Panel A
System load map
Service graph with latency and saturation overlays by endpoint.
Understand and simulateAWS architecture automatically.
Strikeloop connects to your code and AWS account, simulates real load, and proposes infrastructure changes for approval.
Analyzes APIs, datastore usage, and compute pressure
Simulates workloads before any infrastructure changes
Generates Terraform or CloudFormation with approval gates
Problem
Why cloud architecture is still hard
Most teams have application context in one place and infrastructure context in another. Important architecture decisions are then made manually, under pressure, and without simulation.
Architecture decisions happen with partial context
Application behavior, cloud resources, and growth assumptions are reviewed in separate tools, so teams miss system-level risks.
Manual reviews are slow under delivery pressure
Infra design is often a handoff across app engineers, DevOps, and security, which delays releases and incident response.
Bad sizing and weak bottleneck planning are expensive
Over-provisioning raises cost. Under-provisioning causes outages, high p99 latency, and emergency architecture changes.
Solution
Strikeloop automates design, while you keep control
Instead of static templates, Strikeloop reasons from your real system behavior and recommends cloud architecture changes with approval gates.
Step 1
Connect codebase and AWS account
Step 2
Understand APIs, data paths, and compute behavior
Step 3
Simulate workload and growth scenarios
Step 4
Generate recommended architecture + IaC
Step 5
Apply only after explicit approval
Output is actionable IaC plus a clear human approval workflow
Key Features
Capabilities built for engineering teams
Each capability maps directly to a real architecture task: understanding behavior, validating scale, generating infrastructure, and safely applying change.
Deep system understanding
Maps APIs, dependencies, persistence behavior, and compute hotspots instead of doing keyword-level code scans.
Workload simulation
Estimates traffic shape, concurrency, and bottlenecks before rollout so architecture choices are tested first.
Automatic architecture design
Selects compute, data, cache, and scaling controls based on observed behavior and target reliability constraints.
Terraform / CloudFormation output
Produces auditable Infrastructure as Code with clear diffs, dependencies, and environment-specific settings.
Approval-first apply flow
Every change requires user approval, preserving operator control while reducing repetitive architecture work.
Continuous optimization loop
Re-evaluates architecture after code changes and growth shifts, then proposes safe incremental improvements.
How It Works
Core flow from code context to cloud changes
The system follows a strict sequence: analyze, estimate, simulate, generate, and apply only after explicit approval.
01
Input
Connect repository and AWS account context.
02
Application understanding
Map APIs, service dependencies, and data access patterns.
03
Workload estimation
Estimate request volume, concurrency, and growth shape.
04
Simulation
Stress candidate architectures under realistic load.
05
AI architecture decisions
Select compute, database, and cache strategy.
06
IaC generation
Generate Terraform or CloudFormation templates.
07
Approval and apply
Present changes for review before deployment.
08
Continuous optimization
Track drift and propose safe iterative improvements.
AWS Services
Services Strikeloop plans to use and why
Service selection is tied to runtime behavior, reliability goals, and cost posture, not static templates.
EKS / ECS
Runs containerized services with autoscaling and predictable deployment behavior.
Lambda
Executes asynchronous and bursty workflows without always-on compute costs.
RDS / Aurora
Handles transactional persistence with high availability and read scaling.
ElastiCache Redis
Reduces database pressure by caching hot reads and session-heavy workloads.
S3 + CloudFront
Stores and serves static assets and artifacts with global low-latency delivery.
CloudWatch + X-Ray
Collects metrics and traces that drive bottleneck detection and tuning.
IAM + KMS + Secrets Manager
Implements least-privilege access, encryption key control, and secret rotation.
Example Output
What Strikeloop actually produces
Outputs are concrete and reviewable: bottlenecks, recommendations, generated infrastructure, and expected impact on cost and performance.
Detected issue
Primary database saturation near peak window
Write-heavy endpoints push Aurora CPU to 88% at peak, increasing queue depth and tail latency.
- Suggested fix: add Redis caching for read-heavy paths
- Suggested fix: move async writes to queue-driven workers
- Suggested fix: increase read replica count during peak profile
Generated infrastructure
Terraform plan with approval diff
Plan includes ECS service autoscaling policy updates, Redis cluster creation, and alarm thresholds.
+ aws_elasticache_replication_group.session_cache
+ aws_appautoscaling_policy.api_target_tracking
~ aws_db_instance.primary max_connections = 2000
+ aws_appautoscaling_policy.api_target_tracking
~ aws_db_instance.primary max_connections = 2000
Dashboard
What users see day to day
A single operational view of load, cost, bottlenecks, architecture health, simulation scenarios, and pending approvals.
Predicted p95 latency
124 ms
-31% projected change
Monthly infra cost
$6,480
-18% projected change
Write throughput headroom
2.4x
+2x projected change
Cache hit ratio
82%
+29% projected change
Infrastructure overview
ECS services, databases, cache clusters, and scaling posture.
Bottleneck analysis
Ranks hot paths by latency contribution and throughput risk.
Scenario simulation
Test traffic spikes, growth events, and deployment changes.
Approval controls
Review diffs and approve or reject suggested infrastructure actions.
Continuous Intelligence
Not a one-time recommendation engine
Strikeloop continuously reacts to new code paths, traffic growth, and production drift. It keeps proposing infrastructure improvements so architecture quality compounds over time.
Re-check on code changesRe-check on growth shiftsRe-check on incidents
Prototype
Wireframe view for judge walkthrough
These panels represent the operator flow used to inspect system behavior, simulate scenarios, and approve infrastructure updates.
Panel B
Scenario simulator
Traffic multipliers to model growth events and release risk.
Panel C
Approval queue
Prioritized change list with impact estimates and apply controls.
Automate architecture, keep final control.
Strikeloop proposes cloud improvements continuously, but every infrastructure change still requires your approval.
Designed to explain the solution clearly for hackathon judges.