01
Bank operations
Banking documents
Recognize and classify payment orders, contracts, statements and scans. Extract key details and verify payment purposes.
AI PLATFORM FOR FINANCIAL SERVICES
AI agents.Working foryour bank.
Mitra connects language models, knowledge and banking systems to automate how your teams work with documents and information.
Developed by SoftClubSelected agents are already used in production
On-premiseDeployment within the bank's environment
RAGAnswers grounded in your documents
REST APIIntegration with existing systems
01 / THE PLATFORM
From information
to a working process.
Mitra is SoftClub's proprietary AI agent platform. Its builder combines extraction, analysis, generation and integration modules into agents. Modules can be reused and extended as new requirements emerge.
How an agent worksModels and modules are selected for the task at hand
01Input
Documents · PDFs · images
02Mitra AI agent
Extraction · analysis · generation
03Output
JSON / XML · application modules
Bank knowledge baseLanguage modelQuality control
02 / CAPABILITIES
One platform.
Across your teams.
Automation for teams that handle large volumes of information every day.
02
People and expertise
Knowledge inside the bank
An AI assistant for policies, instructions and regulatory documents. Answers are grounded in the bank's knowledge base and can be refined.
03
IT and business analysis
Documentation and development
Generate technical specifications, contracts, reports and release notes. Automate code review for IT teams.
04
Customers and service
Customer support
Agents for recurring questions, with control over answer quality and format. Calibration helps refine responses and define their scope.
03 / IN PRACTICE
Banking documents.
Structured results.
Payment documents, contracts and statements become usable data for core banking application modules.
CASE STUDY / BANK PILOT
01
The challenge
Manual document processing supports account opening, financial statement analysis, customer questionnaires and preparation of data requested by authorized authorities.
02
The solution
Specialized agents extract document details, dates, amounts, account numbers and party information. Results return to the bank's systems in JSON or XML format.
PDFOCRJSONXMLREST API
What the bank gets
- Customer data reconciled against documents
- Validated payment-document details
- Automatically completed on-screen forms
- Data for document and banking systems
Project status
Tested using real bank test data. The first demonstration is complete; the bank is selecting deployment infrastructure.
More platform case studies
Experience from financial services and documentation automation.
Customer service
57calibration scenarios
AI consultant for a crypto exchange
A knowledge base, semantic search and a judge agent to evaluate answers.
Read the case studyShow less
The Mitra-Dev help bot uses SimpleAnswer and KnowledgeBase modules. It answers from documents covering fees, OTC, the referral program and other platform services, and declines questions outside its scope.
Response accuracy increased from 95.1% to 100% across 57 calibration scenarios. This result applies to that specific test set. The prototype was handed to the exchange for testing; full deployment is still in progress.
AI agents · RAG · Agent-as-a-Judge
Content and data
26RSS sources
News and market analytics
Two agents collect, process and publish crypto news and trading-pair analytics.
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The first agent collects news from 26 RSS sources, summarizes and ranks stories, removes duplicates and publishes a Telegram digest twice daily. It generates illustrations when needed.
The second retrieves trading volumes through the exchange API, converts them to USD and produces a daily review of the top 10 crypto pairs. Services run in Docker across dev/test/prod environments.
Python · LLM · Telegram Bot API · Docker
Business analysis
130pages in the specification
Technical specifications with AI
A multi-agent process for creation, critique, refinement and final review.
Read the case studyShow less
A 130-page technical specification following GOST 34.602-89 was created from 388 paragraphs of requirements, including 19 editable draw.io diagrams. The final AI reviewer completed its check with zero comments.
The business analyst received a foundation for proofreading and finalized the document in 7 days. He estimated that preparing the first version required 70% less effort.
Multi-agent LLM · draw.io · Bitbucket · Confluence
Companies are not disclosed under NDAs. Metrics relate to the specific projects described.
04 / INTEGRATION AND CONTROL
AI within your
banking architecture.
Mitra complements existing systems and lets you choose models around your data requirements and use case.
On-premises models
Deploy within the bank's environment using proprietary or locally hosted models. In this configuration, data stays inside the security perimeter.
REST API and authorization
Receive documents, route requests to agents and return results to core banking modules. Connect with enterprise systems, Confluence and Jira.
Quality control
Calibrate answers and use classifiers to check output format and quality. Refine responses around your organization's tasks and knowledge.
MITRA / SOFTCLUB
Your processes.
Mitra's capabilities.
Talk to the SoftClub team about your bank's needs and the platform's capabilities.
Contact SoftClubVisit softclub.com