Core Engineering • AI Enablement • Managed Reliability
ARCelerators helps enterprise teams modernize backend platforms, embed AI into existing operating environments, and sustain reliability improvements through ongoing engineering support.
Focused capabilities across modernization, applied AI, and operational reliability.
Structured to reduce execution risk while keeping modernization, AI adoption, and operational improvement measurable.
Assess architecture, delivery friction, operational constraints, and the risk profile of the current estate.
Advance services, integrations, and data movement through phased execution and controlled cutover.
Introduce AI into internal workflows with retrieval, permissions, evaluation, and governance controls.
Establish an ongoing cadence for support, tuning, monitoring, and reliability improvement.
Three focused offers aligned to modernization, applied AI, and sustained operational performance.
For organizations modernizing complex backend platforms, integration layers, and data-intensive systems.
For teams applying AI to existing systems where governance, integration depth, and operating value matter.
For teams that require a structured operating cadence for support, tuning, and continuous reliability improvement.
Three focused service lines aligned to modernization, controlled AI adoption, and long-term operational resilience.
Representative artifacts and operating assets produced during delivery.
Target-state architecture, service decomposition, integration mapping, migration sequencing, and rollout governance.
Retrieval architecture, internal system connectors, access controls, evaluation approach, and audit-ready operating controls.
Structured backlog for support, observability, cost tuning, incident reduction, and workflow monitoring priorities.
Engineering depth, governed AI adoption, and a disciplined approach to operational improvement.
Core modernization work is anchored in backend systems, event-driven architectures, databases, and demanding performance constraints.
AI is applied within existing systems and operating workflows, with governance, traceability, and measurable business value.
Retained support converts reliability, tuning, and enhancement work into a repeatable operating model rather than a series of one-off interventions.
Illustrative examples of the types of work these engagements are designed to support.
Modernization of Spring Boot services, Kafka integrations, and Oracle-to-PostgreSQL data migration through a staged cutover program.
Internal knowledge search and support assistance connected to SOPs, tickets, and documentation with governed access control.
Ongoing observability, performance tuning, enhancement delivery, and AI workflow monitoring for a business-critical operations platform.
We use phased delivery, test harnesses, migration sequencing, and controlled cutovers so backend and data changes remain reversible, measurable, and operationally safe.
Our focus is on AI embedded in internal systems and workflows, supported by retrieval, permissions, auditability, and clearly defined operating metrics.
The retainer typically includes monthly support, targeted enhancements, observability, performance and cost tuning, incident reduction, and monitoring for AI-enabled workflows.
We define success measures at the outset, including release confidence, latency, incident rate, support load, automation gains, and business outcomes aligned to the engagement.
We can help identify the right entry point based on architecture complexity, support pressure, delivery risk, and operating goals.