Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

TriggerWare

TriggerWare is a cloud-based analytics and automation platform built by CalQLogic, a Los Angeles-based software company. The platform addresses a persistent gap in business intelligence: accessing truly live data from multiple sources and acting on it instantly, without expensive ETL/ELT pipelines or data syncing. TriggerWare is designed to serve both technical developers and non-technical business users, offering SQL-based APIs alongside no-code, natural language interfaces.

General
CompanyCalQLogic
Founded2017
HeadquartersLos Angeles, CA, USA
Websitetriggerware.ai
Documentationdocs.triggerware.com
Consoleconsole.triggerware.com
TypeReal-Time Data Platform, AI Agent Infrastructure

Core Products

TriggerWare Platform

The TriggerWare platform connects to internal and external data sources, correlates data across those sources in real time, and converts insights into alerts and AI agent triggers. It is delivered as a web app with REST API access and includes a console for building and managing data workflows. Each user gets a separate server instance, ensuring isolation and customizability.

MCP Integration for Agentic AI

TriggerWare provides an MCP (Model Context Protocol) Server and Client that AI agents can use to pull real-time data on demand or to trigger agent actions when data conditions change. This makes TriggerWare a live data layer for autonomous AI workflows.

DELT (Dynamic Extract Load Transform)

DELT is TriggerWare's patented approach to data retrieval that reads directly from source systems without staging data in a warehouse. This eliminates the latency and cost associated with conventional ETL/ELT pipelines while keeping query results current.

Developer Resources

TriggerWare is accessible via REST APIs for developers who prefer programmatic integration, and via a no-code console for business users. The platform supports MCP for agent tooling and offers SQL-compatible query access under the SQL Over Everything® API.

Key Features

Dynamic Extract Load Transform (DELT) Retrieves data directly from source systems in real time, removing the need for ETL/ELT pipelines or data syncing that introduce lag and cost.

SQL Over Everything A patented API layer that lets developers with SQL knowledge query any connected data source using familiar syntax, regardless of the underlying data format.

No-Code GenAI Connectors Uses generative AI to create data connectors from natural language descriptions, so non-technical users can onboard new data sources without writing code.

Natural Language Query Engine Allows business users to formulate queries by describing what they want in plain language, with results appearing immediately based on current data.

Real-Time Alerts Sends notifications whenever query answers change, keeping decisions based on the most recent state of the data rather than a snapshot.

MCP Server and Client Provides MCP-compatible tooling so AI agents can access live data or trigger workflows as part of autonomous decision loops.

Use Cases

AI Agent Data Layer Teams building autonomous AI agents can connect TriggerWare via MCP to give agents access to live business data, enabling decisions grounded in real-time context rather than static training data.

Business Monitoring and Alerts Analysts and operations teams can set up queries on external and internal data sources and receive alerts when conditions change, such as market signals aligning with sales targets or supplier risk indicators shifting.

Real-Time Application Integration Developers can embed live data queries into business applications using the SQL Over Everything API, removing the need to build and maintain separate data pipelines for each application.

IoT and Event Analytics The platform supports time-stamped and IoT sensor data, making it suitable for use cases that involve monitoring physical systems or high-frequency event streams.

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TriggerWare AI Technologies Hackathon projects

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

PriceGhost: Dynamic Pricing Forensic Exposé

PriceGhost: Dynamic Pricing Forensic Exposé

PriceGhost is a full-stack forensic intelligence platform that detects, measures, and cryptographically proves dynamic geographic pricing discrimination. THE PROBLEM: Corporations silently charge different prices based on your location, device, and browser fingerprint. 78% of consumers report feeling targeted by location-based pricing bias, yet proving it is nearly impossible. HOW IT WORKS: PriceGhost coordinates 10 simultaneous residential proxy scrapes across global coordinates (Mumbai, New York, London, Tokyo, Berlin, Sydney, Lagos, Buenos Aires, Dubai, Singapore) via Bright Data's Web Unlocker API. Each scrape rotates device fingerprints and captures raw HTML payloads. STATISTICAL FORENSICS ENGINE: Four custom mathematical algorithms run natively — Gini Coefficient of Spatial Inequality, Coefficient of Variation, Mann-Whitney U Significance Test (p < 0.05), and GDP Pearson Wealth Correlation — establishing courtroom-ready mathematical proof of pricing discrimination. AI-POWERED PARSING: When standard regex price extraction fails on complex HTML, Featherless AI's hosted Llama-3 model acts as a semantic fallback parser. AI/ML API generates authoritative natural language indictments styled as investigative exposés. COGNITIVE MEMORY: Cognee's semantic graph database indexes every pricing anomaly, enabling live queries against historical precedents to expose long-term corporate discrimination patterns. AUTOMATED ALERTS: TriggerWare webhooks automatically dispatch incident alerts to legal networks when Gini/Pearson indices flag "Severe" exploitation levels. EVIDENCE INTEGRITY: Every scrape result is sealed with SHA-256 cryptographic signatures and timestamp chains, producing immutable evidence packages exportable as courtroom-ready JSON dossiers. BUILT WITH: Next.js 16 (Turbopack), better-sqlite3 (7-table schema with WAL), Recharts composed visualizations, Leaflet dynamic trace maps.

RevDev

RevDev

CaliSignal is an AI SDR and GTM intelligence platform built for California startups. It uses Bright Data live web signals to discover high-fit accounts, detect buying windows, score ICP match, and identify the right stakeholders before competitors reach them. The platform turns fragmented public signals like funding news, hiring activity, job posts, reviews, and web changes into prioritized pipeline, account briefs, outreach drafts, alerts, and action workflows. Instead of making SDRs manually research accounts, CaliSignal gives teams a live revenue cockpit that shows who to contact, why now, what to say, and what action to take next.

PulseSignal: Executive GTM Strategy Engine

PulseSignal: Executive GTM Strategy Engine

PulseSignal is a next-generation Market Intelligence platform built for Go-To-Market (GTM) and Sales teams. The platform solves the problem of 'lagging indicators' by treating unstructured web data—specifically high-velocity hiring and news—as leading indicators of corporate strategy. Our autonomous pipeline executes in three stages: 1. Live Data Intercept: Powered by Bright Data SERP API, we perform targeted crawls of Google Search and career pages. 2. Data Structuring: Google Gemini Flash parses messy HTML into strict JSON, identifying niche skills and business priorities. 3. Executive Synthesis: We implemented a Hybrid Multi-Model Architecture, routing complex reasoning through DeepSeek V4 Flash (via AI/ML API) to generate persona-specific playbooks for Sales, Recruiting, and Investors. Key features include a dynamic 'Manager's Weekly Report' with an Account Prioritization Matrix, an Evidence Ledger for transparency, and a 'Magic, Moment' Gmail integration that takes an AI-drafted email and pre-loads it, directly into the user's browser, ready to send. PulseSignal doesn't just show data; it generates quantifiable ROI.

SignalWindow: GTM Intent Radar

SignalWindow: GTM Intent Radar

GTM teams lose deals because the strongest buying signals never make it into their stack. Churn threads on Reddit, pricing complaints on Hacker News, and switching intent on G2 sit behind bot protection, JavaScript rendering, and rate limits—so RevOps and AEs research manually and still miss the window. SignalWindow is not a chatbot. It is a closed-loop intelligence pipeline built for the Bright Data hackathon. For each watchlist company, Bright Data SERP API discovers what changed (pricing, careers, changelog, community queries). Web Unlocker and Browser API extract blocked pages as markdown with source URLs and timestamps. A deterministic Window Score ranks winnability in Python; AI/ML API only explains why—never inventing the score. Cognee plus Redis remember signals across runs so context compounds. When the score crosses threshold, TriggerWare or Slack delivers an action-ready brief with outreach draft. The Streamlit demo runs end-to-end in demo mode with pre-cached extracts, or live against real Bright Data endpoints. Toggle “Simulate change” to inject a synthetic churn signal, watch the diff fire, score jump, and alert execute on stage—proving Discover → Extract → Score → Reason → Remember → Act in under five minutes. We surface buying intent before it appears in ZoomInfo or Clearbit: evidence-grade, cited, and automated. Bright Data unlocks the web; SignalWindow turns it into GTM action.

Fortify DD

Fortify DD

Fortify DD is an AI-powered autonomous vendor due diligence agent designed to revolutionize third-party risk assessment and continuous compliance monitoring. The platform accepts a company name to orchestrate an advanced multi-node LangGraph pipeline. Leveraging Bright Data's SERP API and Web Unlocker, it gathers live open-web signals, employee reviews, and financial health indicators, while incorporating Bright Data MCP for regulatory, legal, and sanctions screening. By upgrading to version 2.0, the tool shifts from one-shot assessments into an event-driven risk-monitoring system. Fortify DD utilizes Cognee to build persistent vendor knowledge graphs, enabling deep historical comparisons and the detection of risk drift over time. Integrated with TriggerWare, the backend automatically schedules routine compliance checks and fires targeted alert webhooks when a vendor's risk rating changes. A Streamlit-based demo dashboard provides full visibility into real-time pipeline execution, detailed risk briefs across five critical dimensions, historical risk delta reports, and Speechmatics-generated audio alert summaries for high-risk red rating events.

Amber: Catch Gray-Market Diversion

Amber: Catch Gray-Market Diversion

Premium brands lose millions every year to gray-market diversion: a distributor buys cheap in one country and dumps the product in another, undercutting the brand's own market. Today, brand-protection teams hear it from a vendor dashboard, a claim they cannot independently check. Amber turns that gap into evidence. It captures the same product from inside each country on Bright Data's residential network, matches the GTIN, and strips VAT to a net-of-tax floor. A within-country control runs three residential exits per country; when all three agree to the cent, the gap is a controlled experiment, not proxy noise. Every observation is sealed into a cryptographically signed, geo-attributed packet using ed25519 and an RFC 6962 Merkle tree, and anyone can verify it offline with one command. Edit a single byte and verification fails, RED. The architecture is honest by construction. Layer 1 is the deterministic signed spine: no AI ever writes a number into the evidence. Layer 2 is a separate, unsigned advisory that only reads the signed facts, a three-model jury via the AI/ML API, a Cognee temporal memory that shows whether a gap persists, and a TriggerWare workflow that turns a signed catch into an alert. A human draws any legal conclusion. We also gave back: an open pull request to Bright Data's own brightdata-mcp turns a discarded blocked-country error into a first-class signed measurement, closing their issue #104. We say what we do not claim. Requests are dispatched the same instant, not witnessed, and the annual recoverable figure uses the brand's own volume assumption, labeled as one. Every number ships inside a signed packet in the public repo with 324 passing tests, so you can clone it and re-check the proof yourself.