Context
Tarjimly helps refugees, aid workers and humanitarian organizations connect with translators during urgent moments. These moments can involve medical care, legal help, border crossings, housing support or personal emergencies.
The platform already had a strong translator network, but people still had to wait before speaking with someone. In a crisis, even a short wait can feel long. Some users only needed a quick translation while others needed a real person who could understand the emotional weight of the situation.
The challenge was to give people useful help faster without removing the option to speak with a real translator.
The Problem
The average wait time to reach a translator was 60 to 90 seconds. For someone asking for help in a stressful situation, that silence could feel like the product had failed.
Speed was only part of the problem. The bigger question was:
"How do we help users quickly while still knowing when a real human needs to step in?"
Some requests were simple. Others needed care, cultural understanding and judgment. Treating every request the same way created unnecessary delay for users and unnecessary workload for translators.
I led product design for Spark, including the first-support chat, confidence states, human handoff and the journey from the first message to resolution. I used Lottie for in-product micro-interactions and Jitter for motion videos and reusable GIF, SVG and JSON assets.
Research & Findings
We reviewed over 10,000 chat logs and saw two clear types of requests.
High-care requests
Sensitive situations where users needed empathy, context and human judgment.
Simple requests
Short translation needs, quick clarifications or basic phrases that could be handled immediately.
That shaped the direction of Spark. The goal was not to replace translators. It was to give users useful help sooner and bring in a person when needed.
Design Strategy
The main product decision was simple:
"Spark should help with the first mile of support then bring in a human when the situation needs one."
The experience was designed around two paths.
Quick Support
For simple requests, Spark gives users fast help inside the chat.
Human Handoff
When the request is sensitive, unclear or too important to handle alone, the system routes the user to a translator and keeps the conversation context intact.
Key Flow
User opens chat → User explains what they need → Spark gives immediate help if the request is simple → User can continue or ask for a translator → Complex requests are passed to a human → The translator joins with the previous context already visible.
Trust and Control
The design keeps the user in control.
Users can ask for a human at any time.
The product avoids acting like it has the final answer in sensitive moments.
The chat keeps the user moving instead of leaving them waiting.
When a translator joins, the user does not have to repeat everything.
The experience feels like support is already happening while deeper help is being arranged.
Motion & Micro-interactions
I used Lottie for micro-interactions around processing, confidence, review and completion states. I used Jitter to create the wider Spark motion flow and reusable assets for product, web and marketing.
Tools · Jitter + Lottie
Conversation, Confidence & Human Review
The sequence shows a user request, Spark’s response, the confidence state, human review and the final reviewed result.
Outcomes
Spark helped reduce the first wait from 90 seconds to under 3 seconds. Abandonment dropped from 25% to 8% and translators could spend more time on the cases that truly needed human care.
Reflection
Spark reinforced something I already believed: AI should speed up the work, but people should still be able to step in when context or judgment matters.
Tarjimly FirstPass AI
Product Designer · AI translation workflow