Built for lawyers, engineers, scientists, researchers, and consultants. Point Vevesh at the folders you already trust — it indexes them, routes the question across the models you already use, and answers with the page and clause your reviewer can open.
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...
Complete context
Vevesh connects project directories, standards libraries, and team knowledge bases — then routes across OpenAI, Claude, Gemini, Groq, and Kimi with page-level citations your reviewers can sign.
Built for professional teams worldwide
Live
Folder indexes
Only changed files re-process
5+
Turbo engines
Express → Premium extraction
100%
Citation grounded
Evidence-ready answers
3
Surfaces
Phone · desktop · browser
Enterprise trust
Access logs, role graphs, and deployment choices that line up with ISO, SOC 2, GDPR, HIPAA, and state-cloud reviews — so legal and healthcare buyers are not starting from a blank page.
ISO 42001
AI management systems
HIPAA
Healthcare data boundaries
TX-RAMP Level 2
State cloud security
SOC 2 Type II
Operational controls
ISO 27001
Information security
GDPR
EU data protection
Certifications and formal audit reports roll out with enterprise design partners. Trust center →
Platform
Anthropic Claude routing
Groq accelerated inference
DeepSeek tool calling
Alibaba Qwen lanes
Multi-step orchestration
Citation-grounded answers
Persistent project indexes
5+ extraction engines
Standards traceability
Vector + BM25 retrieval
Self-host when policy requires it
RBAC & ghost mode
Contract & case corpora
Standards & project folders
Protocols & lab notes
Literature & datasets
Client evidence packs
Audit-ready citations
Multi-format archives
Evidence-driven sign-off
Super private · zero retention · military-grade boundaries
Privacy & trust
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 data planes
Marketing · ops · 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
The problem
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.
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 comparePage-level evidence
Review-ready answers
Policy-controlled KBs
Pack for sign-off
Vector + BM25
5+ turbo pipelines
Role-based access
Local + cloud hybrid
Platform capabilities
Verification loops, revision-aware sync, extraction engines, and a file mesh — each job has its own module. Privacy is above; this section is how the work actually runs.
Access & residency
Index on the laptop, extract on the server, or mix both. Membership and ghost-or-open citations are per knowledge base, so a shared standards library does not automatically leak into a client deliverable.
All privacy features →Core
Not a training dataset
Prompts and source files are processed to answer — not harvested to improve a shared model.
Enterprise
Boundary-first posture
Load cases and client trees stay behind explicit planes — not a shared tenancy slogan.
Hybrid
Local AND cloud — you choose
Desktop-native indexing or edge pipeline — same folder authority.
Multi-surface
Phone · computer · browser
Field engineers on mobile. Leads on desktop. Reviewers in the browser.
Respect for norms & standards
IEC, ISO, Eurocode — cited by clause, not paraphrased from memory.
State-of-the-art verification loop
Retrieve → reason → cite → verify. Not one-shot guessing.
Project management workflow
Indexes follow how projects actually move — not how SaaS wishes they did.
Version control & revision awareness
Content hashing means only what changed gets re-processed.
Turbo processing · 5+ engines
Express · Structure · Flow · Premium — plus local/server paths.
Multiple document types
PDF · Office · code · drawings · media — one index, many extractors.
AI model ecosystem
Vevesh is not locked to one vendor. Our allocator routes engineering RAG, extraction, verification, and massive tool-calling loops across OpenAI, Anthropic Claude, Google Gemini, Groq, Moonshot AI Kimi, DeepSeek, xAI Grok, Alibaba Qwen, MiniMax, and GLM — with metered lanes and model-native wire formats, not a single-model chat wrapper.
OpenAI
GPT-5.x · GPT-5.4 mini/nano
Structured outputs, vision, and reasoning with native tool-calling wire formats.
Anthropic
Claude Opus · Sonnet · Haiku · Fable
Claude-native tool loops and long-context engineering RAG orchestration.
Gemini 3.1 Pro · 2.5 Pro · Flash
Gemini multimodal extraction, summarization, and vision-heavy document paths.
Groq
GPT-OSS 120B · Whisper STT
Groq-accelerated speech-to-text and ultra-low-latency inference.
Moonshot AI
Kimi K2.5 · K2.6 · K2.7 Code
Massive tool-calling range aligned with Moonshot Kimi code and reasoning models.
DeepSeek
V4 Pro · V4 Flash · R1
Deep reasoning and cost-efficient technical synthesis.
xAI
Grok 4.x · Grok Build
Grok-family models for fast reasoning and structured engineering outputs.
Alibaba Qwen
Qwen3.5 · Qwen3.7 Plus · Qwen3 VL
Qwen vision-language and plus-tier models for multilingual engineering corpora.
MiniMax
MiniMax M3
Alternative reasoning lane for long-form report generation.
Zhipu GLM
GLM-5.2
GLM-family routes for multilingual standards and annex parsing.
LangGraph-orchestrated loops retrieve, compute, compare, and format across your indexed corpora. Moonshot AI Kimi K2.x and K2.7 Code lanes are fully aligned with structured tool calling — the same class of multi-step workflows teams expect from frontier code assistants, applied to engineering evidence and standards.
Cursor AI, GitHub Copilot, and Claude.ai excel at in-editor code. Vevesh goes beyond generic AI: deep folder-native knowledge, citation grounding, and answers close to what a field expert would give — with evidence your lead can sign.
Cursor AI
Complements in-IDE coding — Vevesh owns folder-native RAG and audit citations.
GitHub Copilot
Code completion inside repos; Vevesh indexes specs, reports, and standards beside them.
Microsoft Copilot
M365-embedded Q&A; Vevesh covers mixed engineering folders Copilot does not index natively.
Perplexity
Web search answers; Vevesh grounds on your project files with page-level evidence.
Claude.ai
Generic chat from broad training; Vevesh grounds Claude routing on your indexed corpus for expert-level, cited answers.
ChatGPT
Session uploads and generic replies; Vevesh replaces that with persistent field depth and page-level evidence.
Extraction
Local or server extraction per knowledge base — from lightning-fast text pulls to premium vision for the messiest scans.
Plain text layer extraction. Ideal for digital PDFs, Word docs, and clean reports where speed matters.
Best for: Daily specs, code, markdown
Preserves headings, tables, and document hierarchy. Layout-aware conversion for complex Office and PDF files.
Best for: Technical reports, tables
Vision OCR for scanned images and messy documents. Falls back intelligently on unsupported formats.
Best for: Scanned drawings, photos
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.
Use cases
Design partners use Vevesh wherever project knowledge is folder-shaped, revision-heavy, and too valuable to re-upload every Monday.
Wind & renewable energy
Index IEC extracts, foundation reports, and site assessments together. Ask load-combination questions with clause-level citations — not paraphrased guesses.
Civil & structural
Rev A through Rev E live in one folder tree. Vevesh hashes incrementally so only changed documents re-index — critical when submissions land weekly.
Software & R&D
Mix README files, API specs, and architecture PDFs. Hybrid retrieval finds the implementation detail and the design rationale in one grounded answer.
Contract & case corpora
Standards & project folders
Protocols & lab notes
Literature & datasets
Client evidence packs
Audit-ready citations
Multi-format archives
Evidence-driven sign-off
How it works
The workflow mirrors how you already organize work — Vevesh adds the AI layer without forcing a new filing system.
Choose any project directory — specs, reports, CAD exports, code. No forced “import everything to our cloud” step.
Vevesh scans, hashes, and extracts with the engine you select. Local on your machine, or server-side via the edge pipeline.
Chat with full project awareness — citations, cross-document reasoning, and optional shared knowledge bases for the team.
Platform
Local index on the machine that holds the folder. Edge workers for heavy extract. Hosted retrieval only when a team corpus needs to be shared. Three hops, one query surface.
Your machine
Desktop app · local index
Edge
Edge workers
Server
Self-host / Dell
Team KB storage
Object store · vector index · metadata
Consumer AI
General AI · ephemeral file upload
Pros
Cons
Enterprise suite
M365-embedded assistant
Pros
Cons
Knowledge wiki
Wiki-first Q&A inside Notion
Pros
Cons
Enterprise search
Enterprise workplace search
Pros
Cons
Engineering AI
Folder-native engineering RAG
Pros
Cons
Customer stories
Trust & security
After the privacy stance is set, buyers ask who hosts, who pays, who can invite, and what a reviewer sees. This is that layer.
Production ready
Desktop · web · team KBs
Self-host
Optional on-prem path
EU-ready
Configurable deployment
Say where the index lives: this laptop, your server, or hosted team storage. Settings sit in Postgres — not sprinkled through random upload buckets.
Hide a knowledge base from probes, or put file paths on every answer. Consultants pick the mode per client, not as a global on/off.
Role graphs bust cached access when someone leaves a project. An intern does not inherit the full standards library by accident.
Team knowledge bases use Stripe subscriptions. Server extraction and embed usage are metered per engine — no surprise token black boxes.
Filesystem access on the machine that holds the folder. Edge workers when laptops should not run vision. Self-host when procurement says so.
Design partners are lawyers, engineers, scientists, and consultants working revision-heavy trees — not a dozen demo PDFs.
Integrations & stack
Postgres billing, Stripe team KBs, edge extraction — and multi-model routing across OpenAI, Anthropic, Google Gemini, Groq, Moonshot AI, DeepSeek, and xAI Grok.
Desktop app
Native folder access
Edge pipeline
Extract & index at scale
Vector + object store
Team KB storage
Postgres
Settings & billing
Stripe
Team subscriptions
LangGraph
Tool-calling orchestration
Blog
Architecture notes, standards traceability, and pipeline decisions from the team building folder-native RAG.
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
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
Express, Structure, Flow, or Premium? Local desktop extraction vs server pipeline— a practical guide for engineering document sets.
Read articleFAQ
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.
ChatGPT treats uploads as one-off attachments and answers from generic model knowledge. Vevesh indexes your entire field-specific corpus, keeps it live, and grounds every answer in your documents with page-level citations — closer to what a domain expert would say, not a generic summary.
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.
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.
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.
Vevesh routes engineering RAG, extraction, and tool-calling loops across OpenAI GPT, Anthropic Claude, Google Gemini, Groq, Moonshot AI Kimi, DeepSeek, xAI Grok, Alibaba Qwen, MiniMax, and GLM. Admins pick verified model lanes — not a single locked vendor.
Cursor AI and Copilot excel at in-editor code completion. Vevesh complements them with folder-native indexes across PDFs, Office, standards libraries, and project archives — with page-level citations, team KB RBAC, verification loops, and evidence packs for design review.
Yes. Groq powers accelerated speech-to-text (Whisper) and low-latency inference lanes. Moonshot AI Kimi K2.x and K2.7 Code models support massive tool-calling workflows for multi-step engineering tasks — retrieve, compute, compare, and format with LangGraph orchestration.
For professional work, yes. ChatGPT and Claude.ai are strong general assistants — but their answers stay generic unless you re-upload context every session. Vevesh goes further: it builds deep knowledge from your folders, routes across frontier models, and grounds responses in your evidence. The result is field-specific precision close to what an expert in your domain would say — so it can replace generic chat for serious legal, engineering, scientific, and research work. Casual questions? ChatGPT still works. Defensible answers from your corpus? That is Vevesh.
Open the app at ai.vevesh.com, connect your project folders, and start indexing. Team knowledge bases and advanced extraction settings are available from your workspace admin panel.
Connect the project tree you already file. Ask the next design-review question with the clause attached.
Index the folders you already use. Answers with page-level evidence — built for professional review.
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