G2

Verified User in Marketing and Advertising

Mid-Market (51-1000 emp.)

2/24/2026

"Architectural Transformation and Performance Optimization for an AI Analytics Platform"

5/5

What do you like best about Incode Group?

What we value most about Incode Group is their engineering mindset. They don’t just implement tasks – they analyze systems, identify structural weaknesses, and propose improvements that strengthen the product long-term. Their ability to work confidently with large-scale data infrastructure and complex analytical logic sets them apart. We also appreciate their direct communication style and practical approach to solving technically demanding problems. Review collected by and hosted on G2.com.

What do you dislike about Incode Group?

We were satisfied with the collaboration and the technical execution. At this stage, we don’t see any major areas for improvement. The team delivered what was agreed and supported us throughout the process when adjustments were needed. Review collected by and hosted on G2.com.

Recommendations to others considering Incode Group:

If you are looking for a team that can handle complex backend systems, high-load data processing, and architectural transformations, Incode Group is a strong choice. They are particularly well-suited for projects that go beyond standard web development and require deep engineering expertise.

We would recommend clearly defining technical priorities and long-term product goals at the start — their team is capable of contributing valuable architectural input when given strategic context. For data-intensive or AI-driven platforms, their experience with scalable infrastructure and performance optimization is especially valuable. Review collected by and hosted on G2.com.

What problems is Incode Group solving and how is that benefiting you?

Incode Group helped us address architectural limitations that were affecting the scalability and performance of our AI-driven analytics platform. As our dataset continued to grow, existing search and aggregation mechanisms were becoming resource-intensive and costly.

By redesigning parts of the system into a microservices structure and migrating analytical workloads from Elasticsearch to ClickHouse, they improved query speed, reduced infrastructure overhead, and made performance more predictable under load.

They also introduced AI-powered services for automated video transcription and creator analysis, which expanded the platform’s analytical depth and reduced manual processing.

As a result, we now operate on a more stable, cost-efficient, and scalable architecture that supports continued product growth and more advanced data insights for our users. Review collected by and hosted on G2.com.