Research
SecureDocAI: Private Document Intelligence
A research-driven effort focused on retrieval-first question answering for sensitive document environments.
The goal is to build a practical, offline-capable architecture where answers remain grounded, traceable, and policy-aligned.
SecureDocAI Investigation
Objective, experiments, and failure-led iteration toward reliable retrieval-based answering.
Research Pipeline
End-to-end flow from raw documents to evaluated answers, designed for private and constrained deployment settings.
Step 1
Documents
Source reports, notes, and procedural text are collected with context metadata.
Step 2
Parsing
Document structure is extracted and normalized for downstream processing.
Step 3
OCR
Image-based pages are converted to searchable text where extraction fails.
Step 4
Chunking
Text is segmented into context windows that preserve semantic continuity.
Step 5
Embeddings
Chunks are transformed into vector representations for similarity retrieval.
Step 6
FAISS
Vectors are indexed for low-latency local semantic search.
Step 7
Retrieval
Top-ranked context is selected with metadata-aware filtering.
Step 8
Offline LLM
A local model generates responses conditioned on retrieved context.
Step 9
Answer
Responses are structured for readability and source-grounding checks.
Step 10
Evaluation
Outputs are reviewed for relevance, grounding, and consistency.
Objective
Design a private-by-default retrieval and answering workflow for teams that cannot rely on external cloud AI services for sensitive document analysis.
Experiments
Experiments focused on chunk sizing, overlap behavior, retrieval depth, and prompt constraints to improve answer grounding while preserving response speed in local deployment conditions.
Failure Analysis
Observed failure modes include OCR noise propagation, weak retrieval under sparse phrasing, and occasional response overgeneralization when evidence context is under-specified.
Future Work
Planned work includes reranking integration, multilingual document support, automated retrieval regression checks, and stronger citation-level confidence tracing.
Metrics
Metrics are intentionally shown as TODO placeholders until validated evaluation values are finalized.
Grounded answer rate
TODO
Pending validated benchmark suite definition.
Retrieval latency (median)
TODO
To be measured after final index and model configuration lock.
Citation consistency
TODO
Requires rubric finalization for multi-document answers.