Services
Engineering delivery services for production-grade systems — operational, concrete, and built for maintainability.
Ways we engage
Engagement models for delivery.
Flexible engagement models designed for clear ownership and predictable delivery.
Dedicated squads
Full teams assigned to your product or system, with clear ownership and accountability.
- End-to-end delivery
- Clear ownership
- Long-term system health
Team augmentation
Engineers embedded with your team, following your processes and standards.
- Seamless integration
- Knowledge transfer
- Process alignment
Build and harden
Focused engagements to build new capabilities or stabilize existing systems.
- Clear scope and outcomes
- Production readiness
- Handoff documentation
Core services
Product, platform, and AI — delivered with guardrails.
Concrete capabilities that show up in real delivery: decisions written down, safe rollouts, and systems that stay maintainable.
Product
Product engineering
Production-grade delivery of web products — from foundations to iteration.
- UX + design systems
- Front-end + back-end delivery
- Quality gates and production readiness
Workflows & operations
Systems for real business processes — approvals, queues, roles, and audit trails.
- Workflow engines and orchestration
- Role-based access patterns
- Operational dashboards and reporting
Document-heavy platforms
Document generation, templates, and signing flows for regulated operations.
- Template systems
- Document assembly
- PDF generation pipelines
Platform
Architecture & modernization
Move from brittle systems to maintainable architecture without breaking the business.
- Service boundaries and interfaces
- Incremental migration plans
- Performance and reliability work
Observability & resilience
Telemetry and operational practices that reduce surprises in production.
- Tracing, metrics, logging
- Runbooks and incident playbooks
- SLO thinking (as applicable)
Security foundations
Practical security patterns integrated into delivery — aligned to real constraints.
- AuthN/AuthZ patterns
- Secrets and configuration hygiene
- Threat-aware reviews
AI
AI product integration
LLM features that fit real workflows — with evaluation, safety, and fallbacks.
- RAG patterns and retrieval design
- Prompt + tool workflows
- Quality evaluation and monitoring
Data readiness
Contracts, quality checks, and governance clarity so AI does not stall in production.
- Data modeling and contracts
- Latency + reliability constraints
- Access and governance guardrails
Automation with humans-in-the-loop
Assistive automation designed for accountability, auditability, and safe rollouts.
- Approval and review loops
- Audit trails
- Feature flags and staged rollouts
Delivery & Quality
QA and testing
Quality ownership and testing discipline integrated into delivery.
- Test strategy and automation
- Quality gates
- Release validation
Code review and standards
Peer review for quality, maintainability, and alignment with standards.
- Code review process
- Standards enforcement
- Knowledge sharing
DevOps & Reliability
CI/CD and release discipline
Automated pipelines and clear release processes.
- CI/CD pipelines
- Release automation
- Rollback procedures
Infrastructure and monitoring
Reliable infrastructure with observability and incident response.
- Infrastructure as code
- Monitoring and alerting
- Incident response
What you get
Clear deliverables and handoffs.
Written decisions, clear boundaries, and release plans that keep teams aligned and reduce surprises.
- Written scope and constraints (what we know / what we don’t)
- Decision log: trade-offs, ownership, and rationale
- Architecture notes and interface boundaries
- Release plan: rollout, rollback, and validation signals
- Operational readiness: telemetry, dashboards, and runbooks (as applicable)
Technology & delivery
Modern stacks, pragmatic delivery.
We work with modern web stacks and real production constraints. The goal is simple: systems that are maintainable, observable, and safe to change.
Good fit
Delivery collaborations that work well
- Teams building production-grade systems that need reliable delivery.
- Companies that value maintainable code, clear documentation, and predictable releases.
- Projects where quality gates, code review, and operational readiness matter.
Not a fit
When we should not start
- Projects seeking the cheapest build without quality considerations.
- Teams that need aggressive timelines without space for quality gates and testing.
- Work that requires guaranteed outcomes or fixed metrics without discovery.
FAQ
Practical answers.
A few common questions — answered without sales language.
Do you take full ownership or work alongside our team?
Both. We can lead delivery end-to-end or embed with your team. In both cases, we keep decisions written down and progress visible.
Can you start with a small engagement?
Yes. Many delivery collaborations start with a short discovery + plan, then expand into delivery once the scope and constraints are clear.
Do you work with existing stacks and legacy systems?
Yes. We prefer incremental modernization with safe rollouts rather than big-bang rewrites.
How do you handle AI work without hype?
We focus on production fit: data readiness, evaluation, monitoring, and fallbacks — so AI features remain reliable and accountable.
Where can we see examples of shipped work?
We share relevant examples during a walkthrough. You can also browse highlights on the case studies page.
Next step
Let's talk about your delivery needs.
Share what you're building and where you need delivery support. We'll respond with next steps.