Your AI Strategy Is Only as Strong as Your Data Foundation
Most organizations are sitting on fragmented, siloed, and inconsistent data that makes meaningful AI adoption impossible. Sonatafy cleans the foundation first, then builds intelligent systems on top of it. This is not a science experiment. It is a structured, delivery driven approach to turning raw data into a competitive advantage.
The Data Debt Trap
Most Companies Try to Build AI on Top of Broken Data. It Never Works.
Engineering teams spend 60% or more of their time on data plumbing instead of product delivery. Analytics tools produce contradictory outputs because the underlying data is not trustworthy. AI initiatives that look promising in demos break in production because the data foundation was never ready. And executives make critical decisions based on stale spreadsheet reports that were manually compiled days ago.
Data transformation is not optional. It is the prerequisite for everything else.
You cannot automate what you cannot trust. And you cannot trust what you have not governed.
Service Pillars
Four Capabilities. One Governed Foundation.
Whether you need a data strategy, modern pipelines, executive dashboards, or production AI, each capability builds on the one before it.
Delivered through the same Managed Delivery POD model that powers our software engineering engagements. US principal data and AI engineer paired with 2 to 4 senior LATAM engineers, US time zone aligned.
Executive Dashboard Framework
Role Specific Dashboards That Drive Decisions, Not Just Display Data
Every dashboard is tailored to the information needs of each stakeholder level, pulling from the data sources that matter most to their role.
Engagement Tiers
From Discovery to Continuous Value
Every engagement starts with a paid Discovery phase that produces an actionable roadmap. No six month assessment before any action.
How We Compare
Operators, Not Advisors. Builders, Not Theorists.
Most data consulting firms deliver strategy decks. Sonatafy delivers production infrastructure.
Case Study
PE-Backed Portfolio Company: Unified Data Visibility
Is This the Right Engagement?
Data Transformation Works When the Gap Is Foundation, Not Features
If your data infrastructure is sound and the challenge is connecting systems or automating workflows, you may need a different engagement. You will hear that in the first conversation.
Target Outcomes
What Changes When the Data Foundation Is Right
Unified, governed data infrastructure that supports reliable analytics and AI
50%+ reduction in manual data reconciliation and reporting effort
Predictive capabilities for revenue forecasting, churn prediction, and demand planning
Engineering teams freed from data plumbing to focus on core product delivery
Board and investor ready reporting automated and refreshed in real time
Production deployed AI/ML models tied to measurable business metrics
The Connected Path
Data Foundation First. Then Intelligent Automation.
Many organizations start with Data Transformation to build the governed foundation, then layer AI Process and Workflow Automation on top to connect that data to operational systems and executive visibility.
Not sure which offering fits? You will hear what fits in the first conversation.
Industry Impact
Data Problems We Solve Across Industries
Clinical data pipeline is fragile and manually maintained.
Governed HIPAA-compliant pipelines with automated quality monitoring.
Executives make decisions based on stale spreadsheet reports.
Real-time dashboards with automated refresh. Revenue and ops metrics visible instantly.
Operating partners need standardized visibility across portfolio.
Unified portfolio dashboard. Engineering metrics aggregated across all companies.
Data from CRM, ERP, support, and engineering tools is siloed.
Governed data warehouse. Cross-functional executive visibility.
Often Paired With
Data Transformation Often Pairs With These Engagements
Frequently Asked Questions
Common Questions About AI and Data
Do we need clean data before starting an AI project?
No. The first phase of every AI engagement is data assessment and governance. We build the foundation, then the intelligence layer.
How long does a full data transformation take?
Foundation engagements typically run 3 to 6 months. The first executive dashboards and governed data products are usually delivered within 6 to 8 weeks.
Can you work with our existing AI initiatives?
Absolutely. Many clients come to us with proof of concepts that stalled before reaching production. We assess what exists and build the path to production grade deployment.
What AI models do you use?
We are model agnostic. Depending on requirements, we deploy OpenAI, Anthropic, open source models, or domain specific fine tuned models. The choice depends on cost, latency, compliance, and accuracy requirements.
How much does an AI and data engagement cost?
Data foundation and transformation engagements typically range from $20K to $50K per month depending on data complexity, source count and compliance requirements.
How do you measure ROI on AI initiatives?
Every engagement starts with defined business outcomes, whether that is reduced manual processing time, improved forecast accuracy, faster reporting cycles or operational cost reduction. We track measurable impact against those baselines, not vanity metrics.
What is agentic AI and do you build it?
Agentic AI refers to systems that take action autonomously within defined boundaries, not just analyze and recommend. Yes, we build agentic workflows including automated data pipelines, intelligent escalation systems and self optimizing processes, all under human oversight and governance frameworks.
How do you handle data security and privacy?
All data stays within your infrastructure unless explicitly agreed otherwise. We follow encryption at rest and in transit standards, role based access controls and compliance frameworks including SOC 2, HIPAA and GDPR as applicable.
Do we need a data engineering team in place?
No. Sonatafy provides the data engineering expertise as part of the engagement. If you have existing data engineers, we work alongside them and structure the handoff so they can maintain and extend what we build.
What is the difference between a proof of concept and production deployment?
A proof of concept validates that an AI approach works with your data. Production deployment means it is integrated into your systems, monitored, governed, scalable and maintained. Many clients come to us with POCs that stalled before reaching production. We specialize in closing that gap.
Diagnose your AI position
Two diagnostics matched to this page.
Free · confidential · no sales call
AI Data Maturity Assessment
Evaluate whether your data foundation is ready to support production AI workloads, or will become the bottleneck.
Process Automation and Agentic AI Assessment
Identify where agentic AI and workflow automation can eliminate manual coordination and unlock engineering capacity.
Ready to Fix Your Data Foundation?
Your Data Is Either a Competitive Advantage or a Liability. There Is No Middle Ground.
A 2 to 4 week Discovery phase can show you exactly where the gaps are, what the target architecture looks like, and what the first 90 days of execution deliver.
Every decision made on stale data is a decision made in the dark. A 2 to 4 week Discovery engagement shows you exactly where the gaps are.
Discovery engagements start at $20K. Foundation engagements from $25K per month.