New ticket
SUPP-1105
Admin account locked, EU
- Tier
- Enterprise
- Version
- 3.8.1
- Environment
- Azure AD
- Region
- Europe
- Symptoms
- MFA reset loop
The investigation platformfor B2B support teams
Investigate incidents collaboratively, preserve every decision, and let AI recommend the next best action using your historical resolutions.
vinaka.ai / console / SUPP-2088Demo data
SUPP-1847SUPP-2088×SUPP-1977SUPP-1866
SUPP-2088P10h 55mEnterpriseUS-WESTCreated: 2026-07-26
On-Prem upgrade failed at migration step 7/12: foreign-key conflict
CUSTOMERGLGlobal Logistics Inc.ASSIGNEEPSPriya Sharma
StatusIn ProgressUpdated 2 days agoEscalateResolve
AI AnalysisEvidence: Strong
Global Logistics' v6.2.1 upgrade failed at the carrier-integration FK migration because 1.2M historical orders have NULL carrier_id under a custom schema. The team is deciding between a TMS backfill fix-forward and a rollback before the maintenance window closes.
•Root cause confirmed as a schema/data mismatch, not corruption. The customer has a viable TMS backfill source; a snapshot dry-run is pending before resuming the migration.
Recommendation: Validate the TMS export on a snapshot, run a conditional backfill for NULL carrier_id rows, then resume migration step 7/12 with Release Engineering sign-off
2 Recommended ActionsSources:SUPP-2019: schema-aware migration patchCustom Schema Migration Playbook
Confidence: 89%, grounded in 2 cited sources for this ticket
Description
Investigation & EvidenceResolution Path
Open Questions(0)
Recommended Actions(2)
Step 1 of 2 · Validate before touching production
Dry-run the TMS backfill on a restored snapshot
migrationdata
PSPriya Sharma
Confidence: 88%Why?
Sources:KB-0201SUPP-2019
Risk AssessmentLow risk
The state of support, 2026
AI ships the bugs.
Support cleans them up.
Engineers ship more code than ever. Your queue feels it. Your wiki doesn’t.
01Scattered
The answer’s in five tabs.
Symptoms in Jira, the thread in Slack, the fix in a Confluence doc no one can find. Every ticket starts with a scavenger hunt.
02Re-solving
It’s been solved. Three times already.
Same error, three engineers, three weeks apart. The fix sits in a closed ticket, and the senior who wrote it just left.
03Breaching
One ticket. One breach. One customer on the edge.
A single P1 that someone solved last sprint, now ticking past the SLA line while a new engineer starts from zero. The queue isn’t just full, it’s costing you the account.
Three gaps. One pattern: tickets get solved, then buried. Make the last close the first place the next engineer looks.
The Platform
One platform for all investigationsFrom first alert to final resolution.
Follow one P0 from discovery through collaboration, tracking, and the customer reply in a single console.
Now showing: Investigate: AI reconstructs the issue and surfaces historical context: findings, similar incidents, and a recommended approach before the queue opens.
vinaka.ai / ticket / SUPP-1042
SUPP-1042P0-3h 25mEnterprise
E413 timeout errors on checkout API
ACAcme CorporationIn Progress
The AI investigates before you open the ticket. Findings, confidence, and next steps, ready on arrival.
Ticketing platforms are optimized for tracking issues,Vinaka is optimized for resolving them.
The engine
Your wiki stores documents.Vinaka remembers investigations.
Every closed ticket compounds into structured investigation memory: evidence, environment, decisions, and outcomes. Future tickets are matched against investigation state rather than keywords.
SUPP-0921
94%MFA recovery, EU enterprise
Ent3.8.1Azure ADMFA reset4 / 4 facets
SUPP-0843
81%Admin lockout, EU enterprise
Ent3.8.1Azure ADLockout3 / 4 facets
SUPP-0756
72%MFA device lost, EU
Ent3.7.4Azure ADMFA reset3 / 4 facets
Recommended
94%
High confidence
Next best action
Guide the user through MFA recovery via the Azure AD break-glass flow, then re-issue the admin invite.
- 3 sources cited
- 7 successful resolutions
- Decision record DR-0921
Every resolution strengthens the graph.
Better matches. Higher confidence. Over time.
Integrations
Plugs into your stack.
Replies go out from here.
Your team investigates, escalates, and sends customer replies from Vinaka.
Jira
LiveTicket history, comments, and engineering context for matching and escalation.
Recent issues
- SUPP-1105Admin locked outP1
- SUPP-1043SSO failure on loginP2
- BUG-7823Token refresh errorP3
Project: Support
Slack
LiveResolution patterns and tribal knowledge from the threads where fixes actually happen.
# support-eng12 new
Sam10:21
Users unable to login after password reset
Priya10:23
Looks like auth service is failing in EU region
Alex10:24
Investigating the logs now
Confluence
LiveRunbooks, procedures, and architecture docs surfaced in investigation.
Recent pages
- Login Troubleshooting Guide
- SSO Configuration
- Auth Service Architecture
- Known Issues: Auth
Space: Support Engineering
Salesforce
LiveAccount context, entitlements, and customer environment data.
- Account
- Acme CorpActive
- Plan
- Enterprise
- Case
- 00012345P1
- Contact
- Jane Cooper
Connectors
plugin your sources
Live sync
Continuously indexed
Scoped OAuth
Read-write access
Zero training
On your data
+ Zendesk, ServiceNow, PagerDuty planned · Request an integration →
FAQ
Hard questions,
straight answers.
For B2B support teams, L1 and L2 through TSE. Not a chatbot. Not an AI SRE.
No. RAG over docs only knows what someone wrote down, and your hardest tickets get resolved in Slack threads, Jira comments, and engineers’ heads, not in Confluence.
Vinaka parses every closed ticket into a structured Resolution Graph: symptoms, environment, steps tried, what was ruled out, what worked. New tickets are matched by subgraph alignment over that graph, with a learned ranker over tier, version, and config, not by keyword similarity over your wiki. Different architecture, different answers.
Generic copilots match on text similarity from public training data. Workplace search matches on a flat index of your docs. Help-desk AI generates replies from KB articles. None of them model the investigation.
Vinaka matches on investigation state: what’s been tried, what’s been ruled out, the customer’s tier, version, and config. Same error at minute zero and at hour two return different answers, because they should.
Four workflows in one workspace: Investigate, Collaborate, Track, Reply. Investigate matches new tickets and, in Phase 2, can run proactive analysis before your queue opens. Ask handles follow-ups scoped to the ticket. Escalate packages context for engineering. Reply drafts and sends cited customer responses.
Customer replies are drafted, reviewed, and sent from Vinaka today. Source-system write-back remains opt-in and on the roadmap.
Read-only OAuth to Jira, Confluence, Slack, and Salesforce by default. Today: Jira, Confluence, Slack, and Salesforce. You choose what gets indexed. Permissions follow your existing access controls. We never train on your data.
Customer replies are drafted, reviewed, and sent from Vinaka today. Source-system write-back remains opt-in and on the roadmap. Vinaka suggests. Your engineer decides. No autonomous actions on your systems. Every suggestion links to a specific source. Confidence scores reflect evidence weight, not optimism.
A RAG pipeline over Confluence is a weekend’s task. We’ve watched a dozen teams ship one. The thing that breaks, every time, is that wiki retrieval doesn’t model investigations.
The hard part is everything else: parsing tickets into structured investigation graphs, subgraph alignment with a learned ranker over tier/version/config, credibility scoring per record, permission-aware retrieval, calibrated confidence, and a self-learning loop that doesn’t drift. That’s the year of focused engineering. You’d be rebuilding the engine, not the wrapper.
Indexing your first product area (one queue, one product, ~6 months of history) takes hours, not weeks. Most teams feel the graph start to pay back after the first few dozen indexed tickets.
When a senior engineer leaves, their structured records stay: environment, decisions, and what was ruled out, with credibility scores and source links intact. Their replacement onboards against a graph, not a wiki.
The product is built and running on real data. We’re selecting our first design partners now: direct access to the engineers building it, and real influence on the roadmap. No public pricing yet; that lands with general availability.
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