Product Engineering Services:
Enterprise Systems Built to Perform.
Infarsight Product Engineering builds, modernises and operates the software platforms that power enterprise operations, on Azure, AWS and modern cloud infrastructure. We measure engineering success in operational performance and uptime, not features shipped.
What is Enterprise Product Engineering?
Enterprise product engineering is the practice of designing, building and operating the software platforms that power business operations, not as standalone projects, but as embedded, continuously maintained systems. It combines architecture design, AI-accelerated development, reliability engineering and platform operations into a single accountable capability.
We build for what comes
after go-live.
Built for What Comes After Go-Live
Every platform we build is designed to be operated, maintained and evolved. Reliability, observability and BAU ownership are part of the brief, not an afterthought.
Operational Context, Not Just Technical Delivery
Our engineers understand Travel, Ports and Mobility deeply. They build systems aligned to how operations work, not how a requirements document described them.
Embedded, Long-Term Engineering Partnership
We don't hand over and leave. We embed into your teams and stay accountable for the systems we build, tracking their performance and evolving them as the business changes.
Systems That Connect to the Full Stack
Our delivery accelerators connect directly to data, automation and agentic AI layers. Product Engineering isn't isolated, it's the execution layer of a broader operational system.
Each practice line has defined inputs,
activities and measurable outputs.
Platform Design
Designing platforms built for operational purpose, not just technical correctness. Systems that align to how operations actually work.
- Operational platform scoping & service design
- UX design & API-first architecture
- Domain modelling & MVP prototype delivery
- Stakeholder alignment workshops & architecture decision records
- Platforms aligned to real operational workflows
- Faster time to working software
- Lower rework from misaligned specs
- API ecosystem ready for integration from day one
Architecture Modernisation
Moving legacy monoliths to scalable, maintainable architectures, without halting operations or gambling on a big-bang rewrite.
- Monolithic codebase, one change breaks everything
- No test coverage, every release is a risk
- On-premise infrastructure with no elasticity
- Point-to-point integrations between every system
- Undocumented and understood by one person
- Domain-driven services — change without fear
- Test coverage at unit, integration and contract level
- Cloud-native with auto-scaling and DR built in
- Event-driven integration via APIs and message queues
- Documented, observable and team-owned
Reliability Engineering
Designing systems that perform under operational pressure, and recover fast when they don't. Reliability is designed in, not tested in.
- SLA & uptime design, error budgets and SLO frameworks before a line of code is written
- Load & failure testing, stress, chaos engineering and failure mode analysis before production
- Observability & alerting, distributed tracing, structured logging, real-time dashboards
- Incident response design, runbooks, escalation paths and automated remediation
- 99.9% uptime on operationally critical workflows
- Team knows about issues before users do
- Mean time to recovery engineered, not improvised
- 70% reduction in production incidents post-stabilisation
BAU Stabilisation
Keeping live systems healthy, stable and continuously improving, long after the delivery team has left.
- Production support, embedded engineers on call for rapid triage, fix and deploy
- Bug triage & resolution with SLA-based timescales
- Continuous performance monitoring with threshold-based alerting
- Planned refactoring sprints that reduce tech debt and improve velocity
- Performance degrades silently, users complain before the team knows
- Bug backlog grows unchecked, stability erodes month by month
- Tech debt compounds, every new feature takes longer to ship
- Knowledge dies with the delivery team
Incremental Evolution
Continuously improving live platforms so they grow with the business, without disruptive rebuilds. The alternative to incremental evolution is a costly rebuild every 3–4 years.
- Sprint-based capability releases with feature flags for safe rollouts
- Structured backlog prioritisation with effort-impact scoring
- Modularisation, test coverage uplift and security patching
- Capability expansion, new integrations, personas, APIs and roadmap co-creation
- 60% reduction in time from idea to live feature
- 3× faster release velocity after architecture modernisation
- No costly big-bang rebuilds every 3–4 years
- Platforms that grow with the business continuously
AI accelerates every phase
of the engineering lifecycle.
From requirements capture to production operations, AI is woven across the full SDLC, reducing effort, accelerating delivery and improving quality at every stage.
Business Requirements
- AI auto-generates BRD drafts from interview transcripts
- NLP extracts and ranks requirements from emails and tickets
- AI flags missing acceptance criteria and contradictions
- Automated feasibility scoring and dependency mapping
Architecture & Planning
- AI proposes architecture patterns from BRD requirements
- Automated ADR drafting from design discussions
- AI-driven API contract and schema generation
- Tech stack recommendation engine scoring fit and cost
User Experience
- AI generates wireframes and prototypes from user stories
- Automated user journey mapping from analytics data
- WCAG accessibility compliance scan on every iteration
- Multi-variant A/B design generation for critical flows
Development
- AI pair programming reduces boilerplate by 30–50%
- AI-assisted PR reviews flagging security and quality issues
- Automated code refactoring suggestions inline
- AI-generated unit tests alongside code completion
Quality Assurance & Self-Healing QA
- 60–80% AI-generated test coverage from code changes
- Self-healing tests detect, diagnose and repair themselves when the application changes, eliminating maintenance toil
- AI failure classifier distinguishes real defects from test maintenance issues (flaky locators, stale fixtures, env drift)
- Synthetic load generation with AI-driven scenario authoring and anomaly detection
- Test maintenance effort reduced by 60–80% through autonomous repair
Release & Operations
- AI-assisted deployment pipelines with rollback intelligence
- Anomaly detection surfaces regressions before production exposure
- AI-driven incident classification and automated runbook execution
- Continuous monitoring with AI root cause analysis
- 2× improvement in release cadence through AI-optimised pipelines
Architecture-driven selection.
We choose what is right for your operational context, not what is convenient. Technology choices are made to serve the system's reliability and operational requirements.
- Kotlin
- Java
- .NET / C#
- Python
- Swift
- Microsoft Azure
- AWS
- Kubernetes
- Terraform
- Azure API Management
- Apache Kafka
- Event Grid
- MuleSoft
- Playwright
- k6 / Gatling
- Grafana
- Azure Monitor
Purpose-Built Platforms for Travel,
Ports, Mobility and Airlines Operations.
Travel & Hospitality
- Booking & reservation platform modernisation, legacy PSS to cloud-native
- Hotel operations platform, housekeeping, maintenance & guest service automation
- Disruption management system, auto-rebook, re-route and re-accommodate at scale
- Revenue management platform integration with real-time demand signals
Ports & Logistics
- Berth scheduling and port operations platform with real-time conflict resolution
- Gate management system, sensor + ERP integration for throughput automation
- Container tracking and dwell time platform with predictive staging logic
- Port command center, unified operational view across all port systems
Fleet & Mobility
- Fleet dispatch platform — predictive scheduling from vehicle telemetry
- EV network management, charging state, fault resolution & demand forecasting
- Driver and route management platform with real-time optimisation
- Fleet command center, live health, utilisation and SLA dashboards
Airlines
- Airline operations control platform — disruption handling and crew re-planning
- Ground operations system, turnaround, gate assignment and crew allocation
- Passenger services platform, disruption comms, rebooking and compensation
- Revenue and yield management platform integration with live sales data
Measurable results from
Infarsight Product Engineering engagements.
Time to New Capability
Moving from big-bang project cycles to continuous delivery reduces time from idea to live feature by 60%.
Uptime on Critical Systems
Platforms redesigned with SRE principles consistently achieve 99.9%+ uptime on operationally critical workflows.
Production Incidents
BAU stabilisation programmes reduce live incidents by up to 70% within two quarters of embedded engineering ownership.
Release Velocity
Teams shipping from modernised, cloud-native architectures release 3× faster with significantly lower rollback rates.
The product engineering workflow.
From operational problem to production system, in a repeatable, governed process.
Discover
Platform & systems audit, stakeholder interviews, operational pain mapping, architecture assessment. 1–2 weeks.
Design
Platform architecture, API contract design, data & integration model, reliability framework. 2–4 weeks.
Build
Iterative engineering sprints, integration delivery, QA & test automation, performance testing. 6–16 weeks.
Operate
BAU support & monitoring, incident management, performance tuning, stakeholder reporting. Ongoing.
Evolve
Backlog engineering, refactoring cycles, new capability delivery, architecture reviews. Continuous.
Ready to build systems that perform?
We start with a Platform & Systems Assessment, mapping your current architecture, identifying reliability risks, modernisation opportunities and the highest-value engineering investments.
Book a Platform Assessment →