# SignalsAI — Full Site Content for AI Assistants > AI solved coding. SignalsAI solves delivery. Delivery Intelligence for the AI era. > Website: https://orgsignals.com > Support: support@orgsignals.com --- ## Home Page — https://orgsignals.com/ **Headline:** AI Solved Coding. SignalsAI Solves Delivery. **Tagline:** Delivery Intelligence for the AI era. **Value proposition:** Coding moves at AI speed; delivery still runs on coordination overhead. SignalsAI is the engineering platform that manages your entire delivery loop — from planning to execution to feedback — so your team spends time building, not coordinating. **The 19-hour coordination gap:** AI made coding faster, but teams still lose nearly a full workday per engineer to coordination: planning handoffs, status chasing, risk fire drills, and retros that arrive too late. SignalsAI uses AI to automate planning, execution, and the feedback loop so coordination shrinks while shipping accelerates. **Delivery eras (same scope, three comparison eras on the homepage):** - **Legacy Process** — Manual planning, execution (coding + PM), and post-deploy work. - **AI Coding Era (~1.2×)** — Faster code, but coordination, risk management, and PM overhead remain the bottleneck. - **Signals AI Era (~4×)** — AI planning, agentic PM and risk, AI feedback; dramatically less coordination time for the same scope. **Intelligence layer stack (how SignalsAI works):** 1. **Integration** — Plugs into Jira, GitHub, Slack, Microsoft Teams, GitLab, Linear, Azure DevOps, Jenkins, Asana, Notion, Confluence, GitHub Copilot, and more — no rip-and-replace. 2. **Observability** — Delivery visibility, workflow observability, execution monitoring, team health (where most competitors stop). 3. **Organizational context & memory** — Delivery context graph linking teams, repos, tickets, meetings, and AI usage so every agent decision has full delivery context (the differentiator). 4. **Agentic delivery intelligence** — Agentic project coordination, risk detection, predictive insights, delivery intelligence, autonomous follow-through. **End-to-end delivery loop (one loop, no manual steps):** - **Planning** — Capacity planning from real velocity, intelligent task routing, sprint commitment guardrails, cross-tool sprint intake. - **Execution** — Sprint runs itself; risks caught days early; auto-resolves and escalates with full context; PR-level AI vs. human attribution on every pull request; large AI PR detection; mid-sprint risk radar. - **Feedback loop** — Committed vs. delivered tracking, auto-drafted sprint retros, delivery outcomes tied to AI usage, patterns feed the next planning cycle. **Key capabilities highlighted on the homepage:** PR-Level AI Attribution, Capacity Planning, Intelligent Task Routing, Delivery Genie, Feedback Loop Automation, Cycle Time Correlation, Risk Radar, Signals Brain, Large AI PR Detection, Human Edit on AI Code, Committed vs Delivered, Agentic Coordination. **Integrations shown on the homepage:** GitHub, GitLab, Bitbucket, Azure DevOps, Jira, Linear, Notion, Confluence, Slack, Cursor, GitHub Copilot, Claude Code, Atlassian, Jenkins, Travis CI, Microsoft Teams, AWS CodeCommit, ClickUp, Asana, CI/CD. **CTA:** Less coordination. More shipping. Book a 30-minute demo at https://orgsignals.com/demos --- ## What Is SignalsAI? SignalsAI is an engineering delivery platform for the AI era. AI coding tools made writing code faster; delivery still runs on coordination overhead — planning handoffs, status chasing, risk fire drills, and late retros. SignalsAI automates the full delivery loop (planning, execution, feedback) with PR-level AI attribution, organizational context memory, and agentic coordination across the tools your team already uses. **The core shift:** Competitors stop at dashboards. SignalsAI builds connective memory across your stack and runs the delivery loop — so AI speed turns into shipped work, not coordination debt. Traditional PM tools (Jira, Linear, Asana) are passive record systems. SignalsAI is the intelligence layer and orchestration layer: capacity-aware planning, execution monitoring with PR-level AI vs. human metrics, risk radar, committed-vs-delivered tracking, and retros that write themselves from real data. --- ## The Problems SignalsAI Solves 1. **The coordination gap** — Teams lose ~19 hours per engineer per week to planning handoffs, status chasing, and fire drills while coding gets faster. 2. **AI coding without delivery gains** — AI makes code faster (~1.2×) but coordination, risk, and PM overhead cap real throughput. 3. **Dashboards without context** — Visibility tools show metrics; they don't connect teams, repos, tickets, meetings, and AI usage into actionable delivery context. 4. **Sprint-level AI guesses** — Without PR-level attribution, leaders can't see AI %, human edits on AI code, cycle time, or change fail rate per pull request. 5. **Risks surface too late** — Deadline conflicts, blocked chains, and velocity drops appear in standups after damage is done. 6. **Retros and reports arrive too late** — Committed vs. delivered rollups and sprint patterns should feed the next sprint automatically, not after manual assembly. --- ## How It Works SignalsAI runs one continuous delivery loop across three phases: **Planning** — Capacity planning from real velocity and availability; intelligent task routing by skill, ownership, and dependencies; sprint commitment guardrails before kickoff; unified intake from Jira, Linear, and Notion. **Execution** — Progress tracked from real activity; mid-sprint risk radar for deadline conflicts and blocked chains; auto-resolves and escalates to the right person with full context; PR-level AI vs. human attribution correlated to cycle time and change fail rate; large AI PR detection for outsized AI-generated changes. **Feedback loop** — Committed vs. delivered every sprint; auto-drafted retros from delivery data; delivery outcomes tied to AI adoption (not vanity percentages); insights flow into the next planning cycle. **Under the hood:** Four layers — integration with systems of record, observability, organizational context & memory (delivery context graph), and agentic delivery intelligence (live agents for coordination, risk, and follow-through). --- ## Who Benefits - **Engineering Managers** — No more Slack nudges, manual stand-up prep, or spreadsheet-based reporting. SignalsAI monitors progress, flags blockers, and escalates automatically. - **Engineers** — Tasks arrive with full codebase context so there's nothing to guess. Agent prompts are pre-built and ready to paste into Cursor, Copilot, or Claude. - **Heads of Engineering** — Delivery predictability without micromanagement. Compliance, risk, and performance visibility across the entire org in one place. - **CXOs / Product Leaders** — Automated reports and real-time delivery health without waiting for someone to compile a deck. --- ## Product Capabilities SignalsAI is organized into four capability suites: --- ### 1. AI Transformation The AI Transformation suite closes the "context gap" that causes coding agents to hallucinate and generate code that breaks existing patterns. **Why coding agents hallucinate:** Most coding agents fail because they only see the file you're working on. They lack the architectural decisions made six months ago, the PRD requirements written yesterday, and the related logic hidden in a different repository. Developers end up spending more time fixing AI-generated code than writing their own — turning a productivity tool into a productivity tax. --- **Agent-Ready Context & Orchestration** (https://orgsignals.com/features/agent-readiness) SignalsAI's Context Engine generates a "Master Prompt" by combining your PRD, git history, cross-repo dependencies, fragile code warnings, and team conventions into a single, structured, paste-ready prompt for your coding agent. Instead of: > "Write a login page." Your agent receives: > "Write a login page using our Auth0 wrapper, following styling in /styles/theme.css, and handling the 2FA edge case found in last week's retro." This results in production-ready code on the first attempt. Key capabilities: - **PRD-to-Prompt Mapping** — Automatically breaks down unstructured product requirements into precise technical instructions. - **Org-Context Injection** — Indexes your entire codebase and documentation so AI output follows your specific architectural, naming, and linting standards. - **Agent Action Plans** — Replaces single vague tickets with step-by-step technical plans, reducing the chance of an agent looping on under-specified tasks. - **Risk-Aware Prompting** — Automatically adds warnings when an agent is working in a fragile or battle-scarred area of the codebase. - **Cross-Repo Intelligence** — Identifies dependencies across frontends and APIs to provide a unified context map, eliminating isolated silos. Outcome: 10x improvement in agent accuracy, 60% reduction in AI rework time, 40% fewer AI-introduced regressions. --- **AI vs. Human Impact** (https://orgsignals.com/features/ai-vs-human) Prove where AI tools are actually accelerating your team and where human expertise still leads. Compare velocity and code quality between AI-generated and human-written code at the PR and commit level. Measured benchmarks by AI adoption level: | Adoption Level | AI Code % | Cycle Time | Coding Time | PR Revisions | |---|---|---|---|---| | High (102 users) | 68% | 1.4 days | 5.2 hours | 1.8 | | Medium (30 users) | 34% | 2.9 days | 9.7 hours | 2.6 | | Low (15 users) | 8% | 4.5 days | 16.1 hours | 3.9 | Example commit-level insight: A reporting module refactor with 81% AI involvement took 0.8 days and 2.1 hours of coding time. An auth flow fix with 12% AI involvement required 3.1 days and 11.2 hours of coding time. Features: week-over-week adoption trends, team-level benchmarking, identification of power users, industry comparison. --- **Potential Savings** (https://orgsignals.com/features/potential-savings) Surfaces every wasted hour across your engineering org — blocked states, redundant processes, QA delays — and quantifies exactly how much time you'd get back by fixing each one. Shows 870+ hours of potential monthly savings, broken down by opportunity type: time savings (41%), delivery risks (29%), and system issues (29%), with a trend of realized vs. potential savings over time. --- ### 2. Operational Excellence --- **Delivery Genie** (https://orgsignals.com/features/delivery-genie) A proactive alerting engine that monitors SLA thresholds across every team, surfaces breaches the moment they occur, and escalates automatically until someone acts. Set your SLA thresholds once — Review Time, Cycle Time, and more — with P0–P3 priority levels. Delivery Genie then: - Tracks 95+ breach types per deployment cycle - Fires P0 alerts within 5 minutes of a breach - Escalates through three levels: Owner → Team Lead → Management - Ensures 100% breach visibility — nothing closes without acknowledgment --- **Committed vs. Delivered** (https://orgsignals.com/features/committed-vs-delivered) Connect your Jira (or any PM tool) and instantly know how reliably your teams ship what they promise. Tracks ticket counts, story points, quality, and hygiene from real sprint data. - Average delivery rate tracked: 75% within a sprint - 100% ticket traceability — every commitment tracked to completion - Supports 5+ PM tools: Jira, Linear, GitHub Issues, and more - Real-time sync — sprint data always current --- **PM Analytics** (https://orgsignals.com/features/pm-analytics) Deep-dive metrics on ticket health, estimation accuracy, and process effectiveness. Surfaces the invisible project management patterns — scope creep, chronic over-commitment, hygiene failures — that compound into delivery problems. --- **Automated Sprint Retros** (https://orgsignals.com/features/sprint-retros) Stop running the same retro conversation from memory. Signals reads your sprint data from Jira and generates a complete, data-backed retrospective the moment a sprint closes. - Zero manual prep time — retros generated automatically at sprint close - 100% data-backed — every insight tied to actual ticket outcomes - Covers every team and project simultaneously - Delivered instantly to Slack, Teams, or email Each retro surfaces what went well (delivery rate, quality, workflow clarity) and what needs attention (hygiene issues, scope creep, over-commitment patterns), with actionable recommendations. --- ### 3. Engineering Health --- **360° Visibility** (https://orgsignals.com/features/visibility) One unified score for every layer of your engineering org — from individual contributors to entire departments. No dashboards to build. No spreadsheets to maintain. Connect your VCS and you're live. The Overall Score is a composite of four dimensions: - **Speed** — How quickly work moves through the development pipeline (cycle time, PR merge time, review response time). Formula: weighted 25%. - **Quality** — Code quality indicators including review coverage, rework rate, test coverage. Formula: weighted 15%. - **Throughput** — Volume and consistency of work completed. Formula: weighted 40%. - **Collaboration** — Team coordination and knowledge sharing patterns. Formula: weighted 20%. Overall Score formula: (25% × Speed) + (40% × Throughput) + (15% × Quality) + (20% × Collaboration) Benchmarks: Elite teams > 85/100; Needs Focus < 60/100 Visibility levels: org-wide, per-team, and per individual contributor. Real-time updates — no manual refreshes. --- **DORA Metrics** (https://orgsignals.com/features/dora-metrics) Automated tracking of the four standard DORA engineering metrics: - **Deployment Frequency** — How often code is deployed to production - **Lead Time for Changes** — Time from first commit to production deployment - **Change Failure Rate** — Percentage of deployments causing service degradation - **Mean Time to Recovery (MTTR)** — How quickly the team restores service after an incident --- **Workflow Analytics** (https://orgsignals.com/features/workflow-analytics) Identifies bottlenecks in PR review flow, ticket lifecycle, and sprint ceremonies. Pinpoints exactly where your delivery pipeline slows down and provides specific, actionable recommendations to fix it. --- ### 4. Automated Governance --- **Compliance Scores** (https://orgsignals.com/features/compliance-scores) Set the rules once. Signals enforces them automatically — scoring every team against your process standards, continuously, without manual auditing. - 100% automated — no spreadsheets or manual audits - Per-team granularity — score every team independently - Custom thresholds — your standards, your rules - Real-time score updates — always reflects the latest activity Org-level Compliance Score and per-team breakdown shows compliant teams vs. teams that need focus, with period-over-period trends. --- **Proactive Risk Detection** (https://orgsignals.com/features/risk-detection) Stop reacting to delays. Start preventing them with AI-driven early warnings that surface bottlenecks, technical debt, and delivery risk — days before they become a problem. SignalsAI monitors Git, Jira, and docs 24/7. The four risk signal types: - **Code Review Deadlocks** — A critical PR sits unreviewed; senior engineers become a single point of failure before anyone notices. - **Scope Creep** — A ticket that started as "Small" now has 50+ comments and 10 commits — detected and flagged early. - **Idle Time** — Work that has stalled without a blocker being recorded. - **Fragile Zone Warnings** — Changes touching historically unstable areas of the codebase. Critical alerts go straight to Slack — zero dashboard fatigue. Warnings surface days before deadlines are missed. --- **Automated Reports** (https://orgsignals.com/features/automated-reports) Scheduled, end-to-end visibility reports for stakeholders — generated automatically. No more manually stitching together data for the next leadership review. Configure once: report name, percentile benchmark, time period, frequency (weekly/monthly), delivery day and time, and recipients. Reports are delivered on schedule to engineering leads, product ops, and executive stakeholders — based on actual data, not manual compilation. --- ## Engineering Metrics Reference ### Overall Score Formula: (25% × Speed) + (40% × Throughput) + (15% × Quality) + (20% × Collaboration) Elite teams: > 85/100 | Needs Focus: < 60/100 ### DORA Metrics - Deployment Frequency - Lead Time for Changes - Change Failure Rate - Mean Time to Recovery (MTTR) ### AI Impact Metrics - AI Tool Adoption Score — % of engineers actively using AI coding tools, tracked week over week - AI vs. Human Code Comparison — Cycle time, coding time, and PR revision delta - Potential Savings — Hours/cost savings achievable with broader adoption ### Sprint / Jira Metrics - Sprint Commitment Accuracy — Committed vs. delivered ticket and story point ratio - Ticket Cycle Time — Creation to completion - Estimation Accuracy - Scope Creep Rate — Mid-sprint additions as % of sprint total - Ticket Hygiene Score — Quality and completeness of ticket data --- ## Integrations **Source Control & Code Review** - GitHub, GitLab, Bitbucket, Azure DevOps, AWS CodeCommit **Project Management** - Jira, Linear, GitHub Issues, ClickUp, Asana **AI Coding Tools** - GitHub Copilot, Cursor, Amazon Q, Claude Code (Anthropic) **CI/CD** - Jenkins, GitHub Actions, CircleCI, Travis CI, Azure Pipelines, and generic CI/CD **Communication & Collaboration** - Slack, Microsoft Teams --- ## Security & Compliance - **ISO 27001 certified** — Comprehensive data protection - **SOC 2 Type II compliant** — Independently verified security controls - **Read-only metadata access** — No source code is stored or transmitted - **Enterprise-grade security** — Role-based access, audit logs, and data residency options --- ## Documentation Available at https://orgsignals.com/docs - **Metrics Definitions** — Full definitions for every metric with formulas and benchmarks - **Onboarding — Introduction** — Getting started with SignalsAI - **GitHub Integration** — Connecting your GitHub organization - **Bitbucket Integration** — Connecting Bitbucket repositories - **Microsoft Teams Integration** — Team notifications and digests - **CI/CD Integration** — Connecting your build and deploy pipelines - **Jira Integration** — Syncing Jira projects and sprints - **Cursor Integration** — Tracking and measuring Cursor AI usage - **Amazon Q Integration** — Tracking Amazon Q developer usage - **Azure DevOps Integration** — Connecting ADO repositories and pipelines - **Slack Integration** — Automated notifications and SLA alerts - **AWS CodeCommit Integration** — Connecting CodeCommit repositories - **ClickUp Integration** — Syncing ClickUp tasks and projects --- ## Frequently Asked Questions **What is SignalsAI?** SignalsAI is an engineering delivery platform for the AI era. It automates planning, execution, and the feedback loop across your existing tools — with PR-level AI attribution, organizational context memory, risk radar, and agentic coordination. Homepage positioning: AI solved coding; SignalsAI solves delivery. **How is SignalsAI different from Jira or Asana?** Traditional PM tools are passive record systems for tickets and status. SignalsAI is the intelligence layer: capacity-aware planning, execution that runs from real activity, PR-level AI vs. human metrics (not sprint-level guesses), mid-sprint risk radar, committed-vs-delivered tracking, and retros that draft themselves — plus a delivery context graph competitors lack. **What is the "context gap" and why does it matter for AI-assisted development?** The context gap is why coding agents hallucinate. An agent that receives "Write a login page" has no knowledge of your existing auth wrapper, your styling conventions, or the 2FA edge case your team fixed last sprint. SignalsAI's Context Engine injects all of that into a structured Master Prompt automatically, resulting in production-ready code on the first attempt instead of code that requires hours of human fixing. **What measurable results do teams see?** Teams using SignalsAI see a 10x improvement in agent accuracy, 60% reduction in AI rework time, 40% fewer AI-introduced regressions, and an average delivery rate improvement of 30–40%. High AI adopters (68% AI code) average 1.4-day cycle times vs. 4.5 days for low adopters (8% AI code). **Does SignalsAI replace my PM tool?** SignalsAI works alongside your existing PM tools (Jira, Linear, etc.) and enriches them. It keeps your data in sync, enforces process standards, and augments your workflow with AI-powered automation — it does not require you to abandon your existing tool stack. **Is SignalsAI secure for enterprise use?** Yes. SignalsAI is ISO 27001 certified and SOC 2 Type II compliant. It only accesses read-only metadata from your development tools. No source code is stored or transmitted. **What data does SignalsAI access?** Read-only metadata from development tools: PR metadata, commit metadata, Jira ticket data, CI/CD run logs. SignalsAI does not store your source code. **How long does setup take?** Approximately 30 minutes to connect your integrations and see your first insights. **How does SignalsAI predict risk before it becomes a problem?** SignalsAI monitors Git, Jira, and documentation continuously. It detects signals like stalled PRs, scope creep, idle work, and fragile zone changes — and surfaces alerts days before they would appear in a standup. Critical alerts go directly to Slack without requiring anyone to check a dashboard. **Who can benefit from SignalsAI?** Engineering managers get automated follow-ups and compliance monitoring. Engineers get context-rich tasks and agent prompts that eliminate guesswork. Heads of Engineering get org-wide delivery visibility without micromanaging. CXOs get automated reports on delivery health, AI adoption, and risk — without waiting for someone to compile them. **How can I get support?** Email support@orgsignals.com or visit https://orgsignals.com/support. --- ## Comparisons hub https://orgsignals.com/comparisons — Single index page listing every SignalsAI vs. competitor article (DX, Faros AI, Jellyfish, LinearB, minware, Swarmia) plus feature and pricing comparison tables, with one sentence of context per link. Use this hub for crawl discovery and internal linking. ## What is SignalsAI? (standalone) https://orgsignals.com/what-is-orgsignals — Answers brand and search variants (OrgSignals, Signals AI, SignalsAI): who the product is for, what it does, how it differs from Jira/Linear/ClickUp and from DORA-only tools, founders (Gokul Palanisamy and Raghu Mangaraju), trial and pricing pointers. ## Solution: Reduce context switching https://orgsignals.com/solutions/reduce-context-switching — Product landing angle for reducing engineer context switching across Slack, Jira, GitHub, and Linear without replacing your PM tool; includes FAQ-oriented positioning. ## Engineering ROI calculator (SEO URL) https://orgsignals.com/tools/engineering-roi-calculator — Same ROI calculator experience as /roi-calculator; meta and canonical target engineering leaders quantifying context-switching and workflow waste (alias route for shareable SEO URL). ## Blog — context switching, culture, tools, alerts (2026) - https://orgsignals.com/blog/cost-of-context-switching-engineering — Research (40% / recovery time framing), dollar cost model, PM tool irony, SignalsAI’s intelligence layer, internal links to ROI calculator and solution page. - https://orgsignals.com/blog/high-performing-engineering-cultures — Five structural traits: flow, visible state without surveillance, topology, outcomes, systems over heroics; links to scaling culture post. - https://orgsignals.com/blog/build-strong-engineering-culture-at-scale — Playbook for documenting norms, async, visibility, topology, health metrics, and stage-specific guidance. - https://orgsignals.com/blog/best-tools-reduce-developer-context-switching — Jira vs Linear vs ClickUp vs Asana vs GitHub Projects for minimizing switching; “see also” links to SignalsAI vs DX, Jellyfish, LinearB, minware, Swarmia, feature table, pricing table, and /comparisons hub. - https://orgsignals.com/blog/scheduled-reports-automated-alerts-engineering — Which scheduled digests and alerts matter (sprint health, PR lag, deploy frequency, interrupt load, code quality); alert anti-patterns; SignalsAI routing philosophy. - https://orgsignals.com/blog/proactive-workflow-alerts-reduced-delivery-delays — Case study: threshold-based workflow alerts on onboarding stages (In Progress, Review, UAT, Sandbox); p75 time-in-state improvements of 63–84%; hidden QA bottleneck surfaced after obvious stages improved; lessons on dashboards vs real-time intervention. ## ROI Calculator https://orgsignals.com/roi-calculator — Estimate cost and time savings based on your team size, average engineer salary, and current AI adoption level. (See also /tools/engineering-roi-calculator for the same tool.) --- ## Benchmarks https://orgsignals.com/benchmarks — Engineering performance benchmarks across industries and team sizes, including AI-assisted vs. traditional delivery comparisons. --- ## Engineering Delivery https://orgsignals.com/engineering-delivery — Full pipeline visibility connecting every stage from PRD to production, including delivery health, team performance, and historical trend data. --- ## Blog Recent articles at https://orgsignals.com/blog: - **Engineering Time Leak Audit** — How to identify and fix hidden time leaks draining your engineering team's productivity. - **The Hidden Cost of Context Switching in Engineering** — The true delivery cost of frequent context switching for engineers. - **Why DORA Metrics Miss Engineering Productivity** — The gaps in DORA metrics and what else you need to measure for true engineering health. - **The Art of Code Review: Bottleneck or Accelerator?** — How code reviews can either block or boost your team's delivery velocity. - **Building an Engineering Culture That Scales** — Practices for creating an engineering culture that maintains quality and speed as teams grow. - **Engineering Manager's Guide to Technical Debt** — How engineering managers can track, communicate, and systematically reduce technical debt. - **Why AI Isn't Moving the Needle in Enterprise Engineering** — Why AI coding tools fail to deliver expected productivity gains in enterprise engineering teams. - **The Real Story: AI Coding Tools Are Not Enough** — Why AI coding tools alone don't solve engineering productivity problems. - **The Most Expensive Work Nobody Tracks** — The invisible, high-cost engineering work that never shows up in metrics. - **Engineers Hate Wasting Time, Not Documentation** — Why engineers resist documentation and how to make it valuable. - **The Engineering Manager Reality Nobody Talks About** — Honest look at the hidden challenges engineering managers face daily. - **Why CXOs Don't Trust Engineering Dashboards** — The trust gap between engineering metrics and executive decision-making. - **Predictability Doesn't Come from Velocity** — Why chasing velocity metrics fails to improve software delivery predictability. - **Teams Are Busy But Not Moving** — How engineering teams can be fully occupied yet make no real progress. - **What Happens When You Remove 60% of Engineering Work** — Insights from stripping away low-value work to focus on what drives delivery. - **Your Engineers Didn't Write Most of That Code** — The growing reality of AI-generated code and what it means for engineering ownership. - **Your Next Missed Deadline Was Visible Three Weeks Ago** — How predictive signals surface delivery risk long before a deadline is missed. --- ## Contact & Getting Started - **Request a Demo:** https://orgsignals.com/demos - **Support:** https://orgsignals.com/support | support@orgsignals.com - **Documentation:** https://orgsignals.com/docs - **FAQs:** https://orgsignals.com/faqs - **Privacy Policy:** https://orgsignals.com/privacy-policy - **Terms & Conditions:** https://orgsignals.com/terms-and-conditions