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AscendCore
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Jira Service Desk
AscendCore is agentic AI for the L1 IT help desk that resolves tickets, not just chats about them.
Two properties define it. It is deterministic: every request runs a versioned, auditable runbook held in source control, never free-form model output. And it is approval-first: nothing touches a system without explicit human approval, and every decision is written to a SHA-256 tamper-evident audit chain the customer can export as compliance evidence and verify independently.
The problem. Mid-market IT teams lose a large share of engineer time to repeatable Tier-1 work: MFA and password resets, account unlocks, license and group changes, VPN access, provisioning. Password resets alone are 20-50% of service-desk volume (Gartner). ITSM platforms catalog those tickets. Chat-based AI assistants deflect them. Neither executes the change in Okta, Entra ID, M365, Intune or ServiceNow. The work happens in the systems, not the conversation.
Shipped today. 31 production runbooks across identity, access, offboarding and provisioning. Natural-language triage in Slack and Microsoft Teams at parity, so approvals land on an interactive card where the team already works. One-click chain verification, governance dashboards and compliance exports. Orchestration across Okta, Entra ID, M365, Intune, Jira Service Management, Confluence and ServiceNow. Customer API v1 with a public OpenAPI 3.1 spec, admin SSO with IdP-mediated MFA, and customer plus MSP partner portals.
Extending into a governed action plane. An approval-gated Agent Gateway, where AI agents propose runbook actions for a human to approve, is live. Access review campaigns with exportable evidence bundles and a license-reclaim savings ledger are in production. Alert-to-remediation Signals is in development.
Built for procurement-grade environments, where AI sales cycles usually die.
Core from $4/user/month. 30-day pilot, no credit card. Live demo, no signup: ascendcore.ai/demo/dashboard/governance
Logseq AscendCoreAscendCore's answer:
AscendCore is approval-first: the AI never executes anything. It classifies intent, then a named human approves or denies on an interactive card in Slack or Microsoft Teams, and only then does a deterministic runbook run. The classifier holds no credentials and has no execution path.
Every approval and execution is appended to a SHA-256 hash chain in which each record contains the prior record's hash. Customers can export that chain and re-hash it offline to prove nothing was altered. Independent customer verification of the audit trail is uncommon in this category, and it is the part security and audit reviewers care most about.
The runbooks themselves are deterministic TypeScript orchestrators rather than generated output, so the same request produces the same sequence of API calls every time.
AscendCore's answer:
Three things you can verify before you talk to anyone:
Published pricing. Core is $4 per user per month and Professional is $8, listed publicly at ascendcore.ai/pricing. No call required to see a number.
Days, not months. Self-serve onboarding with no certified-partner engagement. Observe mode can be live in under 2 hours, and a 30-day pilot is included with no credit card.
Governance you can check yourself. Open the live demo at ascendcore.ai/demo/dashboard/governance without signing up, click Verify, and watch the audit chain re-hash from genesis.
Honest scope: AscendCore is not a full ITSM system of record. There is no CMDB and no complete ITIL suite. If you need those, keep your platform and run AscendCore alongside it as the approval-first action and orchestration layer.
AscendCore's answer:
Mid-market IT teams, roughly 500 to 3,000 employees, running a modern identity and endpoint stack (Okta or Microsoft Entra ID, Microsoft 365, Intune) and absorbing high L1 ticket volume without matching headcount growth.
Typical buyers are IT directors, IT operations managers and CISOs who need automation that can pass an audit, not automation that acts on its own.
The second audience is MSPs, VARs and systems integrators running L1 queues on behalf of their clients, who want to automate that work without replacing the ITSM platform each client already runs.
AscendCore's answer:
AscendCore was founded in 2026 in Pittsburgh, Pennsylvania, and incorporated as a Delaware C-Corporation.
Founder Jacob Kelly spent a decade on the go-to-market side of enterprise IT services, sitting in the same buyer conversation over and over. One pattern kept surfacing: a large share of service-desk volume is a short list of repetitive identity and access requests, and the teams handling them were not short on intent to automate. They were short on a way to automate that their own security and audit reviewers would sign off on.
Most tools failed that review the same way. They either asked the customer to hand execution authority to a model, or they produced no evidence a reviewer could independently verify. AscendCore was built from the opposite constraint: assume every action must be approved by a named human and provable afterward, then make that path fast enough to be worth using.
AscendCore's answer:
TypeScript end to end. The application is Next.js, deployed on Netlify's US edge, with Postgres (Neon) backing the audit chain and operational data.
Runbooks are deterministic TypeScript orchestrators held in version control rather than model-generated steps, so execution is repeatable and reviewable.
Intent classification uses a hosted large language model confined to one job: turning a natural-language request into a structured intent. It holds no credentials and has no execution path.
Integrations are direct API connectors to Okta, Microsoft Entra ID, Microsoft 365, Intune, Slack, Microsoft Teams, Jira Service Management, Confluence and ServiceNow. Inbound webhooks are verified with HMAC-SHA256 for Slack and JWT validation against the Microsoft Bot Framework JWKS for Teams.
Based on our record, Logseq seems to be more popular. It has been mentiond 300 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Using Logseq[1][2] for years and quite happy with it, especially since it has apps for tablets/iPad and phones as well, while being completely FOSS. [1] https://logseq.com/ [2] https://github.com/logseq/logseq. - Source: Hacker News / 19 days ago
Choose a local Markdown tool like Obsidian, Logseq, Foam, or Tolaria to store all your knowledge as plain .md files you own and control. - Source: dev.to / 3 months ago
I should call out another thing that convinced me was a user of forgetful (twsta) posted in the discord a skill for managing wok and todos from how they used to use Logseq. - Source: dev.to / 5 months ago
The Zettelkasten method is a knowledge management system that helps organise ideas effectively. I believe this system would work well for myself, so I have been looking at applications such a Logseq and Zettlr as a result. I am currently using a Wiki-style solution in Zim, however. - Source: dev.to / 7 months ago
I am a fan of Logseq [0] as well, although itโs slightly different in that it is mostly for bulleted notes and not long-form prose. [0]: https://logseq.com/. - Source: Hacker News / 10 months ago
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