Answers need citations
If staff cannot see the source page, section, or record behind an answer, document AI becomes a trust problem.
03 Document AI & RAG
Document AI that finds answers, extracts fields, and shows the source.
For PDFs, contracts, policies, forms, and records where staff need citations, review queues, and clean structured output.
Get free audit Fully Automated
CitationsSource answers
ReviewHuman correction
Any formatDocument input
What we build
Document workflows for messy PDFs, records, and review-heavy decisions.
01
Turn PDFs, scans, tables, forms, and exports into clean searchable records.
02
Let staff ask questions and see the page, section, or record behind each answer.
03
Extract fields from contracts, policies, forms, invoices, reports, and admin files.
04
Route uncertain answers, missing fields, and low-confidence results into a review queue.
05
Keep access rules in place for clients, departments, workspaces, matters, or user roles.
06
Ship the upload flow, search view, review dashboard, logs, deployment, and handoff.
AI foundation
RAG starts with document structure, not just embeddings.
01
Clean document intake
Files are organized with page, section, table, and source details before the AI is allowed to answer from them.
02
Source-backed search
Answers point back to the source document so staff can check the result instead of trusting a black box.
03
Reviewable output
Important summaries, answers, and extracted fields can be approved, corrected, exported, or sent to the next workflow.
Before we build
Define the document workflow before choosing the model.
01
Document AI Architect
Maps document classes, extraction targets, review states, and confidence thresholds.
02
RAG Engineer
Builds chunking, vector indexes, retrieval evaluation, reranking, and citations.
03
Backend Engineer
Ships FastAPI and Node.js services for ingestion, queues, processing, and APIs.
04
Frontend Engineer
Builds Next.js dashboards for source review, exceptions, and structured output packs.
05
QA & Eval Lead
Tests extraction quality, retrieval misses, hallucinations, and edge-case documents.
06
Workflow Strategist
Keeps the first document workflow tied to a measurable operational bottleneck.
Build choices
How document AI stays source-grounded and reviewable.
01
Document intake
Messy PDFs, scans, tables, forms, and exports need a clean intake path before any answer can be trusted.
PDFsOCRTables
02
Retrieval design
Chunking, metadata, reranking, and citations decide whether users get the right source or a confident guess.
ChunksMetadataCitations
03
Review workflow
Important answers and extracted fields need status, source, confidence, and a correction path for staff.
SourceStatusOverride
04
Access control
Documents often have client, matter, tenant, department, or role boundaries. Retrieval must respect them.
RolesTenantsRules
05
Backend pipeline
Ingestion, queues, retries, exports, and API endpoints keep document AI useful after the first upload.
QueuesRetriesExports
06
Quality checks
Sample questions, expected answers, logs, and failure review stop the system from drifting quietly.
EvalsLogsFailures
Shipped system
Referenceable RAG workflow, not a document chat demo.
We build Retrieval-Augmented Generation (RAG) systems that let you ask questions of your own data. Feed it your documentation, product catalog, legal contracts, or internal knowledge base - and get accurate answers with sources. No hallucinations, no generic responses.
Plan this workflow →AI Product Integration
RAG Pipeline & Document AI
01
Document intake that keeps pages, sections, and metadata intact
02
Search tuned for accurate answers, not just uploaded files
03
Retrieval that finds the right policy, clause, record, or evidence
Workflow signals
Teams measure RAG by source trust and review speed.
Extraction needs correction paths
Every important field should carry status, source, confidence, and reviewer override instead of disappearing into a black box.
Access rules matter
Document sets often contain tenant, client, matter, department, or role boundaries. Retrieval has to respect those boundaries.
Evaluation is part of launch
Keep sample questions, expected answers, and edge cases so retrieval changes do not quietly break quality later.
Why us
Leaders prefer Aiovix when document AI needs auditability.
01
Layout-aware PDF chunking instead of naive page splitting
02
Vector database optimization with metadata and evals
03
Semantic multi-vector retrieval for complex document sets
04
Source citations, confidence checks, and blocked claims
05
Review dashboards for exceptions and structured outputs
06
Hosted or local model orchestration based on privacy needs
Insights
Planning notes from the document AI bench.
01
Start with one document type and one review outcome.
02
Tune chunking and retrieval before adding more documents.
03
Use source-linked output packs for legal, finance, and operations review.
Questions
Questions, answered clearly.
Short answers for teams deciding how document AI should ingest, retrieve, cite, and route review.
What makes your RAG pipelines different from a basic document chatbot?
We handle layout-aware chunking, metadata filters, reranking, multi-vector retrieval, citation construction, and retrieval evaluation instead of simply embedding whole pages and hoping search works.
Can the vector database be tuned for speed and accuracy?
Yes. We tune index type, chunk size, namespace design, metadata fields, hybrid search weights, and reranking thresholds based on measured recall and latency.
How do you handle complex PDFs?
We use advanced PDF layout chunking for tables, headers, clauses, page references, and scanned sections so retrieval preserves the structure users need to trust the answer.
Next step
Put one document workflow into production.
Send the document type, source systems, and review outcome. We will show the first useful RAG workflow.