Private preview · zero retention · military-grade privacy

Super private. Extremely accurate. Engineering AI that respects your standards.

Zero retention. Local and cloud. Phone, desktop, and browser. Five-plus turbo engines. Verification loops. Evidence reporting. Global vetted sector knowledge bases. Vevesh indexes the folders you already trust — then answers with citations your lead can sign.

Request access All platform features
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

Grounded

For 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

0

Retention training

Zero retention on customer corpora

5+

Turbo engines

Express → Premium extraction

100%

Citation grounded

Evidence-ready answers

3

Surfaces

Phone · desktop · browser

Military-grade privacy Local + cloud hybrid Verification loops Conflict resolution Global sector KBs Consultant mode Team RBAC Offline & online RAG Live sync review Tool calling at scale PhD research engine File mesh visualization

Super private · zero retention · military-grade boundaries

Zero retention privacy 5+ turbo engines Military-grade boundaries Local · cloud · mobile · desktop Standards-grade citations Verification loops

Privacy manifesto

Zero retention.
Your knowledge is not our product.

Consumer AI treats your uploads as fuel. Vevesh treats them as classified engineering material — isolated planes, explicit boundaries, and architecture that assumes your worst-case leak is unacceptable.

0

Training retention

Customer corpora are not pooled for model training

3

Isolated CF accounts

Marketing · ops workers · user KB data — separated

∞

Local-first option

Index on your machine; cloud only when you choose

RBAC

Team privileges

Customizable access per project and knowledge base

Read full privacy & platform features

The problem

Your knowledge is already organized.
Your AI tools pretend it is not.

Decades of specs, reports, and calculations sit in directories your team trusts. Then someone asks you to drag those files into a chat window — again — and trust a summary with no page number.

  • 1 Upload limits and session resets erase context between reviews.
  • 2 Wiki migrations fail because engineers never live in wikis — they live in folders.
  • 3 Enterprise search connects SaaS apps but not your mixed PDF/CAD/code project tree.

The Vevesh answer

Index where the work already lives.

Persistent folder indexes. Four extraction engines. Citations your lead engineer can verify before sign-off. Team knowledge bases when the corpus belongs to everyone — not just the laptop that indexed it first.

See how we compare

Platform capabilities

Everything serious teams asked for — built in

Privacy, turbo engines, verification loops, team RBAC — not a slide deck. Explore the full stack on the features page.

Privacy & trust

Super private. Zero retention. Military-grade boundaries.

Vevesh is architected so customer corpora stay in isolated data planes — not pooled into shared model fine-tuning. Query traffic is processed for the answer, not harvested for product analytics.

All privacy features →

Core

Zero retention by design

Enterprise

Military-grade privacy posture

Hybrid

Local AND cloud — you choose

Multi-surface

Phone · computer · browser

5+ engines · Verification loops · Evidence reporting · File mesh · Consultant mode · Offline + online RAG · Tool calling
01

Zero retention by design

Your prompts and source files are not a training dataset.

02

Military-grade privacy posture

Boundary-first engineering for teams who cannot leak load cases.

03

Respect for norms & standards

IEC, ISO, Eurocode — cited by clause, not paraphrased from memory.

04

State-of-the-art verification loop

Retrieve → reason → cite → verify. Not one-shot guessing.

05

Project management workflow

Indexes follow how projects actually move — not how SaaS wishes they did.

06

Version control & revision awareness

Content hashing means only what changed gets re-processed.

See all 28+ platform features

Extraction

Four engines. One knowledge base. You pick the depth.

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.

Local extract Server pipeline

Use cases

Where Vevesh earns its keep on day one

Design partners use Vevesh wherever project knowledge is folder-shaped, revision-heavy, and too valuable to re-upload every Monday.

Wind & renewable energy

Standards libraries that actually answer

Index IEC extracts, foundation reports, and site assessments together. Ask load-combination questions with clause-level citations — not paraphrased guesses.

IEC 61400Load casesSite reports

Civil & structural

Rev-controlled report corpora

Rev A through Rev E live in one folder tree. Vevesh hashes incrementally so only changed documents re-index — critical when submissions land weekly.

EurocodeRev controlPDF + Word

Software & R&D

Code + docs in one query surface

Mix README files, API specs, and architecture PDFs. Hybrid retrieval finds the implementation detail and the design rationale in one grounded answer.

MonoreposAPI specsHybrid RAG

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.

  1. 01

    Pick a folder

    Choose any project directory — specs, reports, CAD exports, code. No forced “import everything to our cloud” step.

  2. 02

    Index & extract

    Vevesh scans, hashes, and extracts with the engine you select. Local on your machine, or server-side via the edge pipeline.

  3. 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

Consumer AI

ChatGPT

General AI · ephemeral file upload

Engineering fit score 58/100

Pros

  • + Instant to try — no setup
  • + Broad general knowledge
  • + Fast for one-off questions
  • + Large model selection

Cons

  • − No persistent folder index
  • − Upload limits & session-bound files
  • − Weak page-level citations
  • − Not built for engineering RAG
  • − Data leaves your boundary by default

Enterprise suite

Microsoft Copilot

M365-embedded assistant

Engineering fit score 64/100

Pros

  • + Deep Microsoft 365 integration
  • + Enterprise SSO & admin controls
  • + Familiar for Office-heavy teams
  • + SharePoint connectivity

Cons

  • − Locked to Microsoft ecosystem
  • − Limited CAD / engineering tool paths
  • − Opaque retrieval across complex folders
  • − Per-seat cost at scale
  • − Not folder-native for mixed repos

Knowledge wiki

Notion AI

Wiki-first Q&A inside Notion

Engineering fit score 61/100

Pros

  • + Clean wiki UX
  • + Team pages in one place
  • + Simple Q&A over Notion content
  • + Good for product/docs teams

Cons

  • − Must migrate content into Notion
  • − No native PDF/CAD pipeline
  • − Engineering folders stay outside
  • − No hybrid vector + BM25 stack
  • − Citation depth varies by page type

Enterprise search

Glean

Enterprise workplace search

Engineering fit score 72/100

Pros

  • + Strong SaaS connector catalog
  • + Enterprise sales & support
  • + Cross-app search unified UI
  • + Mature admin tooling

Cons

  • − Six-figure contracts typical
  • − Long procurement cycles
  • − Not optimized for local project folders
  • − Engineering extraction is generic
  • − Overkill for technical SMB teams

Engineering AI

Vevesh

Folder-native engineering RAG

Best fit
Engineering fit score 93/100

Pros

  • + Indexes folders you already use
  • + 4 extraction engines · local or server
  • + Page-level citations on every answer
  • + Team KBs with ghost / open modes
  • + Hybrid vector + BM25 retrieval
  • + Self-host path + isolated CF data plane
  • + Desktop-native for heavy engineering repos

Cons

  • − Private preview — not self-serve yet
  • − Requires design-partner onboarding
  • − Best for teams with real technical folders
Full comparison matrix

Design partners

Teams who cannot afford vague AI

"We stopped re-uploading the same IEC PDF every sprint. The folder index just stays current — that alone saved hours."

Lead structural engineer

Wind energy · design partner

"Citation paths my reviewers can click beat any summary Copilot gave us on SharePoint-only content."

Technical manager

Civil infrastructure · preview

"Ghost mode for our internal standards KB was the policy knob our legal team actually understood."

Head of digital engineering

Multi-discipline consultancy · preview

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.

Integrations & stack

Plays nice with serious infrastructure

Desktop app

Native folder access

Cloudflare Workers

Edge extract & index

Vectorize + R2

Team KB storage

Postgres

Settings & billing

Stripe

Team subscriptions

Multi-provider AI

Routed via gateway

Blog

Engineering AI — explained properly

Architecture notes, standards traceability, and pipeline decisions from the team building folder-native RAG.

View all articles
PrivacySecurity

Zero-retention privacy architecture for engineering AI

A deep technical guide to zero-retention design, military-grade data boundaries, and why engineering teams cannot treat AI like consumer chat — from the architects of Vevesh.

Read article
TeamKnowledge Base

Team knowledge bases — sharing engineering corpora without losing control

Promote a folder into a billed, shared knowledge base with ghost or open citation modes. How Vevesh handles membership, Stripe billing, and retrieval policy.

Read article
ExtractionKnowledge Base

Local vs server extraction — choosing the right engine for your knowledge base

Express, Structure, Flow, or Premium? Local desktop extraction vs server pipeline— a practical guide for engineering document sets.

Read article

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.

hello@vevesh.com Open PartWind app

No spam. No sales deck unless you ask. We onboard engineering teams who have real folders to index.