Stackwise
Nexus AI Workspace
A professional knowledge platform for operations, finance, legal, and healthcare teams. Upload documents, ask questions, and get trustworthy answers with source references — powered by a production-grade Agentic RAG system.
"Before building Nexus AI, I watched brilliant engineering and research teams lose 40% of their bandwidth manually hunting through hundreds of PDFs for answers. We didn't build this to be another 'AI wrapper'—we built this to reclaim human capital."
From raw PDF to grounded answer in seconds.
Ingest
Drop any PDF, contract, or report. Docling parses complex tables, headers, and multi-column layouts into clean, structured chunks — preserving the semantic hierarchy that basic loaders miss.
Index
Chunks are dual-indexed: keyword tokens land in OpenSearch (BM25) while sentence-transformer embeddings are stored as dense vectors — enabling both lexical precision and semantic recall.
Reason
A LangGraph agent evaluates your query, decides whether to retrieve more context or rewrite the question, grades each retrieved passage for relevance, and loops until it is confident.
Answer
The grounded response arrives with exact page numbers, quoted excerpts, and a confidence score. If the answer isn't in your documents, the system says so — no hallucination.
Every module ships production-ready.
Deploy one module or the entire workspace. Each package is independently versioned, containerised, and connected to the shared knowledge engine.
Proposal Automation
Feed Nexus AI your knowledge base — case studies, past proposals, technical specs — then submit any RFP PDF. The agent reads every requirement, retrieves relevant context from your KB, drafts a compliant section-by-section response, and flags risk clauses automatically. Output lands as a formatted DOCX in seconds, not hours.
Knowledge Retriever
A persistent, always-on knowledge base for your organisation. Index hundreds of SOPs, policies, handbooks, and technical guides. Any team member can query the library in plain English and receive a cited answer with the exact paragraph and page number. Supports multi-document filtering, conversation history, and confidence scoring.
WhatsApp Auto-Reply
Connect Nexus AI directly to your WhatsApp Business number via the Meta Cloud API. Incoming customer messages are routed through the full RAG pipeline and a grounded, concise reply is sent back — automatically. Ideal for support, compliance FAQs, or internal helpdesks. Configurable top-K retrieval and reply length.
Document Chat Interface
A clean Streamlit-powered chat frontend for interactive document research. Toggle hybrid search on or off, adjust the number of retrieved passages, filter by document, and watch answers stream token-by-token. Expandable citation panels show the exact excerpt used — every claim is auditable.
Engineered for precision.
Agentic Reasoning
A LangGraph state machine orchestrates every query: it retrieves context, grades each passage for relevance, rewrites ambiguous queries, and decides when it has enough information to answer — all without manual intervention.
Hybrid Search
Dual-index architecture combines OpenSearch BM25 keyword matching (exact term recall) with OpenAI or local sentence-transformer embeddings (semantic similarity). Results from both are fused for maximum coverage and precision.
Precise Citations
Every answer includes page numbers, document IDs, and the exact text excerpt that was used. Reviewers can trace any claim back to the source document in one click — critical for compliance and audit workflows.
Multi-Doc Filtering
Query a single document or any subset of your library. Filter by document ID, tag, or metadata before retrieval so the agent never pulls irrelevant content from unrelated files.
Advanced Parsing
Docling handles the layouts that split most parsers: multi-column PDFs, embedded tables, footnotes, and numbered section hierarchies. Chunks carry section-level metadata so retrieval understands document structure.
Confidence Scoring
After retrieval, the agent self-assesses the quality of the context and assigns a High / Medium / Low confidence label to each answer. Low-confidence responses prompt the user to provide more documents before trusting the output.
Built on the tools that serious teams trust.
State-machine graph that controls retrieval loops, query rewriting, document grading, and hallucination guardrails.
BM25 full-text index plus k-NN vector index in a single cluster. Handles both keyword and semantic queries simultaneously.
GPU-accelerated PDF parser that preserves table structure, section hierarchy, and formula blocks — far beyond PyPDF or pdfplumber.
Every agent step — retrieval, grading, LLM call — is traced end-to-end. Debug latency, token cost, and retrieval quality from a single dashboard.
Conversation history, embedding cache, and query result cache stored in Redis. Eliminates redundant LLM calls and keeps P95 latency under 500 ms.
Structured metadata (title, author, tags, ingestion date, chunk count) lives in Postgres. Alembic manages schema migrations safely in production.
All endpoints are fully async, Pydantic-validated, and OpenAPI-documented. The server handles concurrent document ingestion without blocking query traffic.
Run embedding generation on-premise without sending document content to external APIs. Swap to OpenAI embeddings with a single config flag.
A clean, zero-infrastructure frontend for power users. Live sidebar controls, document filtering, streaming output, and collapsible citation panels.
Healthcare, Legal, Finance, HR and Operations Teams.
Nexus AI helps teams reduce document search time, improve answer consistency, and keep decisions traceable with source-backed responses.
- Upload documents and set business context
- Index content for fast hybrid retrieval
- Ask natural-language questions in seconds
- Receive answers with exact page citations
- Audit every response back to the source document
"Nexus AI completely changed our research workflow. We no longer drag our principal engineers into long manual searches. The agentic reasoning provides exact citations immediately."
Frequently Asked Questions
Technical clarifications regarding data sovereignty, model training, and integration.
Do you use our proprietary data to train foundation models?
Absolutely not. We operate under strict tenant isolation. Your uploaded documents are converted into vector embeddings stored in an isolated namespace. We do not use any client data to fine-tune or train the underlying Large Language Models.
How does the system prevent AI hallucinations?
Nexus AI utilises an Agentic RAG architecture built on LangGraph. The LLM is strictly instructed to answer only using the retrieved context from your Knowledge Library. After retrieval, the agent grades each passage for relevance and loops to rewrite the query if the context is insufficient. If the answer does not exist in your documents, the system explicitly states that the information is missing rather than guessing.
Is Nexus AI SOC 2 compliant?
Our entire infrastructure is SOC 2 Type II aligned, utilising AES-256 encryption at rest and TLS 1.3 in transit. We conduct regular penetration testing and vulnerability scanning.
Can I run the embeddings on-premise without sending data to OpenAI?
Yes. The embedding layer is fully configurable. Switch EMBEDDING_PROVIDER to 'local' in your environment config and the system uses Sentence Transformers running on your own hardware. The LangChain-Ollama integration also supports fully local LLM inference, making it possible to run the entire stack air-gapped.
How does the WhatsApp module authenticate incoming webhooks?
Every inbound message from Meta is verified using an HMAC-SHA256 signature computed against your APP_SECRET. Requests with missing or invalid signatures are rejected before the message reaches the RAG pipeline.
What document formats are supported?
The primary parser is Docling, which is optimised for PDF. It handles scanned PDFs (via OCR), native digital PDFs with complex table layouts, multi-column academic papers, and structured reports. Additional format support (DOCX, PPTX, HTML) is available through the LangChain document loaders layer.
Ready to reclaim your research hours?
Spin up a demo workspace in under 60 seconds — no credit card, no infrastructure to provision.