Folder-native indexing
Point Vevesh at the directories you already work in. PDFs, Office files, drawings, and code are scanned, chunked, and kept ready — no re-upload circus.
Private preview · engineering teams only
Your technical knowledge, instantly usable by AI
Vevesh indexes specs, drawings, reports, and code from the folders you already use — then gives your team a private, citation-aware assistant that understands engineering context, not just keywords.
Your folders stay local
Citation-grounded answers
Team knowledge bases
vevesh://workspace/turbine-standards
You asked
What are the fatigue load combination requirements for an IEC Class IIA site with 50-year design life?
Vevesh answer
GroundedFor Class IIA, your indexed IEC 61400-1 section 7.4.2 requires DLC 1.2 and 6.4 combinations with the site-specific turbulence class applied. Your foundation report (Rev C) already flags the governing combination as My at tower base under DLC 6.1...
iec61400-1.pdf · p.84
foundation-revC.docx · §4.2
4
Extraction engines
Express → Premium
Hybrid
Search stack
Vector + keyword BM25
100%
Citation traceable
Every answer sourced
Edge
Global scale path
When teams need it
Local-first indexing No forced cloud upload Team KB billing Ghost & open modes Multi-provider AI Desktop native
Features
Built for engineers who live in folders, not chat windows
Vevesh is not another generic document chatbot. It is an indexing and retrieval stack designed around how technical teams actually store knowledge.
Deep document extraction
Express, Structure, Flow, Premium — four engines. Local or server. You choose per knowledge base.
Shared knowledge bases
Hosted, billed corpora your whole team queries from chat — ghost or open citation modes.
Private by design
Your source folders stay yours. Metadata on infrastructure you control.
Engineering RAG
Multi-hop retrieval, standards-aware routing, answers that respect real project structure.
Desktop + web
Native desktop for heavy folders. Web when you need a lighter entry.
Extraction
Four engines. One knowledge base.
You pick the depth.
Not every document deserves the same treatment. Vevesh lets you choose local or server extraction per knowledge base — from lightning-fast text pulls to premium vision for the messiest scans.
Fastest
Express
Plain text layer extraction. Ideal for digital PDFs, Word docs, and clean reports where speed matters.
Best for: Daily specs, code, markdown
Layout-aware
Structure
Preserves headings, tables, and document hierarchy. Cloudflare toMarkdown for complex Office and PDF layouts.
Best for: Technical reports, tables
Vision OCR
Flow
Workers AI vision for scanned images and messy documents. Falls back intelligently on unsupported formats.
Best for: Scanned drawings, photos
Highest fidelity
Premium
Higher-quality vision model for the toughest scans — handwritten margins, low-contrast plots, legacy archives.
Best for: Legacy archives, field notes
Local or server — your call
Extract on your machine for full control, or push raw files to the edge pipeline when you want server-side processing without touching your source folder.
Local extract
Server pipeline
How it works
From messy folders to trustworthy answers in three moves
The workflow mirrors how you already organize work — Vevesh adds the AI layer without forcing a new filing system.
- 01
Pick a folder
Choose any project directory — specs, reports, CAD exports, code. No forced “import everything to our cloud” step.
- 02
Index & extract
Vevesh scans, hashes, and extracts with the engine you select. Local on your machine, or server-side via the edge pipeline.
- 03
Ask in context
Chat with full project awareness — citations, cross-document reasoning, and optional shared knowledge bases for the team.
Platform
Serious infra behind a calm interface
Hybrid stack: local indexing on your machine, managed retrieval where it helps, edge workers for scale-sensitive paths. This marketing site lives on its own Cloudflare account — separate from customer data.
- ✓ Vector + keyword hybrid search
- ✓ Team knowledge bases with Stripe billing
- ✓ Multi-provider AI routing with cost controls
- ✓ Desktop-native filesystem access
Your machine
Desktop app · local index
Edge
CF Workers
Server
Self-host / Dell
Team KB storage
R2 · Vectorize · D1 shards
Why Vevesh
Not another document chatbot
Generic AI tools treat your files as attachments. Vevesh treats your project structure as the product.
Generic AI
Vevesh
Source of truth
Upload copies to vendor cloud
Your folder stays authoritative
Technical citations
Often missing or vague
Page-level, file-level grounding
Extraction depth
One-size-fits-all parsing
4 engines · local or server
Team sharing
Separate product tier
Built-in knowledge bases + billing
Engineering context
Generic chat persona
Multi-hop RAG for standards & projects
Data plane isolation
Shared multi-tenant pool
Separate CF accounts · self-host option
Trust & security
Serious teams need more than a shiny chat box
Vevesh is designed for engineers who cannot afford hallucinated load cases or mystery data residency. We optimize for traceability, isolation, and control — not engagement metrics.
Private preview
Design partner program
Self-host
Optional on-prem path
EU-ready
Configurable deployment
Your data, your boundary
Source folders remain on your machine or infrastructure. Index metadata and team KB settings live in Postgres — never scattered across anonymous SaaS uploads.
Isolated data planes
Customer knowledge bases run on dedicated Cloudflare accounts with separate workers, R2, and Vectorize — not co-mingled with marketing or admin traffic.
Transparent citations
Every grounded answer links back to indexed files and pages. Ghost mode hides KB existence from probing; open mode makes citations first-class.
Predictable billing
Team knowledge bases use Stripe subscriptions. Server extraction and embed usage are metered per engine — no surprise token black boxes.
Engineering-grade stack
Hybrid vector + BM25 retrieval, multi-provider AI routing, desktop-native filesystem access, and optional self-hosted server deployment.
Built in the open with partners
Vevesh is in private preview with design partners in wind, civil, and software engineering. We ship against real folder workflows, not demo PDFs.
FAQ
Questions serious teams ask first
Does Vevesh upload my entire project folder to the cloud?
No. Your source folder stays where it is. Vevesh scans and indexes content locally by default. Team knowledge bases sync extracted chunks and metadata to managed storage — raw files only when you explicitly choose server extraction mode.
How is this different from ChatGPT with file upload?
Upload tools treat files as one-off attachments. Vevesh maintains a persistent, searchable index tied to your folder structure — with hybrid retrieval, engineering RAG routing, and page-level citations on every grounded answer.
Can we self-host?
Yes. Vevesh supports a self-hosted server path for teams that need on-prem control. Managed cloud components (edge workers, team KB storage) are optional layers — not mandatory for every workflow.
What file types are supported?
PDFs, Office documents (Word, Excel, PowerPoint), plain text, code, and common engineering exports. Extraction depth depends on the engine you select — from fast text-layer pulls to premium vision for scanned archives.
How do team knowledge bases work?
An owner promotes a folder into a hosted, billed corpus. Members see it as a sector in chat. Owners control disclosure mode (ghost vs open citations), extraction settings, and Stripe-backed subscription billing.
Is Vevesh ready for production?
Vevesh is in active private preview with design partners. Core indexing, chat, and team KB flows are live; we are hardening server extraction pipelines and onboarding teams incrementally.
Ready to put your project knowledge to work?
Vevesh is in active development with early design partners. Reach out to join the preview or discuss deployment for your team.
No spam. No sales deck unless you ask. We onboard engineering teams who have real folders to index.