Allganize Inc.

What is Allganize?

Allganize is a company that provides enterprises with platforms based on generative AI, knowledge retrieval, and work agents; its core product brand is Alli. This platform offers capabilities such as model selection, RAG, document processing, code-free application development, agent execution, permission management, and various deployment options.

At present, Alli is mainly divided into two product lines: Coworker and Works. Coworker is used for creating AI applications and agents through natural language, while Works offers ready-to-use document-related tasks such as contract review, RFP analysis, knowledge-based Q&A, and report generation.

Core products

Alli Coworker

Alli Coworker allows business professionals to describe their requirements in natural language, after which it generates functional Web applications, AI Agents, information portals, or data integration processes. These applications can be used via a web interface, and they can also be connected to Slack or Microsoft Teams as needed.

• Create AI applications using natural language.
• Create an Agent that can plan and execute tasks.
• Connects ERP, databases, and business APIs.
• Publish the application as an internal web tool.
• Create a unified AI portal for employees.
• Connect Slack and Microsoft Teams.
• Provides version control and rollback for applications.
• Run similar workflows on a local or isolated network.

Alli Works

Alli Works is designed for teams that do not want to build applications from scratch; it allows them to upload documents directly and carry out tasks such as contract summarization, guideline verification, RFP review, and Q&A. It emphasizes starting work with the first document available, without requiring customers to train custom models first.

• Read contracts, policies, reports, and business documents.
• Complete the review of contracts and terms based on the template.
• Compare the RFP requirements with previous solutions.
• Create citable Q&A based on internal knowledge.
• Generate summaries and comparisons for multiple documents.
• It operates based on the model and deployment method chosen by the enterprise.

Enterprise RAG knowledge retrieval

Alli employs retrieval-enhanced generation technology to find relevant information in the documents and data specified by the user, after which the model generates an answer. Users can view the cited materials alongside the answer, which helps to mitigate the issue of relying solely on the knowledge acquired during model training.

Original text area citation

The platform not only provides file links but also marks the areas of reference on the document pages, preserving their original layout, while displaying page numbers and chapter titles. References enhance verifiability, yet it is still necessary to determine whether the model has correctly understood the context, exceptions, and versions.

RAG 2.0

Allganize positions RAG 2.0 as a retrieval solution designed for use with financial documents, documents from the public sector, and complex corporate documents. It handles information such as structure, semantics, titles, and layout, and it enables the return of pages related to a specific query through a Retrieval API.

Document processing capability

• Read corporate files such as PDF, DOCX, XLSX, etc.
• Automatically classify and tag documents.
• Establish relationships between files.
• Process scanned documents using OCR.
• Search shared document libraries and personal document libraries.
• Maintain data integrity in large files.
• Process unstructured and structured business data.

Large files and complex data

The official website states that the platform is capable of handling one million rows in Excel files or 500MB Word files, and it offers features that ensure data integrity. For actual projects, it is still necessary to use real large files in order to test the speeds of uploading, parsing, and searching, as well as to assess memory usage and timeout conditions.

Shared and personal document libraries

Alli Works separates the document libraries that are shared among departments or organizations from those that are exclusive to individual users. The permission settings should apply to files, search indexes, reference results, and Agent tools as well, in order to prevent situations where content can still be accessed through queries even if file downloads are restricted.

In-depth corporate research

Deep Research first plans the questions, then searches through internal data as well as permitted external sources to retrieve, filter, and organize information, ultimately providing analyses along with their sources. It is better suited for market, competitive, customer, and thematic research, rather than simple keyword-based queries.

Contract review

• Extract the contract parties, duration, and key obligations.
• Check for missing or abnormal clauses according to the company guidelines.
• Compare the changes in the terms across different versions.
• Mark the contents that require careful review by the legal department.
• Cite the original page numbers and sections as references.
• Generate summaries, lists of issues, and audit records.

RFP and proposal analysis

Alli can read RFPs and previous proposals, extract the requirements, assess the level of coverage, and assist in creating draft documents with internal references. The bidding team still needs to verify the customer’s requirements, commitments, figures, and compliance statements.

No-code app templates

• Develop proposal content based on the RFP and historical materials.
• Generate an editable PPTX presentation based on the description.
• Screen business documents using OCR and LLM.
• Analyze the model and token usage of the team.
• Convert API specifications into browsable interface tools.
• Generate research briefs with sources for sales accounts.
• Automatically manage visitors through QR codes and notifications.
• Convert training videos into illustrated operation manuals.

Agent workflow

Alli Agent can follow the specified steps, utilize documentation tools or external systems to carry out its tasks, and continue working until a result is obtained. Unlike ordinary chatbots, it is capable of writing data into ERP systems, creating records, or initiating business actions; therefore, it requires more stringent authorization and approval processes.

MCP tool connection

The Agent application allows connection to MCP servers and selection of available tools; authentication can be carried out using mechanisms such as API Keys or OAuth. Administrators should check each server, tool parameter, and writing capability individually, rather than granting full account permissions at once.

Multi-model architecture

Alli, through its multi-model gateway, supports GPT, Claude, Gemini, Llama, as well as other commercial, open-source, or fine-tuned models. Companies can switch between these models without having to rebuild their existing applications and processes, in order to adapt to changes in costs, performance, compliance requirements, or suppliers.

AI application operation and maintenance

Alli offers applications and agents version management, testing environments, approval processes, deployment options, and one-click rollback capabilities similar to those used in software releases. This allows it to keep track of who has modified the prompts, tools, models, or rules, and enables restoration to the previous version in case of errors in production.

• Save the application and Agent versions.
• Verify changes in the staging environment.
• Production release is approved by authorized personnel.
• Roll back with one click in case of problems.
• Control development and usage permissions through roles.
• Log audit logs and model calls.
• Set content and tool invocation safeguards.

AX Report

The AX Report is used to summarize on a monthly basis the locations where AI is being utilized, its adoption levels, and any changes in business operations, thereby assisting management in assessing the value of investments and determining priorities for further development. The number of times AI is used only reflects its level of activity; to assess its true value, it is necessary to take into account factors such as completion time, quality, risks, and business outcomes.

API capabilities

Alli offers REST APIs that enable the execution of LLM applications, initiation of sessions, querying of knowledge bases, as well as uploading or retrieving documents to obtain analysis data. When making requests, it is necessary to include the Alli API Key in the request headers, and access is provided on a basis of isolation between customers and users.

• Run published LLM applications via API.
• Initiate and continue the dialogue process.
• Retrieve relevant pages from the document.
• Manage RAG knowledge and Q&A resources.
• Integrate Alli into web or mobile products.
• Obtain data on usage and dialogue analysis.

System integration

Alli can be connected to SAP, Oracle ERP, internal collaboration tools, databases, Slack, Teams, as well as custom-built systems. It enables simultaneous reading of data and writing back of results; companies should gradually enable various functions using test accounts, with the principle of minimum permissions and approval processes in place.

Deployment method

Which customers are suitable?

• Organizations that require corporate knowledge Q&A and source verification.
• Companies that hope their sales staff can build AI applications without using any coding.
• A team that handles a large number of contracts, RFPs, and internal documents.
• Regulated industries that require deployment on local or isolated networks.
• Companies that need to connect ERP systems, databases, and internal systems.
• Technical teams that wish to be able to switch flexibly between multiple models.
• Platform teams that require unified permissions, auditing, and operations management for AI applications.

Typical use cases

• FAQs on corporate policies and technical manuals.
• Contract summary, comparison, and clause review.
• RFP requirement extraction and proposal content generation.
• In-depth research on markets, competition, and customers.
• Read information from ERP and enter it into the system.
• Internal AI portal for employees and departmental assistants.
• Classification, retrieval, and relationship mapping of large files.
• Reports on AI usage, tokens, and ROI management.

It’s not very suitable for which situations

• Only users who need a free personal chat tool.
• There are no pilot projects involving enterprise data and specific workflows.
• Developers who wish to use the complete platform source code for free commercial purposes.
• Small teams that request the official website to publicly display the fixed total cost of the enterprise.
• Organizations that are unable to arrange for permissions, testing, and manual approval.
• Companies that wish to have the Agent make independent decisions regarding legal or financial matters.
• Projects with extremely poor data quality and a reluctance to first organize the knowledge sources.

Tutorial on creating a knowledge assistant

1. Choose a business problem with clearly defined boundaries.
2. Remove duplicate, expired, and conflicting document versions.
3. Set permissions for shared libraries and personal libraries.
4. Configure the RAG retrieval scope and response rules.
5. Test the completeness of citations and answers using real-world questions.
6. Add cases without answers, conflicts, and overstepping of authority.
7. The data and evaluation sets are continuously updated after publication.

Tutorial for building the Coworker application

1. Define in one sentence the outcome that the application is meant to achieve.
2. It explains the input, output, and data that can be used.
3. Let Coworker generate the initial application and Agent processes.
4. Connect the necessary documents, APIs, or business systems.
5. Check normal and abnormal paths in the testing environment.
6. Set up roles, safeguards, approvals, and auditing.
7. After approval, it is published to Web, Slack, or Teams.
8. Create a new version based on real feedback and retain the rollback option.

Tutorial on Connecting to MCP

1. Verify the source of the MCP server and the list of tools.
2. Determine whether each tool performs a read or write operation.
3. Use an API key or OAuth account with the minimum required permissions.
4. Enable only the necessary tools in the Agent application.
5. Use test data to check parameters and exception handling.
6. Add manual confirmation for high-impact actions.
7. After going live, audit the calls and regularly rotate the credentials.

Enterprise PoC Tutorial

1. Choose production scenarios with clear value and quantifiable metrics.
2. Prepare the actual documents and system interfaces to be desensitized.
3. Determine the boundaries for SaaS, on-premises, or isolated deployments.
4. Establish quality and safety standards together with suppliers.
5. Run the PoC and measure accuracy, time consumption, and adoption rate.
6. Verify permissions, references, rollback, and auditing capabilities.
7. After reaching the threshold, expansion to production is carried out in stages.

Safety and compliance

Allganize lists explicitly the support it provides for ISO 27001, SOC 2 Type II, and HIPAA, emphasizing that when the solution is deployed locally, the data remains within the customer’s infrastructure. Purchasers should still verify the issuer of the certificates, the products covered, the data locations, the subcontractors involved, and the contractual responsibilities.

• Access enterprise single sign-on via SAML or OIDC.
• Control permissions for applications, documents, and tools by role.
• Encrypt static and transmitted data.
• Logs the administrator and Agent operation records.
• Isolated networks restrict external models and connections.
• Set content safeguards and sensitive data policies.
• Regularly test backup, upgrade, and rollback processes.

How to understand the concept of zero hallucinations?

The official website makes use of Zero Hallucination to ensure accuracy in presenting the original text, to include citations, and to enable self-checking of responses; however, no generative AI should be considered to be error-free. A more accurate approach is to reduce the risk of hallucinations by limiting the sources of information, showing evidence from the original text, and conducting manual reviews.

Pricing method

The Allganize official website does not specify fixed prices for Works, on-premises deployment, and the enterprise platform; typically, a quote is required based on the number of users, documents, models, tokens, integration options, and level of support needed. The Coworker self-service portal offers a free tier for teams of 1 to 50 members, with subsequent fees charged based on the number of model tokens used.

It needs to be confirmed before purchasing.

• Number of free credits and expiration rules.
• The pricing methods for tokens in different models.
• Is document storage, OCR, and indexing charged separately?
• Costs associated with Agent tool calls and external APIs.
• Differences in licensing for SaaS, on-premises, and isolated deployments.
• Number of users, administrators, and external access rights.
• Scope of services for system integration, migration, and implementation.
• Supports response times, upgrade frequencies, and service levels.

GitHub and the open-source status

Allganize has an official GitHub organization and makes a few development tools available publicly, such as the pre-commit integration for type checking. The Alli core platform, the RAG engine, Coworker, and Works remain commercial, closed-source products; they cannot be considered fully open source just because there is an official repository available.

Product advantages

• It also offers custom builders and ready-to-use work agents.
• RAG responses can indicate the page number and section in the original text.
• Covers the entire stack including modeling, retrieval, Agents, operations, and governance.
• Supports SaaS, on-premises, hybrid, and fully isolated deployments.
• It is possible to switch between various commercial and open-source models.
• Offers versioning, staging, approval, and one-click rollback.
• It can be connected to ERP, databases, APIs, Slack, and Teams.
• Suitable for regulated industries such as finance, manufacturing, and the public sector.

Usage restrictions

• There is no publicly established, fixed price for the company’s products.
• Zero illusions should not be understood as an absolute absence of errors.
• The performance of large files and complex data requires verification in a real environment.
• Writing to external systems by agents increases the risk related to permissions.
• High-quality RAG relies on document governance and continuous updating.
• Local deployment requires the customer to have the infrastructure and operational capabilities.
• The complete platform and the core RAG engine are not open-source software.
• Implementing a solution in a company typically requires investments in PoCs, integration, and governance.

Basic information

Recommendation score

The comprehensive recommendation score is 4.7 out of 5 points. Allganize is suitable for companies that need document-based evidence, code-free agents, multiple models, and strict deployment controls; however, its price and implementation complexity make it more appropriate for organization-level purchases.

Frequently Asked Questions

What does Allganize mainly offer?

It offers the Alli enterprise AI platform, which includes the code-free application builder Coworker and the document work intelligence agent Works.

What is the difference between Coworker and Works?

Coworker is used for building custom applications and agents, while Works offers ready-made capabilities such as contracts, RFPs, and knowledge-related tasks.

Does it support Q&A in corporate knowledge bases?

Supported: Alli RAG will retrieve information from the specified documents and include quotes from the original texts in its responses.

Can it be deployed locally?

Yes, it also supports isolated deployment that prevents data from being transmitted to external networks.

Which large models are supported?

It supports GPT, Claude, Gemini, Llama, as well as other commercial, open-source, and fine-tuned models.

Are APIs provided?

It provides REST APIs for LLM applications, conversations, document retrieval, and knowledge bases.

How much does Allganize cost?

Coworker allows teams of 1 to 50 members to start using the service for free, with payment based on tokens; a quote is required for full corporate solutions.

Really, no hallucinations will occur?

The platform reduces hallucinations by restricting sources, requiring citations to the original text, and conducting self-checks, but critical results still need to be verified manually.

Is Allganize open source?

The core Alli platform is not open-source; the official GitHub account only makes a small number of projects intended for auxiliary development available to the public.