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
Company	Veea
Founded	2014
CEO	Allen Salmasi
Headquarters	New York City, USA
Website	veea.com
Developer Center	developer.veea.com
GitHub	github.com/veeainc
Type	AI 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.

Helpful Links

Developer Center - official platform documentation and SDK reference
Support Center - technical support and knowledge base
GitHub (veeainc) - open-source repos including Lobster Trap
Lobster Trap repo - MIT-licensed deep prompt inspection proxy
Lobster Trap README - quickstart and full policy reference
TerraFabric - edge orchestration platform overview
Blog - product updates and engineering articles

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.

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

YAYA — Wheel Strategy Trading Agent

YAYA is a fully autonomous options trading agent built on Alpaca's paper trading infrastructure. It implements the wheel strategy — selling cash-secured puts on liquid underlyings, then transitioning to covered calls if assigned — without any manual intervention during market hours. The agent runs a continuous decision loop: it checks market hours via Alpaca's clock endpoint, fetches the live option chain using the Alpaca CLI, selects the nearest-expiry put with a real bid within a defined strike distance, passes the candidate through deterministic risk gates (position size cap, buying power check), and submits a limit sell order through the Alpaca Trading API. Every decision — submitted, blocked, or errored — is logged to a CSV with timestamp, symbol, strike, bid, and reason. Risk gates are a first-class citizen of the design, not an afterthought. The agent will not trade if the notional exposure exceeds the position cap or if buying power is insufficient — and the log proves it. Built solo in Python. Uses Alpaca CLI for chain exploration and quote fetching; alpaca-py SDK for order execution and account management. No external dependencies beyond the Alpaca stack.

Phalanx

The Problem: Modern enterprise LLM pipelines are blindly trusting external web data, leaving them vulnerable to sophisticated prompt injections and context manipulation. Current security solutions rely on recursive LLM-based filtering, which is both latency-heavy and computationally expensive. The Solution: Phalanx AI acts as an immutable air gap. By leveraging the Band SDK, we orchestrate a decentralized swarm of specialized agents. Our pipeline follows a rigid interrogation order: Ingestion (Bright data SERP): Bypasses 403 blocks via rotating proxies. Deterministic (Regex): Filters known threats instantly. Statistical (Math): Our custom Stats Agent uses Shannon Entropy and cryptanalytic formulas to identify obfuscated/encoded payloads in sub-milliseconds without burning API tokens. Semantic (Gemini): Deep behavioral analysis intercepts logic traps. Compaction (Llama-70B): Only safe, cleared data is distilled into a compact JSON format for your core model. Extra Protection: Phalanx integrates Lobstertrap, an MIT-licensed Go-based edge inspection engine, ensuring structural integrity at the lowest level. Phalanx is model-agnostic, scalable via Google Cloud Run, and engineered to enforce deterministic security over unpredictable generative AI.

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 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.