Axentara reimagines how software gets built - through AI Engineers spanning architecture, development, QA, DevOps, and operations, built to deliver measurable impact from day one.
Meet the Axentara Engineers. Each AI Engineer is context-trained to perform a specific enterprise function - Architecture, Development, QA, DevOps, Support, and Operations - creating a complete AI organization in itself.
Translates requirements into system designs, then into epics and user stories.
Covers greenfield projects today, with brownfield expansion underway.
Ensures architecture velocity without sacrificing enterprise rigor.
Specializes in enterprise application development.
Currently trained on Salesforce (Apex, LWC, flows, commits, Jira integration).
Designed to extend into additional enterprise stacks like Python and Java.
Instantly generates mandatory and edge test cases, with context from Jira tickets and attachments.
Publishes directly in BDD syntax to Jira/Zephyr (Xray, TestRail integrations in progress).
Provides QA coverage that scales without expanding headcount.
Reviews code in seconds with multilingual and meta coverage (labels, constants, permissions).
Performs GitOps operations and Slack-integrated merges.
Ensures velocity and governance consistency at scale.
Triages incidents, identifies root causes, and auto-routes to the right teams.
Reduces MTTR (mean time to resolution) while scaling L1/L2 support capacity.
Manages cross-team workflows: ticket routing, handoffs, and escalation flows.
Keeps delivery aligned across engineering and business functions.
Handles structured content: documentation, archival, compliance records.
Automates knowledge ops across repositories for accuracy and consistency.
Monitors service reliability and SLA adherence.
Flags risks, triggers auto-remediation, and provides health intelligence across environments.
Enterprises choose Axentara because it transforms how software gets built.
Unlike generic copilots or automation tools, Axentara embeds AI Engineers directly into enterprise delivery pipelines - improving velocity, accuracy, and governance from day one.
From DevOps to QA, every Engineer is context-trained to deliver measurable outputs, not just suggestions.
98% faster PR reviews with zero false positives. In one enterprise deployment, Cirus flagged multilingual issues that human reviewers consistently missed - ensuring governance across dozens of Salesforce orgs.
20x velocity in test case generation with 100% standardized outputs. A billion-dollar enterprise used Vela to replace inconsistent manual coverage, freeing QA engineers to focus on execution while ensuring audit-ready test suites.
Turns requirements into system-level designs, epics, and user stories. Enterprises used Nova to accelerate solutioning for both new initiatives and modernization, cutting weeks from architecture timelines.
Writes and refactors enterprise-grade code with governance built in. Lynx delivers standardized outputs that scale across distributed teams.
Originally developed at Arkynis, Axentara’s AI Engineers are now part of Metacube Software’s enterprise AI portfolio - where they continue to power production workloads with Fortune 500 enterprises.
Axentara enables CIOs, CTOs, and IT leaders to increase delivery velocity without adding headcount - a critical challenge in today’s cost-sensitive environment. Each AI Engineer is context-engineered for enterprise workflows, ensuring consistent, standardized outputs across teams. Enterprises gain faster delivery, fewer errors, and stronger governance - all while retaining human oversight and configurability.
Each Axentara Engineer is designed for a specific enterprise role:
All Axentara Engineers can be deployed independently or as part of a unified AI organization - scaling as enterprise needs evolve.
Yes. Axentara was first deployed in Salesforce, tackling its complex metadata and governance - signaling the maturity of its design. Its technology-agnostic brain core allows the same AI Engineers to adapt to other stacks like Java or Python, depending on enterprise needs. This flexibility de-risks adoption and makes Axentara future-ready for diverse enterprise environments.
Enterprises testing Axentara have seen 98% faster PR reviews with Cirus (18 seconds vs. ~20 minutes by senior reviewers), 100% accurate Salesforce root cause analysis, and 100% relevant test cases from Vela in under one minute.
In head-to-head evaluations, Vela’s coverage and precision outperformed in-house tools and third-party systems, including Jira’s Rovo Chat. ROI has been realized in weeks, not months - through reduced rework, faster GTM, improved quality, and stronger governance at scale.
Axentara is not a general-purpose model. It is a context-engineered AI organization built for enterprise workflows. While general-purpose models compete on abstract benchmarks, Axentara focuses on delivering ready-to-use outputs across the entire software delivery lifecycle - from architecture and development to testing, deployment, and governance - all integrated directly into enterprise workflows. By embedding context at both the organizational and workflow level, Axentara seamlessly integrates into enterprise systems rather than forcing teams to adapt to abstract model outputs
PDOs that rely on resource augmentation or human arbitrage face shrinking margins, regulatory pressure, and client demands for faster AI-enabled delivery. Axentara lets them differentiate by embedding AI Engineers directly into delivery workstreams.
For example, deploying Vela for QA test case writing ensures standardized consistency while human teams focus on higher-value development and more comprehensive testing - instantly improving velocity and customer satisfaction. This signals innovation, reduces delivery overhead, and expands margins - helping PDOs win more deals instead of being commoditized.
Axentara was built for enterprises where compliance and governance are mandatory. Every Engineer’s output is traceable, auditable, and reviewable by humans. IT leaders retain full control over approvals while AI accelerates delivery cycles.
This foundation - built at Arkynis and now maintained within Metacube’s secure enterprise framework - ensures accountability, traceability, and regulatory compliance at scale.
