Our AI development experts built an AI Support Copilot for a digital payments platform using Agentic AI, function calling, RAG pipelines, vector search, transaction APIs, compliance guardrails, and multilingual automation, helping automate support tickets, reduce response times from 9 minutes to 5 seconds, and improve agent productivity.
A global digital payments platform, One Wallet, serving 1.8M+ registered users across North America and APAC. The platform processes over $450M+ in monthly transaction volume across wallets, remittances, virtual cards, and P2P payments.
Recently, their user base and transaction activity increased, and their support teams faced growing volumes of transfer tracking requests, KYC inquiries, payment-related questions, and account support cases. They wanted to improve customer assistance without proportionally increasing operational resources and costs.
We partnered with their team to build an AI-powered support copilot for modern payment operations. Their goal was to provide instant assistance across web, mobile, and messaging channels like WhatsApp. They needed a solution capable of retrieving live transaction data through structured API interactions. They also wanted AI-driven access to support documentation, operational procedures, and compliance knowledge.
To make their requirement available in the real world, our AI experts focused on reducing repetitive support workloads, improving agent productivity, accelerating case resolution, and delivering consistent support experiences for millions of customer interactions annually.
The digital payments sector processes billions of transactions annually while customer expectations continue rising. Support operations must manage real-time payment visibility, compliance obligations, multilingual interactions, and fraud-related concerns without compromising response quality. These market pressures create operational bottlenecks that traditional support systems struggle to address.
Fragmented transaction data across payment, wallet, and CRM platforms
Growing demand for AI-powered support across multiple customer channels
Limited visibility into cross-border payment and settlement journeys
60K+ monthly support requests are increasing operational pressure on teams
Manual KYC verification assistance is slowing onboarding completion rates
Lack of intelligent knowledge retrieval from compliance and support repositories
14-minute average handling times impacting customer service efficiency
Frequent context switching between ticketing, payment, and customer systems
As part of the engagement, SoluLab worked closely with OneWallet's stakeholders to identify the foundational capabilities required for an AI-powered support ecosystem. The objective was to combine Agentic AI, retrieval-augmented generation (RAG), transaction intelligence, and compliance-aware automation into a single support architecture capable of handling high-volume payment operations while improving customer and agent experiences.
Customers needed immediate visibility into payment journeys, settlement progress, transfer confirmations, and processing events without relying on support agents to manually investigate transaction records.
Rather than generating assumptions, the support copilot needed the ability to execute structured actions across payment APIs, customer services, fee calculators, and operational systems using governed workflows.
More than 50,000+ support documents, SOPs, payment policies, and operational playbooks had to be converted into a searchable intelligence layer capable of delivering source-backed responses.
Financial transactions involve sensitive customer information, making automated redaction, tokenization, and controlled data exposure essential before LLM processing.
With customers operating across multiple regions, the platform required consistent support experiences in different languages while preserving payment terminology and contextual accuracy
Support representatives needed instant access to response suggestions, customer context, case summaries, sentiment insights, and next-step recommendations from a single interface.
Verification-related queries represented a significant share of incoming tickets, creating the need for guided onboarding assistance throughout document submission and identity verification stages.
Customer information was distributed across 10+ operational systems, including CRM platforms, payment engines, transaction ledgers, ticketing tools, and support databases.
Every AI-generated response, API interaction, retrieval source, and compliance validation requires traceable records to support governance standards and operational accountability.
To bring our client's vision to life, SoluLab's AI development experts aligned advanced technologies, payment intelligence frameworks, and enterprise-grade automation capabilities into a unified support architecture. Our team delivered a customer support copilot capable of handling payment operations, knowledge retrieval, compliance-driven assistance, and agent productivity workflows from a single intelligence layer.
Built on LangChain and structured function calling, the support agent delivers multilingual assistance for transfer tracking, KYC guidance, wallet inquiries, and payment-related requests while retrieving verified transaction information directly from backend payment systems.
At the core of the solution, an Agentic AI orchestration framework connected transaction APIs, CRM platforms, wallet services, and payment engines, enabling deterministic transaction lookups, payment tracing, workflow execution, and real-time operational decision-making.
To eliminate knowledge silos, our RAG specialists developed a vector-powered retrieval layer indexing 50k+ support documents, compliance policies, onboarding procedures, and operational playbooks, ensuring every response was grounded in enterprise-approved information.
Given the sensitive nature of financial transactions, automated PII masking, role-based access controls, validation checkpoints, and governed data processing workflows were implemented to protect customer information before it entered LLM-powered support interactions.
Designed for high-volume support operations, the workspace consolidated customer history, transaction activities, ticket context, and AI-generated recommendations into a single interface, where our AI engineers enabled response drafting, summarization, and escalation guidance.
Our team observed that direct API interactions produced more accurate results than standalone AI-generated answers.
Well-organized support content and metadata significantly enhanced RAG performance and response consistency.
PII masking and validation workflows were most effective before customer information reached AI models.
Compliance reviews, payment disputes, and exception handling continued to benefit from expert intervention.
Bringing together CRM, transaction, and support records helped agents make faster and more informed decisions.
Most digital payment platforms eventually hit the same wall: more users, more tickets, more operational costs. SoluLab helps fintech companies break that cycle with AI Support Copilots, Agentic AI, and intelligent automation.
Ticket Load Eliminated
Second Response Speed
Faster Issue Closures
Support Spend Slashed
CSAT Lift
Agent Output
GPT-4o
Claude
LangChain
LangGraph
Function Calling
Agentic AI Workflows
RAG Architecture
Vector Embeddings
Pinecone Vector Database
Semantic Search
Python
FastAPI
REST APIs
Payment APIs
CRM Integrations
Zendesk Integration
PII Masking
Role-Based Access Control (RBAC)
Prompt Guardrails
Response Validation
Audit Logging
AWS
Kubernetes
Redis
Kafka
GitHub Actions
Datadog
“We needed an intelligent support layer that could understand payment operations, access the right information, and help both customers and agents make faster decisions. When we contacted SoluLab, they explained the solution and adjusted our requirements. Finally delivered a well-architected AI service that brought together transaction intelligence, knowledge retrieval, and operational automation into a single experience. Their team's understanding of AI and fintech made a significant difference throughout the engagement.”
