Integrated platform to jump-start Enterprise AI journey with ML models and fine-tuned LLMs

With the 0to60.ai platform, enterprises can accelerate AI journeys by training, validating, tuning and deploying generative AI, foundation models and machine learning capabilities with ease and build applications in a fraction of the time with a fraction of the data.

The point and click, no code AI platform brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful development and deployment environment spanning the AI lifecycle.

Features

Privacy Handling-Data De-Identification

Use TensorFlow-trained AI models auto-detect PII and PHI information to anonymize sensitive information in database tables, XML, JSON, CSV and more.

Offers protection strategies from encryption to redaction based on use-case.

Synthetic Data Generation

Generate production-like data preserving the semantics and inter-columnar relationships

Use combination of data generation techniques such as Generative Adversarial Networks and Transformers

Define hierarchical rule sets via the point and click interface to apply deterministic logic to extend the datasets.

Model Training

Automate selecting and optimizing machine learning models

Simplify model development, making it accessible to non-experts

Identify optimal algorithms and parameters for the dataset

Manage resources during model training and evaluation

Facilitate faster and more efficient deployment of ML models.

Knowledge Base Access

Utilize RAG and fine-tuned LLMs for fast knowledge base access

Easy setup and scaling of RAG applications

Select from open-source LLMs such as Llama2 or Phi-2

Customize data processing and retrieval strategies

Benefits

NO VENDOR LOCKIN

Avoid vendor lock-ins, or stitching together fragmented stove-piped solutions.

Our end-to-end open source MLOps platform built by developers for developers. Turn your use-cases and experiments, into MLOps with using the point and click interface.

The modular architecture supports on-premise, cloud, and hybrid environments for maximum flexibility and frictionless integrations. Deploy pipelines, schedule, orchestrate, and monitor their execution through the UI, API or the chat interface.

On-Prem or Cloud

The 0to60.ai platform offers versatile deployment options to suit various organizational needs. The Managed SaaS option, provides a cloud-based solution with minimal setup required, ideal for businesses seeking a turnkey AI solution. The On-prem deployment is available for organizations that prefer to host the platform on their own infrastructure, ensuring maximum control over data and system integration. Lastly, deployment in a customer's own Virtual Private Cloud (VPC) environment is an option, offering a balance of control and customization within a secure, isolated cloud space. Each of these deployment methods is designed to cater to different levels of operational complexity and security preferences.

No Silos

The 0to60.ai platform fosters a shift in business strategy towards a holistic, platform-based approach to generative AI, steering away from isolated, use-case specific methods. This platform remains resilient amidst the rapidly evolving landscape of AI frameworks, products, vendors, and industry regulations. Crucially, it adapts to changing business needs in the adoption of AI. It promotes involvement from a wide range of enterprise stakeholders, including employees, contractors, AI vendors, and members of the open-source community. While certain components of the platform may have specific requirements and limitations, overall, the 0to60.ai platform liberates the organization's journey towards embracing an AI-first approach.

No Complexity

The 0to60.ai platform opens up accessibility to cutting-edge machine learning (ML) techniques and Large Language Model (LLM) fine-tuning through its no-code-based configuration. This user-friendly approach democratizes AI, allowing individuals without technical expertise to leverage advanced ML tools. By simplifying the process of configuring and deploying AI models, the platform empowers a broader range of users, from business analysts to decision-makers, to harness the power of AI. This not only accelerates innovation but also broadens the scope of who can contribute to and benefit from the latest developments in AI technology.

Reduce LLM Hallucination

The 0to60.ai platform employs the Retrieval-Augmented Generation (RAG) technique as an effective measure to reduce hallucinations in Large Language Models (LLMs). RAG enhances the accuracy and reliability of LLM outputs by cross-referencing generated content with a vast database of factual information. This approach mitigates the issue of LLMs producing plausible but incorrect or nonsensical information, commonly known as 'hallucinations'. By integrating RAG, the platform ensures that the LLM's responses are not only contextually relevant but also grounded in verified data, significantly improving the quality and trustworthiness of the output. This makes the platform particularly valuable for applications requiring high standards of factual accuracy.

Privacy & Compliance

The 0to60.ai platform places a strong emphasis on privacy and compliance, particularly when handling sensitive enterprise data. It incorporates advanced privacy-preserving techniques, such as data anonymization and encryption, to protect personal and confidential information. These measures ensure that while the platform leverages enterprise data for machine learning and AI applications, it remains fully compliant with data protection regulations like GDPR and HIPAA. This commitment to privacy safeguards not only secures sensitive data but also builds trust with users, making the platform a reliable choice for enterprises dealing with critical and private information. By prioritizing these aspects, 0to60.ai aligns with the highest standards of data ethics and legal compliance.

Solution Store

Financial Services

Bias Management

Address inherent bias in the data to ensure fair and accurate ML outcomes. Involves critically assessing and addressing data sources to prevent the perpetuation of societal biases, such as those based on race, gender, or socio-economic status. Diversifies data and incorporates a wide range of perspectives.

Financial Services

Anomaly Management

Use GAN based techniques to introduce synthetic anomalies into data, to benchmark machine learning models designed to detect real-world irregularities. By creating realistic, varied anomalies, it ensures models are robustly trained and ready to identify and respond to actual anomalies effectively.

Healthcare

Medical Claims Synthesis

Synthesize medical claims data by modeling demographic intricacies, coding standardizations, and differentials across demographic cohorts. Also use population growth models to forecast potential claim types driven by racial variations.

Healthcare

SDoH Extraction

Social determinants of health (SDoH) have an important impact on patient outcomes but are incompletely collected from the electronic health records (EHR). This solution extracts the SDoH parameters from clinical texts of these scarcely documented, yet extremely valuable, clinical data. These enhances real-world evidence on SDoH and aids in identifying patients needing social support.

Modelstore

Organize and distribute benchmarked and purpose built enterprise models with complete tracking and provenance.

e5-mistral-7b-instruct

This model is initialized from Mistral-7B-v0.1 and fine-tuned on a mixture of multilingual datasets with multilingual capability.

husky-kbase-retriever-chat

This model is initialized from Mistral-7B-v0.1 and fine-tuned on API sets to enable capability to chat-with knowledge bases.

bhairava-API-retriever-chat

This model is initialized from Mistral-7B-v0.1 and fine-tuned on API sets to enable chat-with-API capability.

rake-configurator classifier

This model classifies IoT datastreams received from the wagons and assembles the rakes.

Automate Your AI Workflows

Prepare Data

Import data from databases, XML, JSON, CSV, Salesforce, databricks, snowflake...

Auto - analyze semantics, inter-columnar relationships Cleanse & impute

Auto-detect sensitive info Personal info - substitute / encrypt / codify / redact

Generate synthetic data using GANs, transformers, rules sets

MLOps

Utilize advanced algorithms for efficient and automated training of ML models.

Continuously monitor model performance to ensure accuracy and reliability.

Regularly update and maintain models to adapt to new data and trends.

Assess and optimize to improve model efficiency and reduce errors.

Ensure models are adaptable to feedback and evolving requirements for sustained relevance.

LLMOps

Choose LLMs: Select a suitable Large Language Model (LLM) like Llama2, Phi-2, etc., based on specific requirements and capabilities.

Finetuning with Proprietary Content: Adapt the chosen LLM by finetuning it on proprietary content and API documentation to enable knowlege base and API discovery.

Leveraging Advanced Features: Utilize advanced features like Retrieval-Augmented Generation (RAG), vector stores, and graph networks to enrich the model's responses and improve its contextual understanding.

Deploy

Deploy models into enterprise model libraries, solution stores
Use synthetic data for test data, ML, forecasting...

Deploy purpose tuned LLMs as chatbots
Deploy chatbots via UI embedding
Chat with data, docs and API in plain English

Deploy across hyper-scalers and private corporate environments

Hosting

We offer multiple deployment options. Choose the strategy that best supports your infrastructure, data residency and compliance needs.

Managed 0to60.ai SaaS

Dedicated single tenant server-cloud deployment. All data & compute stays behind the client’s firewall / VPC.

On-prem

Run 0to60.ai on your own on-prem servers and personal devices. Self-managed with full remote support.

Virtual Private Cloud

VPC deployment across all hyper-scalers. All data stays behind client’s firewall. Self-managed with full remote support.

Managed VPC

Fully managed by 0to60.ai on VPC sub-account setup exclusively for the client.

Client Journeys

Sasol uses rake auto-identification from loT data

Sasol uses rake auto-identification from IoT data

Identify and reduce biases to ensure fair and accurate outcomes in data. Involves using diverse data sources and algorithms that correct biases.

Health data provider forecasts claims data

Forecast health insurance claims data by analyzing the evolving demographic composition of counties, enabling proactive adjustments to healthcare resources and policies in response to population shifts.

Partnerships and Integrations

0to60.ai integrates out of the box with all your favorite platforms, frameworks and tools.

Come Partner With Us

Join our Channel, VAR, or Technology Partner Programs to work with the leading no code Synthetic Data, AI/ML and LLMOps platform.

JOIN NOW

Come Join Us

We seek passionate individuals who delight in crafting innovative solutions for challenging problems, driven by their expertise in artificial intelligence and deep learning.

EXPLORE

Ready to test drive?

Clients have access to selected open source models from Hugging Face, as well as other third-party models including Llama-2-chat and Phi-2 LLM for Gen AI applications, and a family of purpose-trained models.

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Accelerate Enterprise AI Adoption

Point and Click No Code Platform

Leverage AI to integrate your platforms faster, discover, transform and synthesize data, orchestrate your processes and Machine Learning, all without writing a single line of code

Get Started

PRIVACY

De- identify sensitive data

PII PHI Auto-Detect

Use TensorFlow-trained AI models auto-detect PII and PHI information to anonymize sensitive information in database tables, XML, JSON, CSV and more.

Protect / Synthesize

Offers protection strategies from encryption to redaction based on use-case.

DATA SYNTHESIS

Synthetic data generation

Data Accuracy

Generate production-like data preserving the semantics and inter-columnar relationships

Leverage GANs, Transformers

Use combination of data generation techniques such as Generative Adversarial Networks and Transformers

Granular sub-columnar rulesets

Setup granular rules to govern data synthesis.

PRIVACY

Hierarchical rules engine

Hierarchical

Employ hierarchical structure to organize and control the execution of complex rule sets

Deterministic

Inherently deterministic, ensuring consistent and predictable outcomes based on the specified conditions and actions

MLOps

Model training MLOps

Automate selecting and optimizing machine learning models. Simplify model development, making it accessible to non-experts.

Identify optimal algorithms and parameters for the dataset. Manage resources during model training and evaluation. Facilitate faster and more efficient deployment of ML models.

Model Selection

Automate selecting and optimizing machine learning modelsSimplify model development, making it accessible to non-expertsIdentify optimal algorithms and parameters for the dataset

Model Deployment

Manage resources during model training and evaluationFacilitate faster and more efficient deployment of ML models.

LLMs

Access enterprise knowledge bases

Define hierarchical rule sets via the point and click interface to apply deterministic logic to extend the datasets

Fine-tuning

Utilize a combination of Retrieval Augmented Generation and fine-tuning of LLMs for fast knowledge base access

Deterministic

Easy setup and scaling of RAG applicationsSelect from open-source LLMs such as Llama2 or Phi-2Customize data processing and retrieval strategies

Features

Pre Built Solution Sets

Data Wrangling

Preprocessing and cleaning the dataset to ensure it's suitable for model training. This includes handling missing values, outliers, encoding categorical features, splitting data into training and testing sets, and applying scaling or normalization as needed. The objective is to prepare high-quality data that is consistent, relevant, and free from errors for effective machine learning model development.

Tabular Data Synthesis

Tabular data synthesis to create artificial datasets that replicate the statistical characteristics of real-world tabular data. Support applications in various fields, including machine learning, to generate diverse training data and ensure data privacy by preserving sensitive information while sharing datasets.

GANs, Transformers, Rules Engine

Encrypt Targeted Data

Selectively encrypt critical data before sharing with testers or for other secondary purposes. This ensures that even if the data falls into the wrong hands during downstream processes, the confidentiality and integrity of the information remain intact. Helps enterprises mitigate risks and enhance data security posture.

Retrieval Augmented Generation

Redact Critical Data

Redact Personally Identifiable Information (PII) and Protected Health Information (PHI), ensuring that sensitive details are obscured or removed to maintain privacy and comply with regulatory standards. This is crucial for organizations handling confidential data, providing an added layer of security and privacy protection.

Retrieval Augmented Generation

De-identify Data

Transformation of data to make it more difficult to identify specific individuals, typically through techniques like pseudonymization, anonymization, or aggregation. This is essential for complying with privacy regulations such as GDPR, CCPA, HIPAA and facilitating the safe sharing of data for research, analysis, and other purposes.

Large Language Models

Rules Based Generation

Augment structured data by applying predefined hierarchical rule sets. These rules suggest data according to specific criteria, ensuring structured and accurate information augmentation. The hierarchical nature allows for layered complexity and specificity in data handling, enabling precise and context-aware modifications.

Rules Engine

Integrations

0to60.ai connects to your favorite Platform

Amazon S3

Amazon Web Services offering object storage via web service interface.

Apache Hive

A Hadoop-based data warehouse for summarizing, querying, and analyzing data.

Avro

A data serialization system that provides a compact, fast, binary data format.

ORC

A type of columnar storage format that is highly optimized for heavy read workloads.

Apache Cassandra

Distributed NoSQL database for vast data on multiple commodity servers.

HDFS

Hadoop's key storage: scales for reliability with large datasets.

MS Azure Blob Storage

Cloud storage service from Microsoft that is highly scalable and available.

Google Cloud Storage

Unified object storage for devs and enterprises: live app data to cloud archival.

Testimonials

Read real reviews from our satisfied customers

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Malcolm Reynolds

Co-founder

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Sophia Salazar

App Developer

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Lisa Nelson

App Developer

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Otis Jordan

Operations Manager

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Kenneth Thompson

Operations Manager

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Julia Payne

Co-founder

ZeroToSixty, Inc.
2261 Market Street, STE 5959
San Francisco, CA 94114
United States

+1 (415) 818-0260

go@0to60.ai

Accelerating Enterprise AI

Accelerating Enterprise AI

Integrated platform to jump-start Enterprise AI journey with ML models and fine-tuned LLMs

Features

Privacy Handling-Data De-Identification

Synthetic Data Generation

Model Training

Knowledge Base Access

Benefits

NO VENDOR LOCKIN

On-Prem or Cloud

No Silos

No Complexity

Reduce LLM Hallucination

Privacy & Compliance

Our Expertise

Solution Store

Bias Management

Anomaly Management

Medical Claims Synthesis

SDoH Extraction

Modelstore

e5-mistral-7b-instruct

husky-kbase-retriever-chat

bhairava-API-retriever-chat

rake-configurator classifier

Automate Your AI Workflows

Prepare Data

MLOps

LLMOps

Deploy

Hosting

Managed 0to60.ai SaaS

On-prem

Virtual Private Cloud

Managed VPC

Client Journeys

Sasol uses rake auto-identification from IoT data

Health data provider forecasts claims data

Trusted By

Partnerships and Integrations

Come Partner With Us

Come Join Us

Ready to test drive?

Accelerate Enterprise AI Adoption

Point and Click No Code Platform

Data Wrangling & Synthesis

Train ML Models, Finetune LLMs

Deploy to Private Environments

Data Privacy and Synthesis

Automated MLOps

LLM Finetuning & RAGs

De- identify sensitive data

PII PHI Auto-Detect

Protect / Synthesize

Synthetic data generation

Data Accuracy

Leverage GANs, Transformers

Granular sub-columnar rulesets

Hierarchical rules engine

Hierarchical

Deterministic

Model training MLOps

Model Selection

Model Deployment

Access enterprise knowledge bases

Fine-tuning

Deterministic

Current Challenges

Pre Built Solution Sets

Data Wrangling

Tabular Data Synthesis

Encrypt Targeted Data

Redact Critical Data

De-identify Data

Rules Based Generation

0to60.ai connects to your favorite Platform

Amazon S3

Apache Hive

Avro