Core Engineering Modernization
- Spring Boot / Kotlin microservices
- API design and integration
- Kafka and event-driven systems
- PostgreSQL / Oracle and data migration
- Performance tuning for deep hierarchy and large-data workloads
Core Engineering • AI Enablement • Managed Reliability
Modernize critical systems. Introduce AI with governance. Improve reliability continuously.
ARCelerators helps enterprise teams modernize backend platforms, embed AI into existing operating environments, and sustain reliability improvements through ongoing engineering support.
A
Core systems modernization
B
AI embedded in internal operations
C
Ongoing reliability stewardship
How we work
- Enterprise delivery grounded in phased execution and rollout safety
- Architecture decisions shaped by integration depth, data complexity, and operating risk
- AI initiatives governed through access control, auditability, and evaluation
- Improvement programs measured through delivery, reliability, and support metrics
Capability highlights
Focused capabilities across modernization, applied AI, and operational reliability.
AI Enablement for Existing Systems
- Internal knowledge search over docs, SOPs, and tickets
- AI support assistants for operations and internal teams
- AI-assisted analytics summaries
- AI automation around alerts, incidents, and reports
- Integration, guardrails, access control, and auditability
Managed Reliability & Continuous Improvement
- Monthly support and targeted enhancements
- Performance and cost tuning
- Observability and incident reduction
- AI workflow monitoring
- Predictable, retainer-based delivery cadence
A delivery model designed for enterprise change
Structured to reduce execution risk while keeping modernization, AI adoption, and operational improvement measurable.
01
Diagnose
Assess architecture, delivery friction, operational constraints, and the risk profile of the current estate.
02
Modernize
Advance services, integrations, and data movement through phased execution and controlled cutover.
03
Enable AI
Introduce AI into internal workflows with retrieval, permissions, evaluation, and governance controls.
04
Retain & Improve
Establish an ongoing cadence for support, tuning, monitoring, and reliability improvement.
Services
Three focused offers aligned to modernization, applied AI, and sustained operational performance.
Core Engineering Modernization
For organizations modernizing complex backend platforms, integration layers, and data-intensive systems.
- Microservices, APIs, event-driven systems, and data migration
- Performance tuning for complex, high-volume application estates
- Representative mandate: Modernize legacy backend and data pipelines with faster releases and lower production risk.
AI Enablement for Existing Systems
For teams applying AI to existing systems where governance, integration depth, and operating value matter.
- Knowledge retrieval, internal assistants, summaries, and operational automation
- Integration, guardrails, evaluation, and measurable operational outcomes
- Representative mandate: AI copilots integrated with internal systems, documents, and workflows with access control and auditability.
Managed Reliability & Continuous Improvement
For teams that require a structured operating cadence for support, tuning, and continuous reliability improvement.
- Monthly support, targeted enhancements, observability, and incident reduction
- Performance, cost, and AI workflow monitoring
- Engagement value: recurring engineering support that strengthens operational resilience over time.
What this means for your team
Three focused service lines aligned to modernization, controlled AI adoption, and long-term operational resilience.
- Modernize with control — evolve backend platforms and data flows through staged delivery models that reduce operational and migration risk
- Apply AI where governance matters — embed AI into internal systems, knowledge sources, and workflows with access control and auditability
- Improve reliability as an operating discipline — convert support, performance tuning, and observability into a sustained improvement program
- Operate within enterprise realities — architecture complexity, security review, compliance expectations, and rollout safety are treated as first-order concerns
- Start from the highest-value constraint — engage around modernization, AI enablement, or retained reliability support based on the most immediate operational bottleneck
- Keep delivery tied to measurable outcomes — scope is anchored to release confidence, incident reduction, support efficiency, and operating performance
- Create a path for expansion — modernization engagements can extend naturally into AI enablement and ongoing reliability stewardship
- Support clearer decisions — each service line is specific enough for technical and executive stakeholders to evaluate with confidence
Typical engagement outputs
Representative artifacts and operating assets produced during delivery.
Modernization blueprint
Target-state architecture, service decomposition, integration mapping, migration sequencing, and rollout governance.
AI integration package
Retrieval architecture, internal system connectors, access controls, evaluation approach, and audit-ready operating controls.
Reliability improvement backlog
Structured backlog for support, observability, cost tuning, incident reduction, and workflow monitoring priorities.
Why ARCelerators?
Engineering depth, governed AI adoption, and a disciplined approach to operational improvement.
Deep backend credibility
Core modernization work is anchored in backend systems, event-driven architectures, databases, and demanding performance constraints.
AI with integration and guardrails
AI is applied within existing systems and operating workflows, with governance, traceability, and measurable business value.
Recurring improvement engine
Retained support converts reliability, tuning, and enhancement work into a repeatable operating model rather than a series of one-off interventions.
Representative engagements
Illustrative examples of the types of work these engagements are designed to support.
Reduced release risk · improved latency · completed migration
Legacy core platform modernization
Modernization of Spring Boot services, Kafka integrations, and Oracle-to-PostgreSQL data migration through a staged cutover program.
Faster resolution paths · fewer manual escalations · auditable AI outputs
Internal AI enablement
Internal knowledge search and support assistance connected to SOPs, tickets, and documentation with governed access control.
Lower incident volume · improved cost profile · monthly improvements delivered
Managed reliability retainer
Ongoing observability, performance tuning, enhancement delivery, and AI workflow monitoring for a business-critical operations platform.
Frequently asked questions
How do you approach core engineering modernization safely?
We use phased delivery, test harnesses, migration sequencing, and controlled cutovers so backend and data changes remain reversible, measurable, and operationally safe.
Do you build AI chatbots?
Our focus is on AI embedded in internal systems and workflows, supported by retrieval, permissions, auditability, and clearly defined operating metrics.
What does the managed reliability retainer cover?
The retainer typically includes monthly support, targeted enhancements, observability, performance and cost tuning, incident reduction, and monitoring for AI-enabled workflows.
How do you measure success?
We define success measures at the outset, including release confidence, latency, incident rate, support load, automation gains, and business outcomes aligned to the engagement.
Discuss the right starting point
We can help identify the right entry point based on architecture complexity, support pressure, delivery risk, and operating goals.