From Alert Overload to Actionable Intelligence.
SignalManager AI uses your favorite AI to collect signals from Sentry, GitHub, Datadog, and more — correlate them across sources, prioritize by impact, and generate tickets with full context. Less noise. More clarity. No tool-hopping.
Connects to tools you already use
How It Works
Four steps from raw signal to actionable ticket
Collect
Connectors pull signals from Sentry, GitHub, Datadog, NVD, and more via webhooks or polling. Your team connects their tools once. You see signals flowing in from day one — no manual data gathering.
Store
Signals land in PostgreSQL with structured metadata. No vector DB — just smart SQL and full-text search.
Analyze
AI correlates signals across sources, identifies patterns, and prioritizes by severity and impact — so your team works on what matters, not what is loudest.
Act
Actionable tickets are created in Jira, Linear, or GitHub Issues with full context and remediation steps. No one has to ask “who is handling this?”
PostgreSQL + Smart SQL. No vector DB needed.
While others add complexity with vector databases and embeddings, SignalManager uses PostgreSQL’s built-in full-text search, JSONB, and smart SQL queries to correlate signals. Simpler to deploy, easier to debug, and you already know how it works.
Use Your AI of Choice
No vendor lock-in. Bring any OpenAI-compatible model or run locally.
Everything You Need to Manage Signals
Six core capabilities that turn your dev tool noise into clarity
Signal Pipeline
Ingest signals from any source via webhooks, polling, or the Connector SDK. Normalize into a unified schema.
AI Analysis Engine
Bring your own AI model — OpenAI, Anthropic, or local LLMs. AI scores severity, suggests fixes, and writes ticket descriptions.
Cross-Source Correlation
Connect a Sentry error to a CVE to a GitHub PR. See the full picture across your entire toolchain.
Ticket Generation
Auto-create tickets in Jira, Linear, or GitHub Issues with full context, severity, and remediation steps.
Connector SDK
Build custom connectors for any signal source. TypeScript SDK with hot-reload and built-in testing.
Rules Engine
Define routing rules, severity overrides, and auto-assignment. YAML-based, version-controlled, and auditable.
Built for Your Entire Engineering Org
Whether you write code or manage the team that does
For Developers
- Stop context-switching between Sentry, GitHub, and Datadog
- Get tickets with full context — stack trace, CVE, affected endpoints
- Build custom connectors with the TypeScript SDK
- Self-host with Docker Compose in under 5 minutes
For Engineering Managers
- See signal health trends across teams at a glance
- Know release readiness before approving deploys
- Identify risk hotspots in the stack before they become incidents
- Understand team workload distribution without asking for status updates
For Project Managers / TPMs
- Automated ticket creation lands issues in your backlog — no manual entry
- Sprint planning informed by real signal data, not guesses
- Status updates that write themselves from correlated signals
- Backlog prioritization driven by AI severity scoring
Use Cases
Real workflows that save your team hours every week
Intelligent Signal Prioritization
It’s Monday standup. Your team reports 3 hours lost manually reviewing weekend Sentry errors. A P1 customer-facing bug sat untouched. With SignalManager, that never happens. Sentry fires 200 errors overnight. Instead of manually reviewing each one, SignalManager groups them by root cause, scores severity with AI, and creates a single prioritized work item with full context.
- Groups 200 errors into 3 root causes
- AI scores severity: P1, P2, or P3
- Creates Jira ticket with stack trace and fix suggestion
Continuous Dependency Audit
Your security team flags a CVE during quarterly audit. You need to know which repos are affected, now. SignalManager already has the answer. SignalManager monitors NVD and npm advisories, cross-references with your dependencies, and creates tickets for vulnerable packages before they hit production.
- Monitors NVD + npm advisories in real-time
- Matches CVEs to your actual dependencies
- Auto-creates upgrade tickets with safe version info
Release Readiness Check
You are about to approve v2.4.0 for production. CI is green, but is it really safe? Instead of pinging three team leads, you check one readiness score. Before you ship, SignalManager aggregates open errors, unresolved CVEs, and test coverage signals into a release readiness score. Ship with confidence.
- Aggregates signals from errors, CVEs, and CI
- Generates a release readiness score
- Blocks or warns before risky deploys
Incident Correlation
A customer Slack message drops: “Payments are failing.” Your on-call engineer is switching between four tools. With SignalManager, the incident timeline was already built. When an incident hits, SignalManager links the Datadog alert to the Sentry error to the GitHub deploy. Your on-call engineer gets one ticket with the full timeline.
- Correlates alerts across Datadog, Sentry, and GitHub
- Builds a timeline of events leading to the incident
- Creates an incident ticket with root cause analysis
Be among the first to try SignalManager AI.
We’re building the signal intelligence platform that dev teams deserve. Join the waitlist to get early access, shape the product, and lock in founding member pricing.
Simple, Credit-Based Pricing
Start free. Scale as your signal volume grows.
Free
$0/mo
500 credits/mo
- 3 team members
- Basic connectors
- Email support
Team
$79/mo
5,000 credits/mo
- All connectors
- 15 team members
- Email support
Growth
$199/mo
15,000 credits/mo
- Unlimited members
- Smart model routing
- Priority + Slack
Enterprise $750+/mo
Unlimited credits, Intelligence Network, SSO, RBAC, dedicated CSM & custom SLA.
Frequently Asked Questions
Common questions about SignalManager
What is a “signal”?
A signal is any event or data point from your developer toolchain — a Sentry error, a GitHub PR, a Datadog alert, an NVD advisory, an npm audit warning. SignalManager normalizes them into a unified schema so they can be correlated and acted on. For example, a Sentry error, a Datadog CPU spike, and a GitHub config change that all happen within the same 20-minute window might be one incident — SignalManager would correlate those into a single timeline.
How are credits calculated?
One credit = one AI analysis operation. Ingesting signals is free. Credits are consumed when AI analyzes, correlates, or generates tickets from signals. Self-hosted users have no credit limits — you pay your AI provider directly.
Can I self-host SignalManager?
Yes. The Free plan includes a self-hosted option via Docker Compose. You need PostgreSQL and an AI API key (OpenAI, Anthropic, or any OpenAI-compatible API). Setup takes under 5 minutes.
Which AI models are supported?
SignalManager works with any OpenAI-compatible API: OpenAI (GPT-4o, GPT-4), Anthropic (Claude), local models via Ollama or vLLM, Azure OpenAI, and more. You bring your own API key.
Why PostgreSQL instead of a vector database?
Signal data is structured (source, severity, timestamp, metadata). PostgreSQL’s JSONB, full-text search, and lateral joins are more than sufficient for correlation. No need for the complexity and operational overhead of a vector DB.
Ready to Turn Signals Into Action?
Join the waitlist to get early access and founding member pricing. No credit card required.