Industry
Client Overview
A Series B FinTech startup in the USA aiming to disrupt the small business lending market. Their goal was to provide faster, fairer loan decisions than traditional banks by leveraging alternative data sources. They had a strong business concept and a small in-house team but lacked the specialized AI/ML engineering and backend development capacity to build their platform at the speed the market demanded.
Client Testimonial
"CIS was the engineering firepower we needed to turn our vision into a reality. Their Staff Augmentation POD didn't just feel like an external team; they were our partners, deeply invested in our product's success. We launched our platform three months ahead of schedule, and the AI credit scoring model they helped build is our single biggest competitive advantage. We would not have secured our Series C funding without the platform they helped us create." - Founder & CEO
Problem
The client needed to build a complete digital lending platform from the ground up. The core of this platform was an AI-driven credit scoring model that could analyze non-traditional data (e.g., online sales data, social media sentiment, supplier payment history) to assess risk for businesses without extensive credit histories.
Key Challenges
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01
Speed to Market : The client was in a race against competitors and needed to launch an MVP within six months.
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02
Talent Gap : They could not find or afford the specialized data scientists and backend engineers needed to build the platform.
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03
Scalability : The platform had to be built on a scalable cloud architecture to handle rapid user growth post-launch.
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04
Data Integration : The system needed to integrate with dozens of third-party APIs (Plaid, Stripe, Shopify, etc.) to pull in alternative data.
Our Solution
We provided a MEAN/MERN Full-Stack Pod and a Python Data-Engineering Pod under our Staff Augmentation model, which integrated directly into the client's product team.
Agile Development : Working in two-week sprints, our team took ownership of the entire backend and AI development, allowing the client's small in-house team to focus on the frontend and business logic.
AI Credit Model : Our data scientists developed a custom machine learning model using a gradient-boosting framework that could process diverse data types and generate a highly predictive credit risk score.
Microservices Architecture : We built the backend on AWS using a serverless, microservices-based architecture, ensuring the platform was cost-effective and could scale on demand.
Third-Party API Integration : Our engineers built a robust and resilient integration layer to connect with over 20 external data source APIs, creating a rich dataset for the AI model.
Implementation & Execution
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Weeks 1-2
Onboarded our PODs and integrated them into the client's Jira and Slack, establishing a seamless, collaborative workflow.
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Weeks 3-10
Focused on building the core platform services: user authentication, API integration layer, and the data ingestion pipeline.
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Weeks 11-18
Developed, trained, and validated the first version of the AI credit scoring model while simultaneously building the loan origination and management workflows.
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Weeks 19-22
Conducted end-to-end testing, integrated the backend with the client's frontend, and prepared for a closed beta launch.
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Week 23
Launched the MVP to a select group of beta customers.
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Week 24 - Ongoing
Continued to iterate on the platform, adding new features and refining the AI model based on real-world loan performance data.
Positive Outcome
1. Accelerated Launch
The platform MVP was launched in under 6 months, three months ahead of the client's original schedule.
2. Secured Funding
The successful launch and innovative technology were key factors in the client securing a $50M Series C funding round.
3. Superior Underwriting
The AI model proved to be 30% more accurate in predicting defaults than traditional models, allowing the client to approve more loans safely.
4. Scalable Business
The platform has now processed over $100M in loan applications and has scaled seamlessly with zero downtime.
Why Choose Us
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Staff Augmentation PODs
Provided the exact, specialized talent the client needed, when they needed it.
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Speed & Agility
Our agile process and expert team were built for the rapid pace of a startup.
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Cost-Effectiveness
Our global delivery model gave the startup access to top-tier talent at a sustainable cost.
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White Label & IP Transfer
The client retained 100% ownership of their core intellectual property, the AI model, and platform.
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Scalable Cloud Expertise
We built a platform that could grow with their business from day one.
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Collaborative Culture
Our team became a seamless extension of their in-house engineering department.
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Free Replacement Guarantee
Gave the client confidence that they would always have the right talent on their team.
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Focus on Business Goals
We understood that the goal was not just to write code, but to build a business and secure funding.
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Full-Stack Capability
Our pods covered the entire technology stack, from data engineering to backend services.
Conclusion
This case study highlights CIS's role as an accelerator for FinTech innovation. By providing flexible, expert talent through our POD models, we empower startups to compete with established players, get to market faster, and build truly disruptive technology.