How a Data and Analytics Startup Built a Scalable, AI Enabled Platform with Sonatafy

The company's product vision centered on transforming large volumes of information from external sources into useful business intelligence.

Those sources contained different data formats, identifiers, structures, and levels of consistency.

Combining them into meaningful insights required an architecture capable of managing data ingestion, transformation, normalization, and analysis across the product.

The platform also needed to support different customer experiences.

Some users would interact with information through a web application, using visualizations and analytical tools. Others would need to integrate the company's capabilities directly into their existing applications and workflows.

AI introduced another opportunity: helping customers explore complex information through natural language while maintaining a connection to the platform's underlying analytical data.

To advance the product, these capabilities needed to be developed together rather than as unrelated technical initiatives.

Sonatafy worked with the company to connect the technical foundation, customer experience, and emerging AI functionality through a coordinated product engineering approach.

Sonatafy provided technical and architectural guidance as the product moved beyond its early concept.

The consulting work addressed technical feasibility, architecture decisions, platform evolution, and the operational requirements of a growing software business.

A central priority was establishing a shared data foundation that could support the company's application, analytics, APIs, and AI capabilities.

Creating a Consistent Analytical Foundation

When information originates from multiple independent sources, the same business entity can appear in different formats or under different identifiers.

Without consistent data processing, these differences can affect how information is aggregated and presented.

Sonatafy helped design an architecture that brought data ingestion, transformation, entity resolution, and analytical modeling together.

The approach supported consistent information across product features while allowing the platform's underlying services to evolve as the business grew.

Supporting Multiple Product Experiences

The company wanted its analytical capabilities to be available through an interactive SaaS application and through integrations with external customer systems.

Sonatafy helped establish a platform architecture supporting both experiences through shared underlying services and data.

This approach allowed the business to develop multiple ways for customers to consume its intelligence without duplicating the entire analytical foundation.

Connecting AI with Trusted Data

The company's AI strategy required more than placing a conversational interface over its product.

AI capabilities needed access to the analytical foundation so responses and workflows could draw on the information processed by the platform.

Sonatafy helped integrate generative AI and machine learning functionality with the platform's existing data and analytics capabilities.

The result was a more connected approach to AI development, with natural language experiences supported by curated analytical information.

Sonatafy's engagement brought together product engineering, data engineering, analytics, design, AI, and delivery management through a coordinated managed team.

The work involved interconnected development initiatives that contributed to the same product.

Rather than treating data processing, application features, and AI as separate projects, the team worked across the platform to support consistent architecture and product evolution.

Data Engineering and Analytics

A foundational area of the engagement involved developing the capabilities required to consolidate information from multiple external sources.

Sonatafy helped build and evolve processes for ingesting, transforming, cleansing, normalizing, and enriching data.

The team also implemented entity resolution capabilities that helped connect related records across different datasets.

Reusable analytical models created consistent definitions and calculations that could support multiple areas of the product.

This provided a foundation for customer facing analytics and additional product capabilities.

Application and Product Development

Sonatafy helped build the company's web application, connecting user experiences with the underlying data platform.

The work included application functionality, analytical views, visualizations, authentication, and user workflows.

UX/UI design was integrated into product development, helping translate complex analytical information into experiences customers could navigate and understand.

Sonatafy also helped establish the API capabilities needed to make the platform accessible through external integrations.

This supported the company's vision of offering analytical intelligence through both its own application and customer systems.

AI and Machine Learning

AI development became an integrated component of the product engineering engagement.

Sonatafy connected generative AI and machine learning capabilities with the platform's analytical environment.

The functionality included natural language interaction, retrieval, semantic analysis, classification, forecasting, and analytical enrichment.

By building AI on the same analytical foundation supporting the broader product, the company could develop AI enabled experiences connected to its underlying data capabilities.

Cloud Architecture and Enterprise Readiness

As the platform evolved, Sonatafy supported improvements to its cloud architecture and operational foundation.

The engagement included technical practices related to scalability, identity management, access controls, monitoring, and application reliability.

These capabilities supported the company's continued evolution toward a platform intended for enterprise customers.

Several technical challenges shaped the engagement.

Consolidating Different Data Sources

External information rarely arrives in one consistent format.

Sonatafy helped build the ingestion and transformation capabilities needed to bring diverse information into a standardized analytical environment.

This allowed product features to work with processed information rather than independently interpreting raw data sources.

Connecting Related Information

Data from separate sources may refer to the same real world entities using different identifiers.

Sonatafy implemented entity resolution capabilities to support more consistent relationships and aggregation across datasets.

This strengthened the platform's ability to present connected analytical information.

Maintaining Consistency as the Product Expanded

As new features were developed, the underlying calculations and data definitions needed to remain consistent.

Reusable transformations and analytical models helped establish shared logic across the platform.

This supported the development of additional functionality without requiring each feature to recreate the same analytical processing.

Making Insights Available in Different Ways

The product needed to serve both customers using the application directly and customers integrating its capabilities into other systems.

Sonatafy helped create an architecture supporting SaaS experiences and programmatic consumption through shared platform services.

Making AI Part of the Product

The company wanted AI to enhance the usefulness of its analytical information.

Sonatafy integrated AI capabilities with the curated data environment, supporting natural language and machine learning experiences grounded in the platform's analytical foundation.

These initiatives reflect the broader engineering challenge of building a data product: the application, data architecture, analytics, and AI must evolve together.

Building a modern data and analytics product requires several technical disciplines to make coordinated decisions.

Data engineering determines how information is collected and prepared. Application engineering turns that information into usable product functionality. Analytics creates consistent intelligence, while AI introduces new ways for customers to interact with it.

Architecture and delivery management keep those activities aligned with the company's product direction.

Sonatafy's managed product engineering approach connected these disciplines within one engagement.

The company received technical guidance and coordinated development across its data platform, customer application, analytical capabilities, and AI functionality.

This allowed architecture and product implementation to advance together as the company refined its commercial offering.

For Sonatafy, the engagement reflects its approach to complex software initiatives: combine experienced technology consulting with managed delivery across the disciplines required to achieve the client's product objectives.