The problem
Every corpus is different.
The chunking and retrieval setup that works for clean product documentation fails on dense financial filings full of tables and cross-references. There's no universal configuration.
So teams hand-tune every time.
Try a chunk size. Swap the embedding model. Adjust retrieval. Eyeball whether results look better. Repeat — for every new client or customer.
That doesn't scale.
Doing it once is a week of an engineer's time. Doing it for every onboarding is a permanent tax on margin — and when it's skipped, retrieval quality quietly degrades and customers churn.
What Aptessa does
1. Point it at your documents.
Ingests real-world formats — PDF, Word, HTML, Markdown, plain text — including the structured tables inside them.
2. It tries real configurations.
Multiple chunking strategies, retrieval approaches, and settings are actually run against your corpus — not selected from a rule of thumb.
3. It measures which one wins.
Aptessa generates its own evaluation set from your documents and scores each configuration against it, so "best" is a measured result rather than an opinion.
4. You get a configured pipeline, plus the evidence.
The chosen configuration, what else was tried, and why this one won.
Why it's different
Frameworks give you the building blocks. Aptessa decides which ones to use.
General-purpose RAG frameworks hand you every option and leave the choice to you. Aptessa makes the choice and shows its work.
Adaptive at every layer.
No hard-coded chunking strategy, no single default embedding model, no fixed retrieval approach — every layer is selected per corpus.
Proof, not assertion.
Every configuration decision is backed by measurement against the actual documents, and the reasoning is retained so it can be inspected and audited.
Built for the hard cases.
Developed against financial documents specifically — tables, dense narrative, and heavy cross-referencing — because that's where generic retrieval visibly breaks down.
Benchmarked the right way
Measured against industry-recognized benchmarks and real-world corpora representing the hardest retrieval problems — not synthetic data.
Full results and methodology will be published.