"The demo is done, but it's not in operation"
Over the past year, many companies have conducted LLM PoCs. They created chatbots with internal documents, summarized meeting minutes, and extracted draft reports. The demos were generally successful. However, when it comes time to show the executives "AI cases in operation," most of them come to a halt at the same point.
Many often look for the cause in the model. "If we use a better model," "If we refine the prompts more" — but the fact that the demo was successful means the model was already sufficient. The real bottleneck lies elsewhere. While a demo only requires inputting data once, operation requires a continuous flow of data. The reason why PoCs turn into graves is because this 'continuity' was not designed.
Demos and operations are different entities
During a PoC, data is typically prepared once. After collecting a few hundred documents and confirming that the AI responds well, the demo is over. The problem arises afterwards.
- The stored documents become outdated over time. AI that answers based on a price list from 3 months ago or last quarter's competitive situation loses credibility.
- The range of questions that can be answered with internal documents is limited to regulations, benefits, and manuals. It cannot answer questions like, "How much did our competitor offer this time?"
- There is no set rule on who will update the data and how frequently.
The demo is a 'snapshot at a moment in time,' while operation is a 'continuously updated flow.' The gap between these two is reflected in the disparity between PoC and operation conversion rates.
Three data issues preventing the transition to operation
1. Supply is cut off
Operational services need data to come in regularly to stay alive. However, data manually collected during the PoC phase lacks a recurring supply structure. If the person in charge gets tired of updating manually every time, the service quietly begins to deteriorate.
2. Data to be input is only within the company
For AI to be useful for business, it requires external data such as market, competitors, and customer responses. However, most PoCs start with internal documents only, lacking the materials to answer questions directly related to revenue. This is where they get stuck when faced with executive questions like "What does our AI do?"
3. Lack of responsibility for updates
It is not clear who will notice when the data becomes outdated and who will put in fresh data. While there are responsible parties for the model and infrastructure, there are often no responsible parties for "keeping the data fresh."
All three are not model issues but issues with the data supply chain. Without a supply chain, even the best model will end up as a fresh demo for a few days.
Condition for transitioning to operation: Turning data into a 'flow'
To transition from a PoC to operation, the one-time loading must be transformed into a regular supply pipeline. There are four things to check.
| Item | Question |
|---|---|
| Supply cycle | How often is the data updated (daily/weekly)? |
| Refinement | Is there a stage where the original data is processed into a form directly usable by AI? |
| Detection | Does it detect when the supply is cut off or the data becomes outdated? |
| Responsibility | Is there a designated party responsible for maintaining this flow? |
The key is not 'inputting once' but 'continuously inputting.' And this continuous input is not the responsibility of the model team but of a separate layer responsible for collection, refinement, and delivery.
Building it yourself or outsourcing the supply
To build an external data supply chain yourself, you need to develop crawlers, handle blocking infrastructures, maintain them when sites change, and monitor collection continuously. This can be a significant burden for AI transition teams without dedicated development resources, and it can be challenging to keep developers on board for ancillary purposes.
Therefore, outsourcing data supply to external parties becomes an alternative. By outsourcing collection, refinement, and delivery to a pipeline, AI transition teams can focus on models and use cases while treating data as something that 'continuously comes in.' Hashscraper provides everything from collection to AI analysis (sentiment, classification, translation), API/DB delivery in a regular pipeline, and handles crawler operation, maintenance, and monitoring. We also support a connection method (MCP) that AI agents can call directly, allowing them to keep an eye on external data.
Frequently Asked Questions
Q. If we switch to a better model, will it lead to a transition to operation?
If the demo was successful, the model is often already sufficient. The key difference in operation lies in whether the data is continuously supplied and updated. Changing the model will not solve this issue.
Q. How long does it take to establish an external data supply chain?
If the collection targets and items are defined, it doesn't take long to establish regular supply. If you start from defining requirements, the initial stages may take longer. If you provide the targets and objectives, we can guide you on the schedule.
Q. Can we use internal and external data together?
Yes. By refining externally collected data and delivering it through APIs/DBs, you can integrate it with internal pipelines and RAG.
Recommended Readings
- From web crawling data to decision-making
- RAG with only internal documents is incomplete — Why AI needs external market data
- Practical guide to connecting web crawling data to RAG
- Subscription-based crawling vs. individual billing — You'll lose money if you don't compare the total cost of ownership (TCO) for a year
Start Now
If you've made it to the PoC stage, you're halfway there. Let us know the purpose and targets, and we will help you design the data supply chain needed to transition to operation.
