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Veea

Veea is an AI-driven edge infrastructure company founded in 2014. The company develops hardware and software systems that bring secure, governed compute and AI workloads to distributed edge environments, targeting enterprise, industrial, and public-sector deployments where centralized cloud alone falls short. Veea holds over 117 patents and has been recognized by Gartner for its edge computing innovations.

General
CompanyVeea
Founded2014
CEOAllen Salmasi
HeadquartersNew York City, USA
Websiteveea.com
Developer Centerdeveloper.veea.com
GitHubgithub.com/veeainc
TypeAI Edge Infrastructure

Core Products

Lobster Trap

Lobster Trap is a deep prompt inspection (DPI) proxy that sits between AI agents and any OpenAI-compatible LLM backend, enforcing firewall-style policy rules on every prompt and response. It is MIT-licensed and classifies threats using compiled regex patterns in sub-millisecond time without additional LLM calls. It ships as a single static Go binary with no cloud dependency, API keys, or rate limits required.

TerraFabric

TerraFabric is the control plane for governed autonomous systems at the edge. It coordinates distributed deployments as cohesive systems rather than individual devices, handling fleet orchestration, policy enforcement at L2 through L7 network layers, zero-trust security boundaries, controlled rollouts with rollback, and workload lifecycle management.

VeeaONE Platform

VeeaONE is Veea's unified operating model combining orchestration, application lifecycle management, and fleet-wide visibility across edge sites. It integrates compute, connectivity, storage, and cybersecurity in a single platform.

VeeaHub Edge Servers

VeeaHub is Veea's on-site compute hardware, converging networking, security, and AI workloads in a single edge device for enterprise and industrial sites.

SecureConnect

SecureConnect provides zero-trust networking and policy enforcement across distributed operations, enabling secure site-to-site communication without a centralized cloud dependency.

Developer Resources

Lobster Trap is MIT-licensed and available as pre-built static binaries for Linux, Windows, and macOS. It requires no Go toolchain, signups, or external services to run.

Key Features

Deep Prompt Inspection Without LLM Overhead Lobster Trap extracts structured metadata including intent categories, risk scores, credentials, PII, injection patterns, exfiltration signals, target paths, and risky commands using compiled regex patterns. All classification runs in sub-millisecond time with no secondary model calls.

Programmable Policy Engine YAML-based firewall rules use first-match-wins logic with actions including ALLOW, DENY, LOG, HUMAN_REVIEW, QUARANTINE, and RATE_LIMIT. Rules apply bidirectionally to both incoming prompts and outgoing responses.

Declared vs. Detected Intent Inspection Agents can declare intent via _lobstertrap headers. Lobster Trap compares declared intent against detected behavior and surfaces mismatches in audit logs, enabling governance workflows a regulator or security team can read.

Drop-In Deployment Works with any tool using the OpenAI chat completions API, including Ollama, vLLM, llama.cpp, OpenAI, Anthropic, and Gemini via proxy, without application code changes.

Edge-Native Orchestration TerraFabric manages distributed AI workloads across multiple physical sites with controlled rollouts, rollback capabilities, and compliance-grade audit trails under a single governance model.

Use Cases

AI Agent Security and Guardrails Development teams building production AI agents use Lobster Trap to detect prompt injection attempts, enforce access control policies, and generate audit trails that meet enterprise security review requirements.

Enterprise Edge AI Deployment Organizations running AI inference across distributed sites use TerraFabric and VeeaHub to deploy, govern, and update edge workloads with fleet-wide visibility and zero-trust security boundaries.

Red-Team and Compliance Tooling Security teams use Lobster Trap's built-in adversarial test suite (./lobstertrap test), single-prompt debugger (./lobstertrap inspect), and structured JSON audit logs to measure risk reduction and demonstrate regulatory compliance.

Multi-Agent Permission Systems Platform teams use Lobster Trap as a trust layer beneath multi-agent systems, enforcing per-agent permission boundaries and logging every cross-agent interaction for governance review.

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veea AI Technologies Hackathon projects

Discover innovative solutions crafted with veea AI Technologies, developed by our community members during our engaging hackathons.

ATLAS - Enterprise Multi-Agent Governance

ATLAS - Enterprise Multi-Agent Governance

ATLAS is an enterprise multi-agent system where every agentic decision is inspected, signed, and auditable. THE PROBLEM Goldman Sachs CIO said publicly: "We don't know what controls we need for agentic AI." Enterprise LLM agents make decisions affecting databases, APIs, financial records. There is no infrastructure that makes these decisions inspectable, auditable, and compliant. WHAT ATLAS DELIVERS - 29/29 scientific test suite PASS in under 1 second - All 5 sponsors integrated end-to-end (real API calls, not mocked): · Speechmatics for voice transcription · Featherless for open-source model routing (MiniMax-M2.5, DeepSeek-V3.2, Kimi-K2.5, Llama-3.3-70B) · Google Gemini 2.0 Flash for orchestration and synthesis · Vultr for infrastructure layer · Kraken for financial action layer - SOUF AI DPI inline governance: every prompt inspected in 0.079ms avg (well under 1ms ceiling) - Ed25519-signed audit chain with SHA-256 Merkle tamper-evidence - 8 signed records per full pipeline request, chain verified - Isaac Adams (Featherless judge): "confidence is what enterprise AI needs" — ATLAS is that confidence layer ARCHITECTURE 6-layer governed pipeline: Voice → Speechmatics → SOUF AI DPI gate → Gemini orchestrator → Featherless router → Tool executor (Search/Database/Kraken/Vultr) → Ed25519 audit trail → Gemini synthesis. REPRODUCIBILITY git clone https://github.com/SRKRZ23/atlas cd atlas && pip install -r requirements.txt python3 src/test_atlas.py → 29/29 PASS in under 1 second ECOSYSTEM ATLAS is the routing layer of a 4-product AI safety ecosystem: SOUF AI provides DPI, FORGE generates policies, CITADEL evaluates models, ATLAS calls them all. Same Ed25519 audit chain across four products. MIT licensed. Lobster Trap is the floor. ATLAS is the agent governance ceiling. Built solo by Sardor Razikov, Tashkent.

CXGuard - Powered by Lobstertrap

CXGuard - Powered by Lobstertrap

CXGuard is a security gateway and governance dashboard for AI-powered customer support agents. It is designed for companies that want to deploy AI support bots safely without exposing themselves to prompt injection, customer data leakage, refund abuse, secret extraction, policy manipulation, or unsafe automated actions. As more customer experience teams adopt AI agents to answer questions, resolve tickets, process returns, and reduce support costs, a new class of risk appears. Unlike traditional software, AI agents can be manipulated through language. A malicious customer may try to override the agent’s instructions, reveal hidden system prompts, extract private customer information, access internal policies, bypass refund limits, or trick the bot into performing actions it should not take. For enterprises, these failures are not just technical bugs — they can become privacy incidents, financial losses, compliance gaps, and reputational damage. CXGuard solves this by sitting between the customer support interface and the underlying language model. Every support conversation is routed through Lobster Trap, an inline prompt inspection and policy enforcement layer, before the request reaches the LLM. Lobster Trap inspects incoming prompts and outgoing model responses for risky signals such as prompt injection, credential extraction, personally identifiable information requests, sensitive file paths, role impersonation, unsafe commands, external exfiltration, and other suspicious patterns. CXGuard then turns those low-level security signals into an enterprise-ready product experience: clear decisions, risk scores, policy hits, incident details, human-review queues, and audit-ready logs.

Carapace

Carapace

Carapace is the action-layer trust boundary that sits on top of Veea's Lobster Trap. AI agents now run at the edge with authority to take destructive actions on production infrastructure — isolate a spine switch, quarantine a node, migrate VMs across sites. Lobster Trap guards the conversation with deep prompt inspection, but a conversation that looks perfectly clean can still end in isolate(spine-switch-sj-01), and a single poisoned log line can trigger a self-inflicted outage with no clear audit of why. Carapace closes that gap. It sits between the agent's reasoning and its tool execution as a fail-closed policy engine that gates the action itself on declared-versus-detected intent, source provenance (trusted telemetry versus untrusted ingested text), and blast radius. It folds Lobster Trap's conversation-layer verdict into its own rule matrix so the two compose into genuine defense-in-depth — Lobster Trap can only ever make Carapace stricter, never looser. An injection-driven destructive action is escalated to QUARANTINE and never reaches the executor: proven live with real Gemini through the real Lobster Trap binary, blocked at $0 versus a ~$47k/minute outage with no trust layer. Every decision lands in a tamper-evident, hash-chained audit trail a regulator could read. It's the ceiling Veea's "floor, not ceiling" challenge asked for — a drop-in trust layer enterprise security teams will actually sign off on, with a 114-test suite and a live Gemini + Three.js demo.

RackVision AI

RackVision AI

### Problem Global enterprise data centers currently operate with a critical "Visibility Gap." Traditional network management systems monitor logical traffic and software logs but remain completely blind to the physical space. When an onsite technician over-bends a fiber line or miswires a high-density GPU rack, the logical system drops packets, and engineering teams waste hours guessing why. ### Solution RackVision AI bridges this gap as a Physical-to-Logical Digital Twin Platform. We turn real-world visual telemetry into intelligent infrastructure workflows, giving operators full visibility across both the physical data center floor and the network layer. ### How We Built It (Technology Stack) * **Google Gemini Flash (via Gemini API & Google AI Studio):** Powers our Vision-Language pipelines. It acts as our autonomous workspace inspector, processing low-latency live video streams to evaluate hardware device health, track port alignment, and identify physical cable strain. * **Google Gemini Pro:** Drives our "Sketch-to-Topology" simulation engine. It interprets geometric, hand-drawn layout photos from field engineers and instantly converts them into interactive digital twin maps. * **Veea Lobster Trap:** Our mission-critical AI governance layer. Because an infrastructure agent handles production switch logic, we deployed Lobster Trap as a Deep Prompt Inspection proxy firewall. It blocks malicious prompt injections, intercepts unauthorized credential extraction commands, and logs an unalterable, regulator-ready compliance audit trail. ### Track Alignment: Track 3 (Robotics & Simulation) RackVision AI directly addresses Track 3 focus areas by deploying multi-modal Vision-Language models to interpret real-world spatial environments and compiling hand-drawn drafts into scalable, digital twins for industrial IT environments.

Incident Brain: Autonomous AI for Handling Outages

Incident Brain: Autonomous AI for Handling Outages

Incident Brain is an autonomous co-responder for production incidents built with Google Gemini and Veea Lobster Trap. It continuously ingests Slack conversations and terminal or screen activity during live outages, converts them into structured events, and builds a searchable semantic timeline in real time. Instead of acting like a chatbot that waits for prompts, Incident Brain proactively monitors the flow of an incident. Every action, hypothesis, observation, failure, and mitigation attempt is embedded into long-term incident memory using pgvector similarity search. When engineers repeat actions that previously failed in similar incidents, the system immediately warns the team with contextual reasoning grounded in historical outcomes. Google Gemini powers multimodal event extraction, reasoning, embeddings, intervention generation, cascade prediction, and automated post-mortem creation. Veea Lobster Trap provides a governed AI gateway layer for secure and policy-aware model traffic during incident analysis. Incident Brain also predicts cascading failures before they happen. By analyzing the evolving incident narrative, it identifies likely downstream failures, estimates confidence, and recommends mitigation steps while the outage is still unfolding. If the system detects that responders are stuck in a failure loop, it escalates into an autonomous co-responder. It synthesizes the current timeline, warning history, and historical recovery patterns to suggest actionable next steps directly in Slack and the live dashboard. Privacy is built into the architecture. Sensitive terminal data is redacted locally using OCR and Presidio before any structured events are sent to Gemini. Raw screenshots and secrets stay on the engineer’s machine. After resolution, Incident Brain automatically generates a complete post-mortem with timelines, root cause hypotheses, failed attempts, successful mitigations, and follow-up actions.