Team Rebase
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After interviewing 200+ Engineering leaders over the past 10+ months, we kept hearing the same frustration expressed in different ways:
"We have Datadog, PagerDuty, GitHub, Jira, and five other tools. Each one tells me something, but none of them tell me why we keep having incidents in the same services."
"I can't answer basic questions: Which architectural decisions are slowing us down? Where should I invest to reduce incidents? Which teams need help?"
"I'm flying blind with $500K in monitoring spend."
The pattern was clear: engineering organizations are drowning in tools but starving for insight. They have all the data but none of the intelligence.
This is why we built Rebase, and why we're defining a new category: Engineering Intelligence Platforms.
The Intelligence Gap We're Solving
The problem isn't that engineering leaders lack data. It's that their tools don't talk to each other.
GitHub knows about code changes. Datadog knows about infrastructure health. PagerDuty knows about incidents. But none of them know how these relate to each other. When something breaks, engineering teams spend 4+ hours in war rooms manually correlating: "What deployed? What changed in infrastructure? Which services are involved?"
The data exists. The synthesis requires human effort.
This is what we call Engineering Intelligence: synthesizing signals across your entire software delivery lifecycle (code, infrastructure, deployments, incidents, and team dynamics) to surface patterns, predict risks, and support better decisions.
Unlike observability (which answers "what's happening right now?") or analytics (which answers "what happened?"), Engineering Intelligence answers: "Why does this keep happening?" and "What should we do about it?"
The defining characteristic is cross-domain correlation. Rebase connects data across tools that don't naturally talk to each other:
Code quality metrics from GitHub with production incident patterns from PagerDuty
Deployment frequency from CI/CD with infrastructure capacity from AWS CloudWatch
Team velocity from Jira with code review bottlenecks and architectural complexity
This correlation reveals systemic issues that single-domain tools can't see. A code review tool might flag a risky code pattern, but only Engineering Intelligence can tell you that this exact pattern caused three production incidents in the past 60 days, the team that wrote it has a 4x higher rollback rate than other teams, and infrastructure is at the same utilization level as last month's outage.
This is the intelligence gap we're solving with Rebase.
Why This Category Exists Now
Three things converged to make Engineering Intelligence possible:
Tool proliferation created fragmentation. The average engineering org now uses 10-15 tools across the SDLC. Each captures valuable signals, but they operate in silos. Engineering teams spend hours manually connecting dots across GitHub, CI/CD, observability, and incident management. The coordination tax is massive.
AI made correlation technically feasible. Modern LLMs can parse heterogeneous data (structured logs, unstructured code, time-series metrics, incident reports) and identify patterns across domains. What previously required months of custom data engineering now happens through intelligent agents that understand context across different data types.
Engineering effectiveness became a board priority. As software becomes central to every business, boards ask: "Why are we getting outages?" "How does our engineering effectiveness compare to peers?" "What's our return on $50M in engineering spend?" Traditional metrics (velocity, sprint points, uptime %) don't answer these questions.
We saw this convergence and realized: there's a fundamental category missing.
What Engineering Intelligence Is NOT
Before we explain how Rebase works, let's clarify what Engineering Intelligence isn't. We get confused with adjacent categories constantly.
Not observability. Observability platforms (Datadog, New Relic) excel at real-time monitoring: "Is my service healthy right now?" They provide deep visibility into infrastructure and application performance. But observability is reactive. It tells you what's happening, not why patterns emerge or how to prevent future issues. Observability is a critical input to Engineering Intelligence, not a replacement for it.
Not Internal Developer Portals. IDPs (Port, Backstage, Cortex) catalog services, ownership, and dependencies. They answer: "What services exist? Who owns them?" This is valuable metadata, but IDPs are largely static catalogs. They don't analyze patterns across code quality, deployment risk, and incident history to predict which services will cause problems. Think of IDPs as the data layer. Engineering Intelligence is the analysis layer on top.
Not code review tools. AI code review tools (Greptile, Graphite, CodeRabbit) catch bugs before production by analyzing code changes. They're excellent at single-domain analysis (finding security vulnerabilities and logic errors in pull requests). But they can't tell you that this code pattern correlates with production incidents, or that similar changes by this team have a 60% rollback rate. Code review tools operate pre-production. Engineering Intelligence spans the entire lifecycle.
Not autonomous agents. While Rebase uses AI agents, we're not building autonomous systems that replace engineers. The distinction: agents with guardrails versus black-box automation. We provide recommendations and generate reviewable changes, but engineers retain decision authority. No system should autonomously modify production infrastructure without explicit human approval.
How Rebase Implements Engineering Intelligence
We built Rebase around three core capabilities that define Engineering Intelligence:
1. Cross-Domain Correlation
This is the defining capability. Rebase maintains a knowledge graph of your engineering system: services, dependencies, code ownership, deployment history, infrastructure topology, incident patterns, and team structure. When an event occurs (deployment, code change, alert), we correlate it across domains.
Example from a real customer deployment:
A team prepares to deploy authentication service changes. Code review passed. Tests green. Traditional tools see no issues.
Rebase surfaces:
Code: This code path was modified by a junior engineer, touches critical auth service, and adds database queries without connection pooling
Infrastructure: Database connection pool at 85% capacity (historically, incidents occur at 90%+)
Operations: Similar deployments by this team caused 3 incidents in past 90 days. Auth service was involved in 40% of P0 incidents this year
Team: Team lacks embedded SRE. Code reviews don't check infrastructure implications
Recommendation: "High deployment risk detected. Deploy Monday morning with staged rollout, prepare rollback plan, alert SRE team."
A code review tool sees "code looks fine." Observability sees "metrics normal right now." Only cross-domain intelligence sees the systemic risk.
2. Proactive Pattern Recognition
Most tools are reactive. They respond to events (alerts, pull requests, incidents). Rebase is proactive. We continuously analyze patterns to surface risks before they become problems.
We learn normal behavior for your services, teams, and systems. We detect anomalies not by simple thresholds ("CPU > 80%") but by understanding context: Is this normal for this service at this time? Is there a correlated code change? Are other services showing similar patterns?
Example from production:
User service latency increased 35% over 14 days, from 120ms to 162ms. Still under SLA (200ms), so no alerts fired.
Rebase identified:
A new feature launched 12 days ago introduced an N+1 query pattern in user preferences loading
Database queries per request increased 28%
Current traffic is 60% of peak. Holiday campaign in 3 weeks will 2x traffic
Similar pattern caused an incident in April when traffic scaled
The recommendation: "High likelihood of incident during holiday campaign if unaddressed. Performance testing shows 40% latency reduction if you refactor user preferences loading to batch queries. Estimated 2-day effort."
No single tool would surface this. Observability might eventually alert when latency crosses threshold (too late). Code review approved the feature (no obvious bug). Only cross-domain pattern recognition connects historical incidents to current code patterns to future traffic expectations.
3. Decision Support with Human Approval
We made an explicit architectural decision: Rebase augments human decision-making rather than replacing it.
We provide evidence, recommendations, and reviewable action plans. Engineers see the intelligence, validate the logic, and approve actions. For infrastructure changes, we generate Terraform PRs with rollback plans and blast-radius analysis. For incident response, we surface correlated root causes but don't auto-remediate. For deployments, we flag risk but don't block without approval.
Why we chose this approach: Non-determinism in AI systems means fully autonomous execution is risky for production infrastructure. An LLM hallucinating a command or misinterpreting context could cause outages. Decision support with human-in-the-loop provides the speed benefits of AI (seconds to correlate across millions of events) with the safety of human judgment (validation before execution).
This is a principled stance: we believe the future of engineering tools is intelligence plus human oversight, not replacement.
The Strategic Value: What Leadership Actually Gets
We built Rebase for engineers but sell to VPs of Engineering and CTOs. The value proposition differs by level:
For VPs of Engineering:
Rebase answers questions you couldn't answer before:
"Where should I invest to reduce incidents?" We correlate incident patterns to architectural bottlenecks, showing that auth layer refactoring would eliminate 40% of P0s
"Which teams need help?" We identify that Team A's 4x incident rate stems from inadequate infrastructure context, not code quality issues
"What's our technical debt costing us?" We quantify velocity drag: legacy payment module slows 60% of payment features by 2 weeks per quarter
This shifts conversations from intuition ("We should probably refactor X...") to data-driven prioritization ("Refactoring X will reduce incidents by 40% and improve feature velocity by 20%, ROI positive in 6 months").
For CTOs:
We connect engineering effectiveness to business outcomes. Board-level questions become answerable:
"Why are we getting outages?" Systemic patterns identified: shared infrastructure bottlenecks, insufficient testing in critical paths, architectural debt accumulated over 18 months
"How does our engineering effectiveness compare to industry?" Benchmark deployment frequency, lead time, incident rates against anonymized industry data
"What's our return on engineering investment?" Connect engineering initiatives (platform team, tooling, technical debt reduction) to measurable outcomes (incident reduction, velocity improvement, developer satisfaction)
Why Cross-Domain Is the Breakthrough
The technical innovation in Engineering Intelligence (and what makes Rebase fundamentally different from existing tools) is connecting data that was never meant to be connected.
Traditional approaches treat code, infrastructure, and operations as separate concerns with separate tools. This made sense when these were distinct teams: developers wrote code, ops managed infrastructure, SREs handled incidents. Modern engineering blurs these lines (platform engineering, DevOps, "you build it you run it"), but tools haven't caught up.
We built a unified data model that represents your entire engineering system. This isn't just aggregating logs. It's building a semantic understanding of relationships:
Service A depends on Services B, C, D
Service A is owned by Team X
Team X deployed feature Y on date Z
Feature Y modified code modules M, N, O
Modules M, N, O have cyclomatic complexity scores and test coverage metrics
Service A has incident history: 3 incidents in past 90 days, all involving database connection exhaustion
Current infrastructure: database connection pool at 85% capacity, auto-scaling policy changed 6 days ago
Team X has deployment patterns: 8 deployments in past month, 3 resulted in rollbacks
This graph enables queries that single-domain tools can't answer:
"Which architectural patterns correlate with incidents?" Identifies that services touching shared authentication layer have 3x higher incident rates.
"Why does Team A have higher rollback rates than Team B?" Discovers Team A lacks infrastructure context during code reviews, deploys without checking capacity.
"What's the ROI of investing in technical debt reduction?" Correlates specific code modules with incident frequency and engineer time spent debugging.
Where Engineering Intelligence Is Going
We're in the early stages of what this category becomes. Current implementations (including Rebase v1) focus on visibility and pattern recognition. The trajectory points toward intelligence-driven engineering systems.
Phase 1 (Now): Insight Generation
Platforms analyze data and surface patterns. Engineers act on insights manually. Value: faster diagnosis, better prioritization, prevented incidents.
Phase 2 (2 to 3 years): Guided Orchestration
Platforms recommend actions and generate reviewable change plans. Engineers approve, system executes. Example: "Approve deployment strategy, and we coordinate code merge + infra scaling + monitoring setup, all with one approval." Value: reduced coordination overhead, standardized practices, faster execution.
Phase 3 (5+ years): Intelligence-Native Development
Engineering organizations operate through intelligence layers. You describe intended outcomes ("Reduce checkout latency by 200ms while maintaining 99.9% availability") and the system proposes multi-domain approaches (code optimization + infrastructure scaling + architecture changes), simulates impact, and orchestrates execution with human oversight.
This isn't science fiction. The technical components exist today. The limiting factor is trust: engineering teams must see the system make good decisions repeatedly before delegating complex orchestration.
We're building toward this future deliberately, starting with decision support and expanding as organizations build confidence in the intelligence.
Getting Started: How to Evaluate Engineering Intelligence
If you're evaluating Engineering Intelligence for your organization, start here:
Assess your intelligence gap. Can you answer these questions with current tools?
Which architectural patterns cause the most incidents?
Which code changes have the highest deployment risk?
What's the true cost (time + incidents) of your top 3 areas of technical debt?
Which teams or services need the most attention?
If you're manually correlating data across tools to answer these, you have an intelligence gap.
Identify your pain points. Engineering Intelligence solves different problems depending on maturity:
Growing teams (100 to 500 engineers): visibility into architectural bottlenecks, team effectiveness patterns, incident root causes
Mature teams (500 to 2000 engineers): systematic technical debt prioritization, cross-team coordination, platform engineering optimization
Large organizations (2000+ engineers): engineering effectiveness benchmarking, resource allocation optimization, regulatory compliance
Start with a wedge. Don't try to deploy comprehensive intelligence on day one. At Rebase, we recommend starting with one high-value use case:
Incident prevention: Correlate code patterns with incident history to flag risky deployments
Technical debt prioritization: Connect code complexity metrics with business impact (which debt actually slows you down?)
Team effectiveness: Identify teams that need help based on patterns, not just metrics
Once the platform proves value in one domain, expand to others.
Engineering Intelligence represents the next evolution in how we build software: from siloed tools to unified intelligence, from reactive to proactive, from gut feeling to data-driven decisions.
The engineering organizations that adopt this approach will ship faster, with higher quality, at lower cost. The question isn't whether Engineering Intelligence becomes standard. It will. The question is whether you adopt early enough to capture the advantage.
That's why we're building Rebase.
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