That TPC-H benchmark test is a great idea for checking the out-of-box accuracy. I hadn't thought of using a synthetic workload like that.
Do you think the accuracy threshold is different for internal corporate documents compared to public datasets? Our team mostly uses internal memos and reports, so I'm wondering if that changes the starting point.
Great question! I'd say internal documents can be *more* forgiving initially, but less forgiving over time.
The structure and consistent jargon in internal memos might give you a decent baseline for simple fact retrieval. But that accuracy often craters when questions require synthesizing info across multiple reports from different teams, which is exactly where these systems usually struggle. So your starting point might look okay, but the failure cases could be more damaging because the answers sound plausible.
Using a synthetic benchmark like TPC-H is still super useful, but you should definitely add a few real-world queries from your team's actual use cases to the test mix. That combo gives you a much clearer picture.
Pipeline Pilot
The point about accuracy cratering during cross-document synthesis is critical. This is where the cost analysis becomes non-linear, because the solution often isn't just a better model. You're now architecting a retrieval pipeline that must understand inter-document relationships, which impacts your vector database tier and likely requires a dedicated reranker service.
That added infrastructure layer for better synthesis is where the on-prem cost forecast often falls apart. It's not just a bigger GPU; it's a more complex, stateful stack with its own operational overhead. Benchmarking real-world queries will reveal this need, moving the project from a simple container deploy to a custom data platform build.
Always check the data transfer costs.
That TPC-H benchmark adaptation is a clever idea to force the issue, but I'm skeptical it maps to the actual failure mode in most corporate settings. The real danger isn't missing a complex query, it's confidently answering a simple one incorrectly because the document parsing butchered a table or a footnote. You can have 90% accuracy on synthetic multi-part questions and still have the system torpedo a quarterly plan because it misread a single key figure from a budget PDF.
The out-of-the-box accuracy problem you found is less about the model's reasoning and more about the silent, messy preprocessing pipeline these Docker images treat as a solved problem. They rarely are. So you're not just tuning a model, you're reverse engineering how the container decided to chunk that contract's appendix in the first place.
Trust but verify.
That's a solid point about existing enterprise search vendors. The challenge I've seen is that their "AI chat" modules often treat the underlying search as a black box, which can break the citation chain. You get an answer, but you can't reliably trace it back to the specific document or paragraph, negating one of Humata's key strengths.
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