AI and Data Solutions for Business Growth

What is Datavise?

Datavise is a consulting and development company that provides enterprises with customized AI, data analysis, and cloud services. Its offerings cover everything from strategy and prototyping to integration, deployment, and ongoing optimization; it is not a single, ready-to-use AI tool.

The company’s key capabilities include generative AI, RAG-based knowledge retrieval, AI and machine learning consulting, business intelligence, data architecture, visualization, cloud platforms, and dedicated technical teams. Quotations for projects are prepared based on an assessment of the specific requirements.

A one-sentence summary

Datavise helps companies combine internal data, generative AI, search systems, and cloud infrastructure to create customized business applications that can be put into use.

Service matrix

Generative AI development

Datavise offers generative AI services that range from consulting to development and integration, enabling the creation of industry-specific applications using text, image, and voice models. These solutions are tailored to take into account a company’s data, existing software, and specific business objectives.

• Analyzing the business opportunities and risks associated with generative AI
• Select large models and multimodal models suitable for the task.
• Develop intelligent agents for customer service, sales, and internal staff.
• Generate reports, marketing, and personalized content
• Connect enterprise interfaces, databases, and workflows
• Establish rules for prompts, evaluation, and content security.
• Ongoing monitoring, updating, and optimization after going live

Generative AI agents

Agents can provide round-the-clock Q&A services to customers or employees, and they can access business systems once authorized. In a production environment, it is necessary to design free-form conversations separately from structured processes.

• Answer business questions based on corporate data
• Collect customer requirements and filter out potential leads.
• Call the business interface to query the status or create a task.
• Interact with users via text or voice.
• Forward complex, high-risk requests to human agents.
• Save the necessary context and generate a handover summary.
• Use the test set to verify facts, behaviors, and tool actions.

RAG as a Service

Datavise’s RAG service combines large language models with real-time data retrieval, using corporate data to provide context for responses. The service includes services for strategy development, custom development, integration, testing, and ongoing support.

• Access to files, databases, and sources of business knowledge
• Clean up, split, and enrich document metadata
• Mixed search combining keywords and vectors
• Search for relevant segments based on the user’s query.
• Have the model generate answers based on the retrieved content.
• Restrict sensitive data through permission filtering.
• Monitor retrieval quality and answer relevance

Hybrid retrieval and semantic search

The official website explains in detail the hybrid retrieval method that combines keyword search with vector search; this approach enables matching of exact terms as well as identification of content with similar meanings, making it suitable for regulatory, technical, and industry documents.

• Maintain an exact match for numbers, product names, and regulatory provisions.
• Understanding natural language expressions through vector search
• Filter by metadata using region, date, and permissions.
• Create a hierarchical segmentation structure for long documents.
• Increase the priority of the most relevant segments through rearrangement.
• Refuse to answer questions without basis or ask for clarification.

AI and machine learning consulting

The consulting services cover readiness assessment, strategic planning, model development, intelligent automation, and model operation. Companies can start by addressing a high-value issue, and then decide whether to expand to a data platform or to establish a permanent team.

• Assess the readiness of data, talent, systems, and governance.
• Develop an AI roadmap that is aligned with business objectives
• Design and validate the model prototype
• Developing prediction, classification, and natural language models
• Integrate the model into existing software processes
• Establish mechanisms for deployment, monitoring, and continuous optimization.
• Design responsible, transparent, and auditable AI governance.

Intelligent automation

Datavise utilizes machine learning, robotic process automation, and document processing to reduce repetitive tasks. Whether automation is worth implementing should be determined based on the error rate, the time required for manual handling, and the business outcomes.

• Processes with clear identification rules and a high degree of repetition
• Extract data from invoices, contracts, and forms.
• Assign priorities based on the prediction results
• Automatically generate or verify business reports
• Transferring structured data between systems
• Manual review is arranged for results with low confidence levels.
• Record each automatic decision and exception handling.

MLOps and model management

AI and machine learning consulting includes model deployment, monitoring, maintenance, and continuous improvement. Teams should place training data, code, models, configurations, and metrics under version control.

• Establish reproducible data and training pipelines
• Record the model version and evaluation results
• Monitoring latency, cost, drift, and errors
• Set up the model update and rollback processes
• Protecting keys, data, and inference interfaces
• Retain audit evidence for production forecasts

Data and Business Intelligence Consulting

Data and BI services help businesses create consistent metrics, analysis models, and decision-making dashboards from dispersed data. The value lies not only in the charts, but also in the establishment of a closed loop between data standards, quality, and business actions.

• Inventorying business systems and data sources
• Standardize the metrics for customers, products, and finance.
• Build a data warehouse or lakehouse model
• Design management and operation dashboards
• Identify costs, risks, and growth opportunities
• Establish automatic refresh and quality alerting.
• Control data access rights based on job roles

Data visualization and reporting

Datavise uses tools such as Power BI and Tableau to convert complex data into interactive dashboards and reports. It is necessary to first define the decision-making questions before selecting the appropriate charts and setting the refresh frequency.

• Designing key performance indicators and dimensions
• Create dashboards for sales, operations, risk, and finance.
• Provides functions for drilling down, filtering, and anomaly alerts.
• Connect the cloud warehouse to business applications
• Reduce manual table consolidation and version conflicts
• Explain the origin of indicators through a data dictionary.

Data management and architecture

Data architecture services focus on creating a secure, scalable, and well-governed data environment, providing a reliable foundation for analysis and AI applications. Without a stable data pipeline, it is generally difficult to maintain large models over the long term.

• Design data warehouse, data lake, or lakehouse architectures
• Establish collection, transformation, and synchronization pipelines
• Establish rules for master data and metadata management.
• Monitor integrity, accuracy, and timeliness.
• Implement hierarchical, permission-based, and data lifecycle management.
• Prepare traceable datasets for AI training and retrieval.

Cloud services

Datavise offers support for cloud migration, management, optimization, and AI deployment, and it is available on AWS, Google Cloud, and Microsoft Azure. The choice of platform should be based on existing technologies, location, compliance requirements, and cost factors.

• Assess the existing systems and the scope of cloud migration
• Design computing, storage, networking, and identity architectures
• Deploying data, analytics, and AI workloads
• Establish backup, monitoring, and disaster recovery.
• Optimize resource usage and cloud costs
• Implement encryption, access controls, and security audits.
• Expand capacity in line with business growth

Dedicated AI and machine learning team

Companies can hire dedicated teams comprising AI strategists, data scientists, machine learning engineers, and software engineers. This approach is suitable for ongoing product development, but customers still need to retain a product owner as well as the ability to make business decisions.

• Fill the shortage of AI and data talent within the organization
• Rapid prototyping and production development
• Collaborate with existing product, data, and security teams
• Ongoing maintenance of models, retrieval, and data pipelines
• Gradually expand the team size in accordance with the roadmap.
• Clarify the ownership of code, data, and intellectual property.

Main technical ecosystem

Applicable industries

• Patient support and research retrieval in medical and biotech fields
• Risk management, fraud detection, and compliance analysis in finance and banking
• Recommendations, search, and inventory optimization in retail and e-commerce
• Predictive maintenance and project analysis in manufacturing and engineering
• Insights into the assets and operations of real estate and property assets
• Automation of document retrieval and review for legal and compliance purposes
• Reports from professional service companies and knowledge assistants

Examples of compliance document retrieval

The public cases presented by Datavise demonstrate the creation of RAG document retrieval systems for compliance management platforms. This approach improves the retrieval of large volumes of regulatory information through hierarchical segmentation, metadata enhancement, regulatory ontologies, and vector search.

• Migrate regulatory documents to a vector retrieval system
• Rewrite the indexing process and implement hierarchical segmentation.
• Add domain metadata to the document.
• Designing ontologies and semantic relationships in the field of regulatory oversight
• Combining the full text with vector retrieval
• Adjust the prompts to fit the search results
• Continuously update regulatory information and evaluation samples

Examples of AI for dental speech

Another official example is an intelligent voice reception system available 24/7 for dental clinics. This system integrates generative AI, speech-to-text and text-to-speech technologies, as well as phone call management and medical CRM functions, and is managed by the clinics through front-end and back-end applications.

• Answer patients' calls at night and on weekends
• Understand and answer common clinic questions
• Two-way communication is carried out through a real-time voice interface.
• Connect to the medical CRM to save interaction details
• Use Django for the backend and React for the frontend.
• Assessing the performance of large models using custom benchmark tests
• Scalable across multiple clinics and varying call volumes

Which companies are suitable?

• Companies that need customized AI solutions rather than generic templates
• Teams that possess internal data but lack the capability to put it into practice in engineering terms
• Organizations that need to connect AI to existing systems and cloud platforms
• Customers who wish to develop tools for retrieving industry knowledge and ensuring compliance
• Companies that require long-term access to dedicated data or a machine learning team
• Companies willing to proceed in phases, from consultation and pilot projects to actual production

Datavise prices

The Datavise website does not disclose hourly rates, fixed packages, or minimum prices for services; instead, it offers a free initial consultation to understand the client’s needs before providing a customized quote. The tools listed there are indicated as services for which companies can request quotes, rather than being free SaaS solutions.

Project budget list

• Costs for requirement identification, data auditing, and solution design
• Costs associated with data cleaning, labeling, migration, and access control
• Costs for models, vector libraries, cloud resources, and third-party interfaces
• Costs related to front-end and back-end development, business integration, and user experience design
• Investments in security, compliance, testing, and auditing
• Costs for monitoring, support, and ongoing optimization after going live
• Scope of knowledge transfer, document, code, and data export
• Change requests, out-of-scope items, and termination clauses

Quick Start Tutorial

1. Choose a specific issue that affects revenue, costs, or risks.
2. Organize the current processes, data, users, and key performance indicators.
3. Conduct requirement and readiness consultations with Datavise.
4. Break the project into discovery, prototype, pilot, and production phases.
5. The contract should specify the deliverables, acceptance criteria, and intellectual property rights.
6. Validate the technical solution using representative data.
7. Launch on a small scale while retaining the option for manual rollback.
8. Decide whether to expand based on the actual business results.

RAG Project Tutorial

1. Identify the target users, types of problems, and reliable sources of knowledge.
2. Remove expired, duplicate, and conflicting data.
3. Design segmentation, metadata, permissions, and update mechanisms.
4. Establish a retrieval baseline that combines keywords with vectors.
5. Prepare real questions, standard answers, and supporting evidence.
6. Evaluate the retrieval hits and the quality of the final responses separately.
7. Add rules for refusal, clarification, and manual escalation.
8. After going live, continue to collect failure issues and conduct regression testing.

Tutorials on Data and BI Projects

1. List the management and operational decisions that need to be supported.
2. Standardize the definitions, responsible parties, and calculation logic for key metrics.
3. Audit data sources, refresh frequency, and quality issues.
4. Design the target data model and permission structure.
5. Deliver a small number of high-value dashboards first.
6. Have business users verify the definitions and usage processes.
7. Establish quality monitoring and anomaly alerts.
8. Iterate continuously based on adoption rates and business actions.

Tutorial on Deploying AI Agents

1. Define the scope of what the agent is allowed to answer and perform.
2. Prepare the necessary knowledge, tool interfaces, and authentication methods.
3. Design free-response, fixed-process, and manual intervention boundaries.
4. Add confirmation and auditing for high-risk write operations.
5. Create sets of facts, behaviors, tools, and security tests.
6. The test environment covers timeouts and dependency failures.
7. Publish at low volume and monitor results and costs.
8. Convert production failures into new regression test cases.

Security, Privacy, and Compliance

The Datavise website emphasizes data privacy, security, ethical AI, as well as industry regulations such as GDPR and HIPAA. The specific certifications, scope of responsibilities, and methods of data processing shall be determined in accordance with the project contract and relevant security documents.

• Only collect the data required to complete the project.
• Isolate access permissions by role and project environment
• Encrypt static and transmitted data.
• Record the data source, model version, and automatic decision-making.
• Confirm cloud regions, subcontractors, and cross-border processing
• Arrange expert and manual review for regulated scenarios
• Establish processes for retention, deletion, export, and incident response.
• Regularly verify the fairness, accuracy, and security boundaries of the model.

Effect evaluation

Product advantages

• End-to-end capabilities covering AI, data, BI, and cloud.
• It can be customized to suit specific industry processes.
• The RAG service takes into account both hybrid search and metadata.
• Supports the entire lifecycle, from strategic consulting to production and operation.
• A dedicated AI and machine learning team can be provided.
• Public case studies demonstrate the details of compliant search and voice Agent implementation.
• Compatible with major cloud services, data platforms, and visualization tools
• Emphasize data security, ethics, and ongoing support.

Usage restrictions and precautions

• The official website does not specify fixed prices; the budget needs to be determined after an assessment of the requirements.
• The timeline for custom projects is usually several weeks to several months.
• The quality of delivery depends heavily on customer data and business involvement.
• Multi-system integration may increase time, cost, and security risks.
• The results shown on the official website cannot be considered a guarantee for all projects.
• Regulated industries still require clients to have their own legal and compliance teams for verification.
• The dedicated team model requires a clear product owner and governance structure.
• Third-party models, cloud services, and interfaces incur ongoing costs.
• The core services are not open-source products; withdrawal and migration require provisions in the contract.

GitHub and open source

The Datavise official website does not provide a link to an official GitHub organization that can be used for verification. There are several accounts with similar names on public platforms, but due to the lack of an official website, company information, or code documentation for cross-verification, these accounts cannot be considered official.

Datavise offers business consulting and custom development services; it does not make the source code of its core platform available, nor does it provide a unified open-source license. The issues related to whether the project code will be delivered, who will be responsible for its maintenance, and whether it can be modified further should all be specified in the contract.

Basic information

Recommendation score

4.5 / 5. Datavise is suitable for teams that need to turn corporate data, RAG, generative AI, and cloud systems into customized applications; it offers a comprehensive range of services with clear technical approaches demonstrated in various cases. However, its pricing is not transparent, and the success of a project depends on the quality of the data, the scope of the contract, and ongoing collaboration between the parties involved.

Frequently Asked Questions

What does Datavise do mainly?

It offers enterprises generative AI, RAG, machine learning, data analysis, cloud platforms, and dedicated team services.

Is Datavise a SaaS tool?

It is not a single, standardized SaaS solution; instead, it offers consulting, customized development, integration, and support based on the needs of each enterprise.

Does Datavise offer RAG?

Services included are mixed search, vector retrieval, knowledge architecture, custom development, and continuous optimization.

How much is Datavise?

The official website does not specify fixed package prices or minimum amounts for services; a customized quote is provided after consulting about the specific requirements.

Does it support voice AI?

Yes, the official examples demonstrate voice reception agents that integrate speech recognition, speech synthesis, call flows, and CRM.

Which cloud platforms does it support?

The official website showcases major cloud platforms such as AWS, Google Cloud, and Microsoft Azure.

Is Datavise suitable for small and medium-sized enterprises?

It is suitable for small and medium-sized enterprises that have clear business challenges and project budgets; however, complex projects still require internal personnel and data resources.

Does Datavise offer long-term support?

The official website indicates that monitoring, updates, and optimization are available after deployment, and a dedicated team approach can also be adopted.

Is Datavise open source?

The core service is not an open-source product, and the official website does not provide a link to any verifiable official GitHub repository.