How does Sagy integrate with our existing systems?

Sagy connects directly to the tools your team already uses, Slack, Microsoft Teams, Jira, Confluence, GitHub, and dozens more. Memory is created in the background without changing your workflow.

Is our data secure with Sagy?

Absolutely. We employ enterprise-grade security measures, including full source traceability, role-based access control, and on-premise deployment options. Your memory stays yours with complete data ownership.

How long does it take to set up Sagy?

Most companies are up and running within 24 hours. Sagy begins capturing and organizing knowledge immediately, with memory becoming more valuable over time as it compounds.

How is Sagy different from a knowledge base or wiki?

Traditional knowledge bases require manual documentation and become stale quickly. Sagy is a memory layer that automatically captures knowledge from your existing tools, continuously updates when information changes, and delivers answers with full context and verified sources.

What kind of knowledge does Sagy capture?

Sagy captures decisions, explanations, and know-how as work happens, from conversations, problem-solving moments, code reviews, support tickets, and more. It detects what's important automatically and organizes it where it belongs.

Engineering memory for AI agents

AI agents for engineering teams. That learn how your team works, and get better every day.

Sagy gives your engineering team AI agents that investigate incidents, recover historical context, and execute workflows grounded in your code, your tools, and your team's real engineering knowledge.

Faster investigationsFaster onboardingFewer interruptions

Built for engineering teams of 10–100 · Software teams shipping production systems · Deploys in your existing tools

Built with engineering teams who feel this pain every day.

The Pain

Engineering knowledge lives in 7 tools and 4 people's heads.

The teams shipping the most complex systems struggle the most with knowledge.

Not because they don't write things down, but because the important knowledge never makes it into a doc. It lives in Slack threads, incident retros, PR comments, and the head of the engineer who joined three years ago.

Repeated interruptions

Senior engineers answer the same questions every week.

Slow onboarding

New hires spend weeks rebuilding context that already exists somewhere.

Investigation chaos

Critical debugging knowledge is fragmented across incidents, PRs, tickets, and tribal memory.

Tribal knowledge

The architecture decision from 18 months ago still lives in someone's DMs.

Stale documentation

Runbooks and wikis drift away from how your systems actually work.

Docs are never enough.Engineering knowledge is dynamic. Your tooling should be too.

The Solution

Turn engineering knowledge into reusable Skills.

Sagy continuously captures engineering context from the tools your team already uses, and structures it into Skills, reusable procedures your team and your AI agents can execute on demand.

Capture

Conversations, incidents, debugging sessions, architecture decisions, onboarding guidance, and procedures, pulled from where they already happen.

Connect

Operational memory of how your engineering organization actually works. Always current. Grounded in real activity, not stale wikis.

Execute

Repeated engineering workflows become Skills, runnable on demand, by your team or by AI agents working alongside them.

Engineering knowledge in. Runnable Skills out.

See it in action

Built For Engineering

Skills built for how engineering teams actually work.

Three Skills that engineering teams use every day. More ship every month.

Incident Investigation

The pain

Every incident pulls a senior engineer away from their work to reconstruct context manually. Past similar incidents. Recent deploys. Related PRs. Affected services. Historical fixes. It takes 30+ minutes before the real debugging begins.

What Sagy does

When an alert fires, Sagy gathers:

Prior incidents on the same service
Related Slack discussions
Recent PRs that touched the affected code
Impacted downstream services
Historical fixes and their owners
Relevant runbooks

The outcome

Investigations start with context, not with reconstruction. Senior engineers stop being the bottleneck.

Engineering Q&A

The pain

Senior engineers answer the same questions in Slack every week. "Where does service X log to?" "Who owns the payments service?" "Why don't we use Kafka here?" Each question costs 15 minutes and breaks deep work.

What Sagy does

Sagy answers inside Slack or Teams using your company's engineering history, past discussions, architecture decisions, ownership, and conventions. When Sagy doesn't know, it routes to the right person and learns from the answer.

The outcome

Senior engineers protect their flow. Institutional knowledge stops being a single point of failure.

Engineering Onboarding

The pain

New engineers rely on senior engineers for context that should be self-serve. Ramp-up takes 8–12 weeks. Every new hire taxes the rest of the team.

What Sagy does

Sagy guides new engineers through:

Architecture and service ownership
Team conventions and code style
Recent incidents and how they were handled
The "why" behind major decisions
Their first issues, with full context

The outcome

Faster ramp-up. Less interruption. New engineers contribute meaningfully in weeks, not quarters.

How It Works

Built around your engineering workflow.

Step 1

Connect

Connect your tools

Slack, Jira, GitHub, docs, meetings.

No new workflow to learn.

Step 2

Capture

Capture engineering context

Discussions, incidents, decisions, fixes, and operational patterns are continuously connected automatically.

Context stays current in the background.

Step 3

Build

Create operational Skills

Repeated engineering workflows become reusable Skills:

incident investigations
onboarding
escalations
debugging
engineering Q&A

Skills, not static docs.

Step 4

Run

AI agents help engineers directly

AI agents use your company's operational knowledge to:

investigate incidents
recover historical context
answer repeated questions
onboard engineers
assist debugging

Grounded in how your engineering team actually works.

Differentiation

Not another chatbot. Not another wiki.

Search tools

Return documents and links.

Chatbots

Generate generic answers, disconnected from how your team works.

Wikis

Become outdated and disconnected from real engineering workflows.

Sagy

Captures engineering knowledge from where it lives, and turns it into Skills your team and agents can run.

Integrations

Works where engineering knowledge already lives.

Slack · Jira · GitHub · Confluence · Notion · Teams · SharePoint · Google Drive

Outcomes

What changes when Sagy is in place.

Faster onboarding

New engineers reach productivity in weeks, not quarters.

Faster investigations

Context is ready when the incident fires, not 30 minutes after.

Fewer interruptions

Senior engineers stop being the lookup service.

Knowledge that persists

The architectural decisions, debugging patterns, and tribal context survive turnover.

What's Next

Every engineering organization needs operational memory.

AI systems are getting capable enough to do real engineering work.

But engineering knowledge stays fragmented across tools, incidents, tickets, conversations, and people. The teams that benefit most from AI won't be the ones with the best models. They'll be the ones with structured engineering memory, and reusable Skills grounded in how their teams actually work.

Sagy builds that foundation.

Capture how your engineering team works. Turn it into Skills your team and AI agents can run.

Designed to deploy alongside your existing tools, no rip-and-replace.

FAQ

Visit our help center to get in touch.
We're super responsive.

AI agents for engineering teams. That learn how your team works, and get better every day.

Sagy gives your engineering team AI agents that investigate incidents, recover historical context, and execute workflows grounded in your code, your tools, and your team's real engineering knowledge.

Faster investigationsFaster onboardingFewer interruptions

Built for engineering teams of 10–100 · Software teams shipping production systems · Deploys in your existing tools

Every engineering organization needs operational memory.

AI systems are getting capable enough to do real engineering work.

Sagy builds that foundation.