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Xinexis

Services

Software, data & AI engineering services

Build software that fits your operations, connect fragmented systems, and turn data into useful tools. Xinexis brings software, data, cloud, and AI engineering to the work your team needs to move forward.

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Ways to work together

Start with the problem you need to solve

Begin with an assessment, a focused build, or one phase of a larger modernization. Scope and deliverables are agreed for the engagement.

Assessment & architecture

When the problem is clear but the solution is not. Map your systems, identify risks, and define a practical plan for the next investment.

Typical result: A system assessment and implementation roadmap.

Focused engineering delivery

When you have a defined need. Build an application, connect systems, improve a data pipeline, or integrate an AI capability in testable stages.

Typical result: A working capability with tests and handover documentation.

Modernization partnership

When an existing platform needs to evolve. Plan incremental improvements around the systems your team depends on each day.

Typical result: A scoped modernization phase with a clear next step.

Custom Software Engineering

Operational platforms, internal applications, backend systems, APIs, workflow applications and modern cloud-native software.

Situations & deliverables

Common situations

  • Off-the-shelf tools do not fit operational workflows or data models.
  • Manual processes depend on spreadsheets, email, and brittle scripts.
  • Teams need reliable software that integrates with existing systems.

Typical deliverables

  • Greenfield application development
  • Backend and API development
  • Workflow and case-management applications
  • Legacy application extension
Technical scope & intended value

Technical scope

Python · Go · FastAPI · SQL · Docker · Kubernetes

Intended operational value

  • Purpose-built software aligned to operational requirements.
  • Maintainable codebases with clear ownership and documentation.
  • Reduced manual effort through reliable automation.

Data Engineering & Integration

ETL/ELT pipelines, APIs, event-driven integration, data ingestion, transformation, quality, migration and operational data flows.

Situations & deliverables

Common situations

  • Data is fragmented across databases, files, and SaaS tools.
  • Reporting depends on manual exports and inconsistent definitions.
  • Integration projects stall without a practical data architecture.

Typical deliverables

  • Pipeline design and implementation
  • System-to-system integration
  • Data quality and validation frameworks
  • Migration and consolidation projects
Technical scope & intended value

Technical scope

Python · SQL · Kafka · Elasticsearch · AWS · ETL/ELT

Intended operational value

  • Consistent, auditable data flows between systems.
  • Reduced reconciliation effort and data errors.
  • Foundations for analytics and decision support.

Cloud & Distributed Systems

Scalable cloud architectures, Kubernetes, microservices, event streaming, observability, reliability and performance optimization.

Situations & deliverables

Common situations

  • Existing systems struggle with volume, latency, or reliability targets.
  • Infrastructure costs grow without clear performance gains.
  • Operational visibility is limited across distributed components.

Typical deliverables

  • Architecture review and hardening
  • Microservices and event-driven design
  • Performance and cost optimization
  • Reliability engineering and observability
Technical scope & intended value

Technical scope

AWS · Kubernetes · Docker · Kafka · Go · Python

Intended operational value

  • Systems designed for production load and failure modes.
  • Improved throughput, uptime, and operational visibility.
  • Infrastructure aligned to business constraints.

AI & Machine Learning

Applied ML, document intelligence, prediction, classification, computer vision, NLP and intelligent workflow components designed for production use.

Situations & deliverables

Common situations

  • Teams need ML where it measurably improves decisions or throughput—not as a standalone experiment.
  • Document-heavy workflows require extraction, classification, or routing at scale.
  • Models must integrate into existing software with appropriate oversight.

Typical deliverables

  • ML feasibility and use-case assessment
  • Production model integration
  • Document intelligence pipelines
  • Evaluation, monitoring, and human-in-the-loop design
Technical scope & intended value

Technical scope

Python · PyTorch · scikit-learn · OpenCV · FastAPI · AWS ML

Intended operational value

  • ML components embedded in operational workflows.
  • Measurable improvement on defined tasks with guardrails.
  • Explainable outputs where decisions require accountability.

Systems Architecture & Modernization

Current-state assessment, technical architecture, legacy modernization, integration strategy, target-state design and implementation roadmaps.

Situations & deliverables

Common situations

  • Legacy systems constrain new capabilities and increase operational risk.
  • Technology decisions lack a practical path from current to target state.
  • Multiple vendors and platforms create integration debt.

Typical deliverables

  • Architecture assessment and documentation
  • Modernization and migration planning
  • Integration strategy and API design
  • Technical advisory with implementation support
Technical scope & intended value

Technical scope

Cloud platforms · APIs · Event-driven patterns · SQL · Kubernetes

Intended operational value

  • Clear, implementable architecture aligned to constraints.
  • Reduced rip-and-replace risk through incremental modernization.
  • Roadmaps teams can execute with confidence.

Analytics & Decision Support

Data modelling, dashboards, forecasting, scenario modelling, operational analytics and decision-support software.

Situations & deliverables

Common situations

  • Leaders lack timely visibility into operational performance.
  • Analytics depend on ad hoc spreadsheets and inconsistent metrics.
  • Forecasting and scenario analysis are manual and error-prone.

Typical deliverables

  • Metrics design and data modelling
  • Operational dashboards and reporting
  • Forecasting and scenario tools
  • Decision-support application development
Technical scope & intended value

Technical scope

SQL · Python · BI integration · Data pipelines · APIs

Intended operational value

  • Reliable metrics tied to operational definitions.
  • Faster access to information for planning and oversight.
  • Software that supports—not replaces—expert judgment.

Example applications

Practical applications across your operations

Examples of where these capabilities can help your team. Each application is scoped around your systems, data, access requirements, and the people who will use it.

Sales and pipeline automation

CRM integration, lead routing, follow-up workflows, and pipeline hygiene—where sales operations need reliable automation.

Support and service workflows

Ticket routing, knowledge retrieval, draft assistance, and escalation paths integrated into existing support tools.

Internal operations

Back-office automation, approvals, notifications, and reporting across systems teams already operate.

Knowledge and document systems

Search, extraction, classification, and Q&A grounded in organizational documents with appropriate access control.

See how these capabilities map to your industry.

Discuss a project

Let's work through your next engineering challenge.

Tell us what needs to improve and what is getting in the way. You do not need a complete specification to start a useful conversation.

  • Operating problem
  • Existing systems
  • Data context
  • Delivery constraints
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