Business
Company Foundation & Leadership
Company founding story
Interval was founded in January 2024, with rapid growth to 45+ employees from major technology companies including Meta, LinkedIn, Coinbase, Palantir, Twitter, and Near Protocol. The company was founded after recognizing a major disconnect between enterprises' desire to access data and their ability to distribute and monitor it effectively, particularly in the era of AI transformation.
Company founding story
Interval was founded in January 2024, with rapid growth to 45+ employees from major technology companies including Meta, LinkedIn, Coinbase, Palantir, Twitter, and Near Protocol. The company was founded after recognizing a major disconnect between enterprises' desire to access data and their ability to distribute and monitor it effectively, particularly in the era of AI transformation.
Company founding story
Interval was founded in January 2024, with rapid growth to 45+ employees from major technology companies including Meta, LinkedIn, Coinbase, Palantir, Twitter, and Near Protocol. The company was founded after recognizing a major disconnect between enterprises' desire to access data and their ability to distribute and monitor it effectively, particularly in the era of AI transformation.
What is the meaning behind the name "Interval"?
The name "Interval" represents the concept of bridging gaps - specifically the gap between real businesses and what data/AI/blockchain technologies can accomplish. We position ourselves as the "interval" or bridge that takes the most pragmatic approach to top-tier technology learnings and applies them directly to customers.
What is the meaning behind the name "Interval"?
The name "Interval" represents the concept of bridging gaps - specifically the gap between real businesses and what data/AI/blockchain technologies can accomplish. We position ourselves as the "interval" or bridge that takes the most pragmatic approach to top-tier technology learnings and applies them directly to customers.
What is the meaning behind the name "Interval"?
The name "Interval" represents the concept of bridging gaps - specifically the gap between real businesses and what data/AI/blockchain technologies can accomplish. We position ourselves as the "interval" or bridge that takes the most pragmatic approach to top-tier technology learnings and applies them directly to customers.
Value Proposition
What is Interval's fundamental market thesis?
Interval's core thesis is that cloud infrastructure has created the single largest wealth of digitally native commodities in the form of private enterprise data. Enterprise data is the most valuable asset in the world, particularly for AI companies seeking to train, access, and understand it. We believe enterprises should get maximum value from this data in two ways: using AI to generate insights and licensing it to interested parties.
What is Interval's fundamental market thesis?
Interval's core thesis is that cloud infrastructure has created the single largest wealth of digitally native commodities in the form of private enterprise data. Enterprise data is the most valuable asset in the world, particularly for AI companies seeking to train, access, and understand it. We believe enterprises should get maximum value from this data in two ways: using AI to generate insights and licensing it to interested parties.
What is Interval's fundamental market thesis?
Interval's core thesis is that cloud infrastructure has created the single largest wealth of digitally native commodities in the form of private enterprise data. Enterprise data is the most valuable asset in the world, particularly for AI companies seeking to train, access, and understand it. We believe enterprises should get maximum value from this data in two ways: using AI to generate insights and licensing it to interested parties.
What problem does Interval solve?
We address three critical challenges: data access control, data maturity, and AI impact measurement. The fundamental problem is that AI is only as good as the data you give it, and when data is semantically tagged or organized differently across structures, it needs to be consolidated into a single container. Rather than letting companies access all your data upfront to maybe solve problems later, we help organizations get their most useful data into a fully private local storage environment with proper governance and control.
What problem does Interval solve?
We address three critical challenges: data access control, data maturity, and AI impact measurement. The fundamental problem is that AI is only as good as the data you give it, and when data is semantically tagged or organized differently across structures, it needs to be consolidated into a single container. Rather than letting companies access all your data upfront to maybe solve problems later, we help organizations get their most useful data into a fully private local storage environment with proper governance and control.
What problem does Interval solve?
We address three critical challenges: data access control, data maturity, and AI impact measurement. The fundamental problem is that AI is only as good as the data you give it, and when data is semantically tagged or organized differently across structures, it needs to be consolidated into a single container. Rather than letting companies access all your data upfront to maybe solve problems later, we help organizations get their most useful data into a fully private local storage environment with proper governance and control.
How does Interval position itself against larger platforms?
We position ourselves as accessible - lightweight and fast for organizations that don't primarily sell software. Our assessment is that most organizations won't turn into software-driven businesses overnight. Rather than paying for feature-rich platforms where organizations may not use many capabilities, we focus on essential AI readiness at reduced cost, helping organizations "crawl and walk before running."
How does Interval position itself against larger platforms?
We position ourselves as accessible - lightweight and fast for organizations that don't primarily sell software. Our assessment is that most organizations won't turn into software-driven businesses overnight. Rather than paying for feature-rich platforms where organizations may not use many capabilities, we focus on essential AI readiness at reduced cost, helping organizations "crawl and walk before running."
How does Interval position itself against larger platforms?
We position ourselves as accessible - lightweight and fast for organizations that don't primarily sell software. Our assessment is that most organizations won't turn into software-driven businesses overnight. Rather than paying for feature-rich platforms where organizations may not use many capabilities, we focus on essential AI readiness at reduced cost, helping organizations "crawl and walk before running."
What is Interval's implementation philosophy?
Our approach is pragmatic rather than over-engineered, providing practical solutions instead of solutions that most businesses don't need. We don't recommend wiping the slate clean immediately. Instead, we assess what's already there, put it in a secure private container, apply private AI for business contextualization, and eliminate the need to pay for edge cases in large LLM solutions or unnecessary features in expensive platforms.
What is Interval's implementation philosophy?
Our approach is pragmatic rather than over-engineered, providing practical solutions instead of solutions that most businesses don't need. We don't recommend wiping the slate clean immediately. Instead, we assess what's already there, put it in a secure private container, apply private AI for business contextualization, and eliminate the need to pay for edge cases in large LLM solutions or unnecessary features in expensive platforms.
What is Interval's implementation philosophy?
Our approach is pragmatic rather than over-engineered, providing practical solutions instead of solutions that most businesses don't need. We don't recommend wiping the slate clean immediately. Instead, we assess what's already there, put it in a secure private container, apply private AI for business contextualization, and eliminate the need to pay for edge cases in large LLM solutions or unnecessary features in expensive platforms.
Strategic Partnerships
How does Interval approach strategic partnerships?
Our expansion focuses on leveraging existing relationships while building new partnerships with major conglomerates. We work closely with partners through a phased approach: initial assessment, synthetic data study, sample data mapping, and full implementation with ongoing tuning based on specific business requirements and preferred AI providers. We position ourselves as enablement partners rather than platform replacements, working alongside existing technology investments.
How does Interval approach strategic partnerships?
Our expansion focuses on leveraging existing relationships while building new partnerships with major conglomerates. We work closely with partners through a phased approach: initial assessment, synthetic data study, sample data mapping, and full implementation with ongoing tuning based on specific business requirements and preferred AI providers. We position ourselves as enablement partners rather than platform replacements, working alongside existing technology investments.
How does Interval approach strategic partnerships?
Our expansion focuses on leveraging existing relationships while building new partnerships with major conglomerates. We work closely with partners through a phased approach: initial assessment, synthetic data study, sample data mapping, and full implementation with ongoing tuning based on specific business requirements and preferred AI providers. We position ourselves as enablement partners rather than platform replacements, working alongside existing technology investments.
Technical Questions
Core Technological Architecture
What is Interval's core technology architecture?
The foundation of our technology stack is data ingestion from disconnected enterprise storage environments; second, normalization and classification using semantic ontologies and taxonomies; and third, an intelligence layer that enables business insights through our agentic framework or client-chosen AI systems. The system features LLM-agnostic design compatible with OpenAI, Anthropic, Perplexity, or private models, with agent interoperability to integrate multiple agentic frameworks in pipelines and responses.
What is Interval's core technology architecture?
The foundation of our technology stack is data ingestion from disconnected enterprise storage environments; second, normalization and classification using semantic ontologies and taxonomies; and third, an intelligence layer that enables business insights through our agentic framework or client-chosen AI systems. The system features LLM-agnostic design compatible with OpenAI, Anthropic, Perplexity, or private models, with agent interoperability to integrate multiple agentic frameworks in pipelines and responses.
What is Interval's core technology architecture?
The foundation of our technology stack is data ingestion from disconnected enterprise storage environments; second, normalization and classification using semantic ontologies and taxonomies; and third, an intelligence layer that enables business insights through our agentic framework or client-chosen AI systems. The system features LLM-agnostic design compatible with OpenAI, Anthropic, Perplexity, or private models, with agent interoperability to integrate multiple agentic frameworks in pipelines and responses.
How does the agent framework technically work?
We work with organizations to extract the most valuable data from fragmented data environments into a fully private local storage environment, ensuring data residency compliance. Once data is consolidated, we apply a private framework that addresses a major problem with large models, privacy. We enable either data obfuscation techniques or private models so your data stays local.
How does the agent framework technically work?
We work with organizations to extract the most valuable data from fragmented data environments into a fully private local storage environment, ensuring data residency compliance. Once data is consolidated, we apply a private framework that addresses a major problem with large models, privacy. We enable either data obfuscation techniques or private models so your data stays local.
How does the agent framework technically work?
We work with organizations to extract the most valuable data from fragmented data environments into a fully private local storage environment, ensuring data residency compliance. Once data is consolidated, we apply a private framework that addresses a major problem with large models, privacy. We enable either data obfuscation techniques or private models so your data stays local.
Data Pipeline & Orchestration
How does Interval handle data ingestion and ETL operations?
We currently implement a general orchestrator. Our architecture follows a multi-stage normalization methodology. Supporting both batch and streaming data processing with plugin extensibility to create custom connectors for diverse data sources.
How does Interval handle data ingestion and ETL operations?
We currently implement a general orchestrator. Our architecture follows a multi-stage normalization methodology. Supporting both batch and streaming data processing with plugin extensibility to create custom connectors for diverse data sources.
How does Interval handle data ingestion and ETL operations?
We currently implement a general orchestrator. Our architecture follows a multi-stage normalization methodology. Supporting both batch and streaming data processing with plugin extensibility to create custom connectors for diverse data sources.
What is Interval's data connector capability?
Our Interval data connector includes approximately 600+ connectors for structured and unstructured data. We can either pull data from these platforms into our data staging environment or connect directly to independent software providers feeding into these systems.
What is Interval's data connector capability?
Our Interval data connector includes approximately 600+ connectors for structured and unstructured data. We can either pull data from these platforms into our data staging environment or connect directly to independent software providers feeding into these systems.
What is Interval's data connector capability?
Our Interval data connector includes approximately 600+ connectors for structured and unstructured data. We can either pull data from these platforms into our data staging environment or connect directly to independent software providers feeding into these systems.
How does Interval handle data processing performance and scalability?
Ingestion represents our slowest stage, but post-ingestion processing achieves streaming-speed capabilities with automatic handling of structural changes. Our systems automatically re-evaluate through our proprietary framework and maintain necessary context for discrete task execution. For processing performance we are able to handle over 1 Petabyte of data and more than 1 Billion+ rows of information.
How does Interval handle data processing performance and scalability?
Ingestion represents our slowest stage, but post-ingestion processing achieves streaming-speed capabilities with automatic handling of structural changes. Our systems automatically re-evaluate through our proprietary framework and maintain necessary context for discrete task execution. For processing performance we are able to handle over 1 Petabyte of data and more than 1 Billion+ rows of information.
How does Interval handle data processing performance and scalability?
Ingestion represents our slowest stage, but post-ingestion processing achieves streaming-speed capabilities with automatic handling of structural changes. Our systems automatically re-evaluate through our proprietary framework and maintain necessary context for discrete task execution. For processing performance we are able to handle over 1 Petabyte of data and more than 1 Billion+ rows of information.
Semantic Ontologies & Data Classification
How does Interval handle data classification across diverse data types?
We develop semantic ontologies with our agentic framework, that are specific to your business with contextual generation from industry depth of knowledge.
How does Interval handle data classification across diverse data types?
We develop semantic ontologies with our agentic framework, that are specific to your business with contextual generation from industry depth of knowledge.
How does Interval handle data classification across diverse data types?
We develop semantic ontologies with our agentic framework, that are specific to your business with contextual generation from industry depth of knowledge.
How accurate are Interval's semantic ontologies?
Our ontology development process includes creating synthetic data to validate accuracy with clients before enabling insights products. We can infer ontologies from unstructured data you provide us in the form of excel models and corporate documentation as well.
How accurate are Interval's semantic ontologies?
Our ontology development process includes creating synthetic data to validate accuracy with clients before enabling insights products. We can infer ontologies from unstructured data you provide us in the form of excel models and corporate documentation as well.
How accurate are Interval's semantic ontologies?
Our ontology development process includes creating synthetic data to validate accuracy with clients before enabling insights products. We can infer ontologies from unstructured data you provide us in the form of excel models and corporate documentation as well.
How does Interval's data quality assessment work?
Our private agent framework includes a comprehensive data quality assessment tool, making recommendations for structural improvements, semantic typing, and data enrichments to optimize AI usage.
How does Interval's data quality assessment work?
Our private agent framework includes a comprehensive data quality assessment tool, making recommendations for structural improvements, semantic typing, and data enrichments to optimize AI usage.
How does Interval's data quality assessment work?
Our private agent framework includes a comprehensive data quality assessment tool, making recommendations for structural improvements, semantic typing, and data enrichments to optimize AI usage.
AI/ML Implementation & Training
When you mention "teaching taxonomy to the agentic framework," do you mean prompt engineering or model retraining?
We employ both approaches based on specific use cases, with our primary method being custom tool integration. We guide LLMs to properly invoke, which is technically challenging. We primarily use prompt engineering and reasoning trace improvements for most business intelligence applications.
When you mention "teaching taxonomy to the agentic framework," do you mean prompt engineering or model retraining?
We employ both approaches based on specific use cases, with our primary method being custom tool integration. We guide LLMs to properly invoke, which is technically challenging. We primarily use prompt engineering and reasoning trace improvements for most business intelligence applications.
When you mention "teaching taxonomy to the agentic framework," do you mean prompt engineering or model retraining?
We employ both approaches based on specific use cases, with our primary method being custom tool integration. We guide LLMs to properly invoke, which is technically challenging. We primarily use prompt engineering and reasoning trace improvements for most business intelligence applications.
When does Interval use model training vs. prompt engineering?
We've found that for business intelligence, we only train when absolutely necessary due to expense and time requirements, instead focusing on enabling LLMs to call tools effectively rather than handling everything directly through language models alone.
When does Interval use model training vs. prompt engineering?
We've found that for business intelligence, we only train when absolutely necessary due to expense and time requirements, instead focusing on enabling LLMs to call tools effectively rather than handling everything directly through language models alone.
When does Interval use model training vs. prompt engineering?
We've found that for business intelligence, we only train when absolutely necessary due to expense and time requirements, instead focusing on enabling LLMs to call tools effectively rather than handling everything directly through language models alone.
How does Interval optimize AI performance while managing costs?
We implement a multi-layered cost optimization strategy that avoids relying solely on LLMs by creating custom tools that LLMs learn to call effectively, gravitating toward smaller models rather than larger models. Our approach includes progressive model shrinking where we reduce model sizes as customers data maturity increases.
How does Interval optimize AI performance while managing costs?
We implement a multi-layered cost optimization strategy that avoids relying solely on LLMs by creating custom tools that LLMs learn to call effectively, gravitating toward smaller models rather than larger models. Our approach includes progressive model shrinking where we reduce model sizes as customers data maturity increases.
How does Interval optimize AI performance while managing costs?
We implement a multi-layered cost optimization strategy that avoids relying solely on LLMs by creating custom tools that LLMs learn to call effectively, gravitating toward smaller models rather than larger models. Our approach includes progressive model shrinking where we reduce model sizes as customers data maturity increases.
Cost Management & Performance
How does Interval provide cost-effective solutions?
We enable organizations to "crawl and walk before running," moving from 0% data maturity to effective AI usage in the most cost-efficient way. We prevent expensive large-scale ETL projects that often need to be repeated every few years. Our agent-driven automation reduces manual data management overhead while providing better cost control through rate limiting and usage monitoring.
How does Interval provide cost-effective solutions?
We enable organizations to "crawl and walk before running," moving from 0% data maturity to effective AI usage in the most cost-efficient way. We prevent expensive large-scale ETL projects that often need to be repeated every few years. Our agent-driven automation reduces manual data management overhead while providing better cost control through rate limiting and usage monitoring.
How does Interval provide cost-effective solutions?
We enable organizations to "crawl and walk before running," moving from 0% data maturity to effective AI usage in the most cost-efficient way. We prevent expensive large-scale ETL projects that often need to be repeated every few years. Our agent-driven automation reduces manual data management overhead while providing better cost control through rate limiting and usage monitoring.
How does data monetization offset costs?
The data monetization option can potentially offset platform costs entirely, turning data infrastructure from a cost center into a revenue generator. We maintain a competitive structure compared to industry standard rates, with clients receiving most of the data sale proceeds.
How does data monetization offset costs?
The data monetization option can potentially offset platform costs entirely, turning data infrastructure from a cost center into a revenue generator. We maintain a competitive structure compared to industry standard rates, with clients receiving most of the data sale proceeds.
How does data monetization offset costs?
The data monetization option can potentially offset platform costs entirely, turning data infrastructure from a cost center into a revenue generator. We maintain a competitive structure compared to industry standard rates, with clients receiving most of the data sale proceeds.
Unstructured Data Processing
How does Interval handle unstructured data compared to structured data?
We employ different processing paradigms for structured versus unstructured data, with structured data receiving full normalization and standardization through traditional database paths, while unstructured data is primarily used as additional context to aid in intelligence rather than undergoing complete normalization.
How does Interval handle unstructured data compared to structured data?
We employ different processing paradigms for structured versus unstructured data, with structured data receiving full normalization and standardization through traditional database paths, while unstructured data is primarily used as additional context to aid in intelligence rather than undergoing complete normalization.
How does Interval handle unstructured data compared to structured data?
We employ different processing paradigms for structured versus unstructured data, with structured data receiving full normalization and standardization through traditional database paths, while unstructured data is primarily used as additional context to aid in intelligence rather than undergoing complete normalization.
What types of unstructured data can Interval process?
We have extensive experience processing unstructured data including image data, video data, smart sensor data, documents, audio, camera footage, and various other formats.
What types of unstructured data can Interval process?
We have extensive experience processing unstructured data including image data, video data, smart sensor data, documents, audio, camera footage, and various other formats.
What types of unstructured data can Interval process?
We have extensive experience processing unstructured data including image data, video data, smart sensor data, documents, audio, camera footage, and various other formats.
How does Interval handle media and content data?
This approach converts unstructured content into semantic tags and structured metadata tables. Our metadata normalization process focuses on creating structured representations of unstructured content characteristics rather than transforming the underlying data itself.
How does Interval handle media and content data?
This approach converts unstructured content into semantic tags and structured metadata tables. Our metadata normalization process focuses on creating structured representations of unstructured content characteristics rather than transforming the underlying data itself.
How does Interval handle media and content data?
This approach converts unstructured content into semantic tags and structured metadata tables. Our metadata normalization process focuses on creating structured representations of unstructured content characteristics rather than transforming the underlying data itself.
Knowledge Base Strategy
Does Interval utilize RAG (Retrieval-Augmented Generation) today?
Yes. Our knowledge strategy is built around the three “experts” in our semantic layer:
A layer manages structured knowledge: schemas, entities, relationships, and canonical definitions.
Another layer manages similarity knowledge: embeddings, semantic search, and document chunks for retrieval.
The final layer manages hard facts: policies, constraints, and “must-be-true” rules.
Our RAG pipelines let each contribute their part of the context that’s passed into the LLM so it can answer with both breadth (similarity) and precision (facts and structure).
Does Interval utilize RAG (Retrieval-Augmented Generation) today?
Yes. Our knowledge strategy is built around the three “experts” in our semantic layer:
A layer manages structured knowledge: schemas, entities, relationships, and canonical definitions.
Another layer manages similarity knowledge: embeddings, semantic search, and document chunks for retrieval.
The final layer manages hard facts: policies, constraints, and “must-be-true” rules.
Our RAG pipelines let each contribute their part of the context that’s passed into the LLM so it can answer with both breadth (similarity) and precision (facts and structure).
Does Interval utilize RAG (Retrieval-Augmented Generation) today?
Yes. Our knowledge strategy is built around the three “experts” in our semantic layer:
A layer manages structured knowledge: schemas, entities, relationships, and canonical definitions.
Another layer manages similarity knowledge: embeddings, semantic search, and document chunks for retrieval.
The final layer manages hard facts: policies, constraints, and “must-be-true” rules.
Our RAG pipelines let each contribute their part of the context that’s passed into the LLM so it can answer with both breadth (similarity) and precision (facts and structure).
When would Interval recommend RAG-based knowledge management?
We lean on RAG whenever a customer needs to:
Pull from large, changing corpora (wikis, tickets, PDFs, emails, reports).
Ground answers in a live data model (metrics, dimensions, entity definitions).
Enforce non-negotiable rules (compliance statements, contractual terms, SLAs).
In practice, that means RAG is our default choice for enterprise question-answering, analytics explanation, and decision support, while fine-tuning approaches are reserved as needed for more specific tasks.
When would Interval recommend RAG-based knowledge management?
We lean on RAG whenever a customer needs to:
Pull from large, changing corpora (wikis, tickets, PDFs, emails, reports).
Ground answers in a live data model (metrics, dimensions, entity definitions).
Enforce non-negotiable rules (compliance statements, contractual terms, SLAs).
In practice, that means RAG is our default choice for enterprise question-answering, analytics explanation, and decision support, while fine-tuning approaches are reserved as needed for more specific tasks.
When would Interval recommend RAG-based knowledge management?
We lean on RAG whenever a customer needs to:
Pull from large, changing corpora (wikis, tickets, PDFs, emails, reports).
Ground answers in a live data model (metrics, dimensions, entity definitions).
Enforce non-negotiable rules (compliance statements, contractual terms, SLAs).
In practice, that means RAG is our default choice for enterprise question-answering, analytics explanation, and decision support, while fine-tuning approaches are reserved as needed for more specific tasks.
How does Interval’s RAG approach compare to a traditional knowledge base or FAQ search?
A traditional KB mostly serves a rules engine—you get exact matches on fixed articles. Our RAG approach lets all methods interpolate:
Retrieval to the right entities, metrics, and systems so answers are structurally correct.
Embeddings and similarity search to find the most relevant passages, even when the question doesn’t match exact wording.
Verified snippets, guardrails, and logic so the final answer is both helpful and defensible.
The result is that instead of “here are three articles you might like,” RAG produces a grounded answer with linkage back into the underlying knowledge base.
How does Interval’s RAG approach compare to a traditional knowledge base or FAQ search?
A traditional KB mostly serves a rules engine—you get exact matches on fixed articles. Our RAG approach lets all methods interpolate:
Retrieval to the right entities, metrics, and systems so answers are structurally correct.
Embeddings and similarity search to find the most relevant passages, even when the question doesn’t match exact wording.
Verified snippets, guardrails, and logic so the final answer is both helpful and defensible.
The result is that instead of “here are three articles you might like,” RAG produces a grounded answer with linkage back into the underlying knowledge base.
How does Interval’s RAG approach compare to a traditional knowledge base or FAQ search?
A traditional KB mostly serves a rules engine—you get exact matches on fixed articles. Our RAG approach lets all methods interpolate:
Retrieval to the right entities, metrics, and systems so answers are structurally correct.
Embeddings and similarity search to find the most relevant passages, even when the question doesn’t match exact wording.
Verified snippets, guardrails, and logic so the final answer is both helpful and defensible.
The result is that instead of “here are three articles you might like,” RAG produces a grounded answer with linkage back into the underlying knowledge base.
Blockchain Infrastructure & Data Provenance
How does Interval implement blockchain technology?
We implement a custom enterprise blockchain designed specifically for data operations, serving four primary use cases:
Data Provenance: Comprehensive tracking including data receipt, structure, batches, and content hashes
Authorized Access: On-chain gateway for secure, auditable data access
Marketplace Transactions: Decentralized control of data asset purchases
Audit Trail: Complete usage records across asset lifecycles
How does Interval implement blockchain technology?
We implement a custom enterprise blockchain designed specifically for data operations, serving four primary use cases:
Data Provenance: Comprehensive tracking including data receipt, structure, batches, and content hashes
Authorized Access: On-chain gateway for secure, auditable data access
Marketplace Transactions: Decentralized control of data asset purchases
Audit Trail: Complete usage records across asset lifecycles
How does Interval implement blockchain technology?
We implement a custom enterprise blockchain designed specifically for data operations, serving four primary use cases:
Data Provenance: Comprehensive tracking including data receipt, structure, batches, and content hashes
Authorized Access: On-chain gateway for secure, auditable data access
Marketplace Transactions: Decentralized control of data asset purchases
Audit Trail: Complete usage records across asset lifecycles
What additional capabilities does Interval's blockchain provide?
Our blockchain provides versioning control through immutable asset snapshots that prevent pipeline breaks from data changes, historical snapshots for point-in-time data consistency, and upgrade management with on-chain versioning.
What additional capabilities does Interval's blockchain provide?
Our blockchain provides versioning control through immutable asset snapshots that prevent pipeline breaks from data changes, historical snapshots for point-in-time data consistency, and upgrade management with on-chain versioning.
What additional capabilities does Interval's blockchain provide?
Our blockchain provides versioning control through immutable asset snapshots that prevent pipeline breaks from data changes, historical snapshots for point-in-time data consistency, and upgrade management with on-chain versioning.
How does Interval handle blockchain costs and decentralization?
We operate a custom Cosmos-based, EVM-compatible L1 blockchain with sub-penny transaction costs designed for scalable enterprise throughput, storing only authorization credentials, access rights, and data hashes on-chain rather than actual data. Our decentralization strategy focuses on enterprise needs with a controlled validator network using proof-of-stake consensus, gradually expanding validators while maintaining enterprise privacy requirements.
How does Interval handle blockchain costs and decentralization?
We operate a custom Cosmos-based, EVM-compatible L1 blockchain with sub-penny transaction costs designed for scalable enterprise throughput, storing only authorization credentials, access rights, and data hashes on-chain rather than actual data. Our decentralization strategy focuses on enterprise needs with a controlled validator network using proof-of-stake consensus, gradually expanding validators while maintaining enterprise privacy requirements.
How does Interval handle blockchain costs and decentralization?
We operate a custom Cosmos-based, EVM-compatible L1 blockchain with sub-penny transaction costs designed for scalable enterprise throughput, storing only authorization credentials, access rights, and data hashes on-chain rather than actual data. Our decentralization strategy focuses on enterprise needs with a controlled validator network using proof-of-stake consensus, gradually expanding validators while maintaining enterprise privacy requirements.
Privacy & Data Monetization
How does Interval maintain privacy when monetizing data?
We implement privacy-preserving monetization through advanced anonymization techniques that particularly benefit organizations with large-scale data collection, enabling valuable inference without identifiable information by leveraging longitudinal data depth combined with recency to create market-valuable behavioral patterns focused on usage, consumption, and activity trends.
How does Interval maintain privacy when monetizing data?
We implement privacy-preserving monetization through advanced anonymization techniques that particularly benefit organizations with large-scale data collection, enabling valuable inference without identifiable information by leveraging longitudinal data depth combined with recency to create market-valuable behavioral patterns focused on usage, consumption, and activity trends.
How does Interval maintain privacy when monetizing data?
We implement privacy-preserving monetization through advanced anonymization techniques that particularly benefit organizations with large-scale data collection, enabling valuable inference without identifiable information by leveraging longitudinal data depth combined with recency to create market-valuable behavioral patterns focused on usage, consumption, and activity trends.
What specific privacy protection methods does Interval use?
Our privacy protection methods include:
Comprehensive data anonymization removing all identifiable information before external sharing
Statistical noise and fuzzing that preserves trends while protecting individual privacy
Derivative data creation where purchasers receive statistically equivalent data rather than original datasets
What specific privacy protection methods does Interval use?
Our privacy protection methods include:
Comprehensive data anonymization removing all identifiable information before external sharing
Statistical noise and fuzzing that preserves trends while protecting individual privacy
Derivative data creation where purchasers receive statistically equivalent data rather than original datasets
What specific privacy protection methods does Interval use?
Our privacy protection methods include:
Comprehensive data anonymization removing all identifiable information before external sharing
Statistical noise and fuzzing that preserves trends while protecting individual privacy
Derivative data creation where purchasers receive statistically equivalent data rather than original datasets
How does Interval preserve data value while maintaining privacy?
The value preservation approach maintains inference validity for purchaser insights while keeping overall patterns intact for targeting and optimization, with zero primary data exposure as clients retain all original data while only derivatives are sold. Our on-chain licensing standard ensures data provided complies to privacy standards set.
How does Interval preserve data value while maintaining privacy?
The value preservation approach maintains inference validity for purchaser insights while keeping overall patterns intact for targeting and optimization, with zero primary data exposure as clients retain all original data while only derivatives are sold. Our on-chain licensing standard ensures data provided complies to privacy standards set.
How does Interval preserve data value while maintaining privacy?
The value preservation approach maintains inference validity for purchaser insights while keeping overall patterns intact for targeting and optimization, with zero primary data exposure as clients retain all original data while only derivatives are sold. Our on-chain licensing standard ensures data provided complies to privacy standards set.
Business Model & Pricing
What is Interval's pricing model and revenue structure?
We operate on an implementation and subscription-based model. Our core offering includes subscription-based storage and private AI services, plus optional revenue sharing on data sales.
What is Interval's pricing model and revenue structure?
We operate on an implementation and subscription-based model. Our core offering includes subscription-based storage and private AI services, plus optional revenue sharing on data sales.
What is Interval's pricing model and revenue structure?
We operate on an implementation and subscription-based model. Our core offering includes subscription-based storage and private AI services, plus optional revenue sharing on data sales.
How does Interval's pricing compare to traditional data brokers?
Traditional data brokers charge higher rates because they provide only sales services without storage solutions, agent frameworks, or ingestion tools, often conducting one-time upfront purchases without ongoing relationships. Our advantage stems from operating a dual revenue stream model where we generate income from services as well as data monetization commissions.
How does Interval's pricing compare to traditional data brokers?
Traditional data brokers charge higher rates because they provide only sales services without storage solutions, agent frameworks, or ingestion tools, often conducting one-time upfront purchases without ongoing relationships. Our advantage stems from operating a dual revenue stream model where we generate income from services as well as data monetization commissions.
How does Interval's pricing compare to traditional data brokers?
Traditional data brokers charge higher rates because they provide only sales services without storage solutions, agent frameworks, or ingestion tools, often conducting one-time upfront purchases without ongoing relationships. Our advantage stems from operating a dual revenue stream model where we generate income from services as well as data monetization commissions.
What guarantees does Interval provide regarding data monetization?
We provide explicit guarantees including no obligation for data monetization (entirely optional), granular control to specify exactly which data assets are available for sale, and primary data security where original data remains in private data lakes under client control.
What guarantees does Interval provide regarding data monetization?
We provide explicit guarantees including no obligation for data monetization (entirely optional), granular control to specify exactly which data assets are available for sale, and primary data security where original data remains in private data lakes under client control.
What guarantees does Interval provide regarding data monetization?
We provide explicit guarantees including no obligation for data monetization (entirely optional), granular control to specify exactly which data assets are available for sale, and primary data security where original data remains in private data lakes under client control.
Enterprise Integration & Deployment
What deployment options does Interval offer?
We offer flexible deployment options including local cloud server deployment or on-premises installations to meet data residency requirements. We can deploy as a sidecar solution alongside existing infrastructure or as a comprehensive replacement.
What deployment options does Interval offer?
We offer flexible deployment options including local cloud server deployment or on-premises installations to meet data residency requirements. We can deploy as a sidecar solution alongside existing infrastructure or as a comprehensive replacement.
What deployment options does Interval offer?
We offer flexible deployment options including local cloud server deployment or on-premises installations to meet data residency requirements. We can deploy as a sidecar solution alongside existing infrastructure or as a comprehensive replacement.
How does Interval support organizational structures?
The platform supports both regional distribution and business line organizational structures, with granular access controls by individual, department, or system. This enables organizations to maintain appropriate security and access controls for different organizational levels while providing unified data insights.
How does Interval support organizational structures?
The platform supports both regional distribution and business line organizational structures, with granular access controls by individual, department, or system. This enables organizations to maintain appropriate security and access controls for different organizational levels while providing unified data insights.
How does Interval support organizational structures?
The platform supports both regional distribution and business line organizational structures, with granular access controls by individual, department, or system. This enables organizations to maintain appropriate security and access controls for different organizational levels while providing unified data insights.
What is Interval's typical implementation process?
Our implementation follows a structured engagement model:
Phase 1: Assess – Define initial data products, data sources, and requirements.
Phase 2: Design – Schema, confirm sources, access, and ingestion approach.
Phase 3: Build – Implement on ~1 week of data and other enterprise documents.
Phase 4: Tuning & Rollout – Full data processing, optimization, and optional external monetization.
What is Interval's typical implementation process?
Our implementation follows a structured engagement model:
Phase 1: Assess – Define initial data products, data sources, and requirements.
Phase 2: Design – Schema, confirm sources, access, and ingestion approach.
Phase 3: Build – Implement on ~1 week of data and other enterprise documents.
Phase 4: Tuning & Rollout – Full data processing, optimization, and optional external monetization.
What is Interval's typical implementation process?
Our implementation follows a structured engagement model:
Phase 1: Assess – Define initial data products, data sources, and requirements.
Phase 2: Design – Schema, confirm sources, access, and ingestion approach.
Phase 3: Build – Implement on ~1 week of data and other enterprise documents.
Phase 4: Tuning & Rollout – Full data processing, optimization, and optional external monetization.
Data Governance & Audit Capabilities
How does Interval handle data governance and audit requirements?
We implement comprehensive data governance through our blockchain-based audit system that creates fully immutable, permanent logs of all data interactions. Our provenance solution tracks who accessed data, who changed it, which agents accessed it, which LLMs were used, and provides heat maps of useful data across organizations.
How does Interval handle data governance and audit requirements?
We implement comprehensive data governance through our blockchain-based audit system that creates fully immutable, permanent logs of all data interactions. Our provenance solution tracks who accessed data, who changed it, which agents accessed it, which LLMs were used, and provides heat maps of useful data across organizations.
How does Interval handle data governance and audit requirements?
We implement comprehensive data governance through our blockchain-based audit system that creates fully immutable, permanent logs of all data interactions. Our provenance solution tracks who accessed data, who changed it, which agents accessed it, which LLMs were used, and provides heat maps of useful data across organizations.
What audit and compliance capabilities does Interval provide?
We maintain detailed lineage tracking showing data origins, transformations, and usage patterns. The system integrates with existing governance tools while providing enhanced capabilities. Our audit logs enable organizations to understand AI usage patterns, identify most commonly used AI providers, track data query frequencies, and monitor organizational behavior from a data perspective.
What audit and compliance capabilities does Interval provide?
We maintain detailed lineage tracking showing data origins, transformations, and usage patterns. The system integrates with existing governance tools while providing enhanced capabilities. Our audit logs enable organizations to understand AI usage patterns, identify most commonly used AI providers, track data query frequencies, and monitor organizational behavior from a data perspective.
What audit and compliance capabilities does Interval provide?
We maintain detailed lineage tracking showing data origins, transformations, and usage patterns. The system integrates with existing governance tools while providing enhanced capabilities. Our audit logs enable organizations to understand AI usage patterns, identify most commonly used AI providers, track data query frequencies, and monitor organizational behavior from a data perspective.
How does Interval implement access controls?
Rate limiting and access controls prevent unauthorized usage, such as preventing junior associates from running up excessive AI service bills. We capture all activity data between vendors and customer teams to create a comprehensive storage environment for you to understand how data is used while maintaining it in a normalized state, plus granular access control for role-based data access by individual, business line, or system.
How does Interval implement access controls?
Rate limiting and access controls prevent unauthorized usage, such as preventing junior associates from running up excessive AI service bills. We capture all activity data between vendors and customer teams to create a comprehensive storage environment for you to understand how data is used while maintaining it in a normalized state, plus granular access control for role-based data access by individual, business line, or system.
How does Interval implement access controls?
Rate limiting and access controls prevent unauthorized usage, such as preventing junior associates from running up excessive AI service bills. We capture all activity data between vendors and customer teams to create a comprehensive storage environment for you to understand how data is used while maintaining it in a normalized state, plus granular access control for role-based data access by individual, business line, or system.