#Case Study#FinTech#API Integration
Case Study: Reducing FinTech Query Processing Time by 62%
By Naala Engineering Team
Last quarter, we completed a 14-day pilot with a mid-market FinTech firm in Nairobi. Their challenge was familiar: customer support queries and loan status checks were flowing into a queue faster than their team could process them manually.
## The Before State
- ~500 customer queries per day via API
- 15+ hours of engineering time weekly on manual fallbacks
- Average response time: 4.2 hours
- SLA breach rate: 18%
They had a solid API stack. They had the data. What they didn't have was an intelligent layer to route, reason, and respond.
## The Agent Solution
We built a stateful AI agent that:
1. **Ingests** customer queries via their existing webhook endpoint
2. **Reasons** about intent using their product knowledge base
3. **Acts** by calling internal APIs for loan status, account balance, and payment history
4. **Responds** in English or Swahili, depending on the customer's preference
5. **Escalates** complex cases to human agents with full context preserved
The agent runs as a service within their AWS architecture. It calls their APIs, writes to their audit log, and respects their VPC.
## The Results
| Metric | Before | After | Change |
|--------|--------|-------|--------|
| Avg. response time | 4.2 hours | 1.6 hours | -62% |
| Manual intervention | 15 hrs/week | 2 hrs/week | -87% |
| SLA breach rate | 18% | 3% | -83% |
| Customer satisfaction | 3.1/5 | 4.4/5 | +42% |
## Why 14 Days?
The pilot was scoped to one workflow: loan status inquiries. This was a contained problem with clear success metrics. We didn't try to boil the ocean.
The client got:
- A working prototype
- API integration specs
- System documentation
- A recorded demo for their board
## The Path Forward
This pilot converted into a monthly retainer. We're now building a second agent for their claims processing workflow.
If you're dealing with similar bottlenecks — high-volume manual processing, API data that isn't being acted on, or customers waiting too long for answers — let's talk. We'll scope a 14-day pilot and build a prototype that proves the value before you commit.