Our expertise
Built through years of production work.
Not theoretical knowledge. Operational expertise.
Hover or tap nodes to see connections. Click to explore.
Software architecture
Architecture is trade-offs.
Every architectural decision opens some doors and closes others.
Toggle between architectures to see how trade-offs shift.
Single deployable unit. Fast to start. Hard to scale.
Typical Problems
- Monolithic systems that resist modification
- Unclear ownership boundaries between teams or modules
- Performance bottlenecks that emerge only at scale
- Technical debt accumulating faster than it can be addressed
- Systems that work in development but fail under production load
Decisions We Make
- Service boundaries based on domain, not convenience
- Synchronous vs. asynchronous communication patterns
- Database per service vs. shared data stores
- Event sourcing where audit trails matter
- Explicit failure modes over silent degradation
Constraints That Matter
- Regulatory requirements that limit architectural choices
- Existing infrastructure that cannot be replaced
- Team size and skill distribution
- Uptime requirements that restrict deployment windows
- Data residency and compliance obligations
What Breaks When Ignored
- Systems become impossible to modify without cascading changes
- Deployments require coordination across multiple teams
- Single points of failure emerge in unexpected places
- Performance issues require architectural rewrites, not optimization
- New requirements cannot be met without starting over
AI & Automation
Where to draw the line.
Drag the trust boundary. Watch the system reconfigure.
Ingest
Auto
Classify
Auto
Route
Auto
Decide
Human
Act
Human
Monitor
Auto
Balanced automation.
Human judgment where it matters. Automation where it is safe.
Typical Problems
- Models that perform well in testing but fail on real data
- Automation that handles common cases but breaks on exceptions
- Unclear accountability when automated decisions go wrong
- Drift in model performance over time without detection
- Integration complexity between AI components and existing systems
Decisions We Make
- Define explicit boundaries for what AI should and should not decide
- Build confidence scoring into every automated decision
- Design human escalation paths before automation paths
- Monitor for distribution shift, not just accuracy
- Version models and data together, not separately
Constraints That Matter
- Explainability requirements in regulated industries
- Latency budgets that limit model complexity
- Data privacy restrictions on training and inference
- Human oversight requirements for high-stakes decisions
- Fallback requirements when AI components fail
What Breaks When Ignored
- Automated systems make decisions no one can explain
- Edge cases cause cascading failures
- Model updates break downstream systems
- No one knows when AI performance degrades
- Regulatory audits reveal undocumented decision logic
Data & integrations
Nothing was designed to connect.
Click integration points to see what connects, what breaks, and how we handle it.
Click an integration point to explore
See connection details and failure modes
Typical Problems
- Data spread across systems with no single source of truth
- APIs that change without notice or documentation
- Legacy systems with no modern integration capabilities
- Data quality issues that propagate across integrations
- Synchronization conflicts between systems with different update frequencies
Decisions We Make
- Canonical data models at integration boundaries
- Idempotent operations to handle retry scenarios
- Circuit breakers for unreliable external systems
- Data validation at ingestion, not consumption
- Explicit versioning for all integration contracts
Product engineering
Products are never finished.
Click through the product lifecycle. Each phase brings new challenges.
Design for real workflows.
User research, workflow mapping, edge case discovery. Build for how people actually work, not how we assume they work.
Typical Problems
- Features designed for demos, not daily use
- User workflows that diverge from system assumptions
- Performance degradation as usage grows
- Accessibility gaps that exclude users
- Complexity creep from accumulated feature requests
Decisions We Make
- Optimize for the 80% case, but handle the 20% gracefully
- Design state management for recovery, not just happy paths
- Build administrative tools alongside user features
- Instrument everything that matters for debugging
- Version APIs to allow gradual migration
Long-term maintenance
Systems age. Maintenance decides how.
Toggle real-world pressures. Drag the age slider.
Watch what happens to software without active maintenance.
Maintenance burden
Additional maintenance requirements:
Nothing beyond the basics. Yet.
Without active maintenance
Documentation
Current
Dependencies
Up to date
Test Coverage
Healthy
Team Knowledge
Deep
Security Posture
Strong
System is young. Everything works. Maintenance feels optional.
Typical Problems
- Knowledge loss when team members leave
- Dependencies that become unsupported
- Security vulnerabilities in aging components
- Increasing cost of changes as systems age
- Regulatory requirements that emerge after deployment
Decisions We Make
- Document decisions, not just implementations
- Automate dependency updates with controlled rollout
- Budget technical debt reduction into every project
- Design for component replacement, not system replacement
- Maintain operational runbooks alongside code