Exploratory Data Analysis
Uncover patterns, anomalies, and relationships in your data through rigorous statistical analysis and visual exploration.
Data Science
Turn Raw Data into Strategic Advantage
From exploratory analysis and statistical modeling to production analytics pipelines and executive dashboards — we help organizations build the data foundation and analytical capability needed to make faster, better decisions.
What We Offer
Data Science Services
End-to-end data science from pipeline engineering to visualization — everything your team needs to go from raw data to reliable insight.
02
Business Intelligence & Dashboards
Design and build interactive dashboards and reporting systems that give decision-makers real-time visibility into KPIs.
03
Statistical Modeling
Apply regression, hypothesis testing, A/B analysis, and causal inference to answer the business questions that matter most.
04
Data Pipeline Engineering
Build robust ETL/ELT pipelines that move, transform, and load data reliably from any source into your analytics warehouse.
05
Data Visualization
Translate complex datasets into clear, compelling visual narratives that drive alignment and action across your organization.
06
Analytics Engineering
Structure your data warehouse with dbt, define metrics layers, and establish a single source of truth for all business reporting.
Proven Results
Our Impact in Numbers
We help organizations deliver measurable results through scalable software solutions.
Data Projects
TB Processed
Faster Insights
Data Sources
Our Clients
Why Choose Us
Why Data-Driven Companies Choose SolveJet
End-to-End Data Engineering
We build the pipelines before the models — ingestion, transformation, warehousing, and governance — so your analytics always run on clean, reliable data.
Statistically Rigorous
Every analysis is grounded in sound statistical methodology. We validate assumptions, quantify uncertainty, and communicate confidence intervals — not just point estimates.
Full-Stack Analytics
From raw data in object storage to interactive dashboards in Looker, Tableau, or custom React — we own the entire analytics stack.
Cloud-Native Architecture
BigQuery, Snowflake, Databricks, Redshift — we design for the platform you already use and optimize for cost and query performance from day one.
Embedded Domain Expertise
Our data scientists bring vertical knowledge in insurance, fintech, logistics, and retail — so models reflect real business logic, not just statistical patterns.
Governance & Compliance
Data lineage tracking, role-based access, PII masking, and audit trails built into every pipeline to meet GDPR, HIPAA, and SOC 2 requirements.
Industries We Serve
Data Science Across Industries
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Insurance
Insurance
Data Science for Insurance Analytics
Insurance generates some of the richest structured datasets in any industry. We help carriers and MGAs build actuarial models, loss ratio dashboards, and claims analytics pipelines that turn policy and claims data into competitive pricing and underwriting advantage.
Loss ratio analyticsActuarial modelingClaims dashboardsPortfolio reporting
Trusted by
Our Process
How We Work
Data Discovery
1 WeekAudit existing data sources, assess quality and completeness, identify gaps, and define the analytical questions to answer.
Data Engineering
2-3 WeeksBuild ingestion pipelines, clean and transform raw data, and load it into a structured warehouse or lakehouse architecture.
Exploratory Analysis
1-2 WeeksProfile distributions, identify correlations, surface anomalies, and generate hypotheses that guide the modeling phase.
Modeling & Analysis
2-4 WeeksApply statistical models, run experiments, validate findings, and quantify the business impact of each insight.
Visualization & Reporting
1-2 WeeksBuild dashboards, automated reports, and self-serve analytics tools that put insights directly in the hands of stakeholders.
Operationalize & Monitor
OngoingSchedule pipelines, set up data quality alerts, monitor KPI drift, and iterate as business questions evolve.
Insurance·Agentic AI
- 64%
- Reduction in ramp time
- 70%
- Less manager coaching
Cutting agent ramp time from 11 weeks to 4 using AI voice roleplay training
A US P&C carrier onboarding 40–60 new agents per quarter was losing 11 weeks of productive capacity per agent to classroom training. An AI voice simulator with 6 customer personas and automated scorecards cut ramp time to 4 weeks.
Read Case StudyFintech·Machine Learning
- 76%
- Fewer false positives
- $2.1M
- Compliance cost eliminated
Cutting AML false positive alerts by 76% without increasing regulatory risk
A US payment processor handling $2.4B in annual transaction volume was generating 1,200+ AML alerts per day — 96% false positives. An ML scoring engine reduced false positives by 76% while improving true positive detection.
Read Case StudyRetail·Intelligent Automation
- 34%
- Abandoned revenue recovered
- 4.2x
- Revenue per recovery email
Recovering 34% of abandoned revenue through multi-signal conversion automation
A US DTC brand generating $40M+ in annual online revenue was recovering less than 6% of abandoned cart value from a single generic email. A multi-signal automation system recovered 34% of previously lost revenue within 90 days.
Read Case StudyManufacturing·Machine Learning
- 67%
- Reduction in unplanned downtime
- $4.1M
- First-year savings
Reducing unplanned downtime by 67% through ML-based predictive maintenance
A precision parts manufacturer with 340+ hours of unplanned downtime annually — at $18,000/hour — had two years of sensor data sitting unused. An ML system now predicts failures 6–18 hours in advance, delivering $4.1M in first-year savings.
Read Case StudyFAQ
Frequently Asked Questions
Find answers to common questions about our services
Data science is the broader discipline — it covers data collection, cleaning, exploration, statistical analysis, visualization, and communication of insights. Machine learning is a subset focused specifically on building predictive models. A data science engagement might not involve any ML at all; it might be a statistical analysis, a dashboard, or a data pipeline. We scope each project based on what the business question actually requires.
Not necessarily. We can work with data in its current state — flat files, operational databases, APIs, or cloud storage. However, for ongoing analytics work we typically recommend building a lightweight warehouse (BigQuery, Snowflake, or Redshift) early in the engagement. It pays back quickly in query speed, cost, and analyst productivity.
Data quality is addressed in the engineering phase before any analysis begins. We profile every dataset for completeness, consistency, and accuracy, document known issues, and implement validation rules in the pipeline. For ongoing pipelines we set up automated data quality checks that alert when anomalies appear.
We work with Looker, Tableau, Power BI, Metabase, and Superset for managed BI tools, and build custom dashboards in React with Recharts or D3 when product-embedded analytics are needed. Tool selection is driven by your existing stack and the technical sophistication of your end users.
Yes — most of our engagements are collaborative. We embed alongside your analysts and engineers, contribute to shared codebases, follow your existing conventions, and transfer knowledge throughout the project. We can also provide fractional data science capacity to augment a small internal team.
Every analysis starts with a defined business question and a clear owner who will act on the output. We present findings in business terms, quantify impact in revenue or cost terms where possible, and recommend specific next steps. We avoid analysis for its own sake — if a finding does not change a decision, we deprioritize it.