Bringg Innovation & Technology Culture

What’s your rule for fast, safe releases — and what KPI proves it works?

AI innovation fails when ideas take too long to meet reality. At Bringg, our rule for fast and safe AI releases is simple: Speed creates clarity. In an environment shaped by dynamic demand, changing driver availability and real-time delivery commitments, we follow two core rules that allow us to innovate quickly while staying in control. 

What standard or metric defines “quality” in your stack?

Quality in our AI stack is defined by how reliably the system behaves in real last-mile delivery workflows and how well its performance matches user expectations in time-critical operations. At Bringg, where planning and execution decisions directly affect fulfillment, delays and customer satisfaction, quality starts with end-to-end workflow reliability. For agentic systems, we use error rate as a strict signal for functional failures and pair it with direct customer feedback to continuously tune behavior. Because agent behavior is non-deterministic, error rate alone is not enough, and softer behavioral signals are essential to ensure trust and usability. 

Performance is equally critical. User-facing AI must be fast and responsive to support real-time decision-making. When agents require heavier computation or deeper reasoning, quality means respecting those constraints by running them in the background and surfacing results as actionable insights, rather than blocking operations. This balance ensures AI improves efficiency without disrupting mission-critical delivery flows.

Name one automation that shipped recently and its impact on the business.

One AI initiative we recently built is our Capacity Planning Agent. The agent is designed to help customers plan delivery capacity more effectively in environments where demand and resource availability change frequently, a problem that is often handled today through manual decisions or intuition. It provides short-term recommendations for the coming days and supports more strategic planning by highlighting how different choices could impact operational outcomes. 

Beyond its direct customer value, this project has had a significant internal impact. Building and validating this agent reshaped how we think about the potential of AI agents across the company, demonstrating how complex, judgment-based planning tasks can be supported by agents that analyze data, simulate outcomes, and surface actionable insights. As a result, it has become a reference point for identifying other domains where agentic systems can drive meaningful business value.

The Core Rules Hayik’s Team Follows to Innovate Quickly and Effectively

• “Build, Learn, Adjust, and Fail Fast: AI cannot be tested like traditional code. Because AI systems are non-deterministic, real validation only happens when they are exposed to real delivery scenarios and customer workflows. We work in short cycles that move ideas quickly from concept into production-like reality. Features are treated as hypotheses and validated early, allowing us to surface edge cases, gather real feedback, and decide quickly whether to continue, pivot, or stop before risk and cost accumulate.”
• “Speed Enables Safety: Fast iteration only works with strong observability. Agents operate with strict boundaries and no access to customer private information. Safety becomes a continuous feedback loop that scales with speed, enabling rapid innovation without losing control in mission-critical delivery operations.”

We always have one eye on the long-term goals of the company. We share that vision regularly with our teams and with our clients. We focus on the “why.”