Dashboards
The UI/UX era for delivery experience and fulfillment data is over. Static dashboards, filters, and prebuilt charts are artifacts of a world where humans had to translate questions into buttons. That world no longer scales — and in the age of generative AI, it’s being replaced by something better.
Welcome to the era of prompt-based visualization.
🧠 From Dashboards to Dialogue
In the traditional model, logistics teams relied on BI teams to surface fulfillment metrics — carrier speed, delivery exceptions, on-time percentages. But the insights were locked inside opinionated UIs, often behind weeks of engineering effort.
Now, thanks to large language models (LLMs), databases can be wrapped in natural language interfaces. This shift means a CX manager can simply ask:
“What’s our carrier performance in California over the past month?”
No clicking through Looker dashboards. No filtering in Tableau. Just… asking
🏆 The Gold Standard: Unconstrained Prompting
At the edge of development from teams at Anthropic and OpenAI, the next frontier is unconstrained prompting — where LLMs are free to navigate any schema, apply business logic, and return accurate, intelligent answers.
In this model:
- The LLM understands fulfillment data down to the timestamp granularity
- The user doesn’t need to know table names, date logic, or definitions
- Insights emerge in seconds, not sprints
It’s the holy grail: a complete abstraction of the interface layer.
⚠️ The Danger: Language ≠ Logic
But freedom has a cost.
A prompt like “What is the carrier speed for my orders in California?” could be interpreted in multiple ways:
- Average time from order placed to delivery?
- First scan to delivery?
- Order print time to in-transit?
- Business days or calendar?
These ambiguities introduce data hazards that traditional UIs were designed to guard against.
🛠 The Middle Path: Semi-Constrained Prompting
Our approach? semi-constrained prompting — a hybrid approach where the LLM is:
- Trained on a manifest of data fields
- Informed of their relationships and use cases
- Guided by common prompt patterns and definitions
Think of it as a structured playground: users can ask questions freely, but the model knows how to interpret them reliably.
This is how we maintain accuracy while preserving the magic of conversational querying.
And, both delivery experience insights and incrementality insights will soon be available in this form at Fenix Commerce!
🌐 Multi-Tenant Intelligence & Benchmarking
In a multi-tenant environment like eCommerce, there’s one more layer: retailer-level data segmentation. The LLM needs to:
- Respect tenancy boundaries (your data = your data)
- · Contextualize responses within that tenant’s data
- · And — when allowed — compare performance against anonymized category-wide benchmarks
This unlocks questions like:
“How do my 2-day delivery rates compare to other brands in my category?”
…answered in real-time, with the assurance that data integrity is preserved.
🚀 What Comes Next
At Fenix Commerce, this is not a theoretical roadmap — we’re actively building toward it. Our FulfilmentGPT and IncrementalityGPT services are designed to:
- Replace dashboards with prompt-native interfaces
- Guide teams toward profitable fulfillment decisions
- Eventually benchmark against anonymized peer data — all with just a few words
Because in the future, the best interface is no interface at all.
This articles is written by : Nermeen Nabil Khear Abdelmalak
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