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-1042Demo data

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.

01Hallucination

Copilot answers, still you double check

Since LLMs can hallucinate a fix, engineers have to verify it before they can apply the fix.

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

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.

New ticket

SUPP-1105

Admin account locked, EU

Tier
Enterprise
Version
3.8.1
Environment
Azure AD
Region
Europe
Symptoms
MFA reset loop
SymptomsMFA reset loopEnvironmentAzure ADEvidenceLogs, audit eventsDecisions2 recordedRunbooks4 referencedEngineers3 involvedRuled outPassword resetVersions3.8.1ResolutionGraph

  1. SUPP-0921

    94%

    MFA recovery, EU enterprise

    Ent3.8.1Azure ADMFA reset

    4 / 4 facets

  2. SUPP-0843

    81%

    Admin lockout, EU enterprise

    Ent3.8.1Azure ADLockout

    3 / 4 facets

  3. SUPP-0756

    72%

    MFA device lost, EU

    Ent3.7.4Azure ADMFA reset

    3 / 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

Live

Ticket 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

Live

Resolution 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

Live

Runbooks, procedures, and architecture docs surfaced in investigation.

Recent pages
  • Login Troubleshooting Guide
  • SSO Configuration
  • Auth Service Architecture
  • Known Issues: Auth

Space: Support Engineering

Salesforce

Live

Account context, entitlements, and customer environment data.

Account
Acme CorpActive
Plan
Enterprise
Case
00012345P1
Contact
Jane Cooper
reported byrefers tocitesresolvesinformsrelated toCustomerAcme CorpProductv2.4.1Evidence23 itemsResolutionValidatedRoot causeAuth configIncidentSUPP-1105

  • 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.

SREs watch your own production. Your metrics, your logs, your dashboards, everything under your control. An AI SRE is built for the environment you own, the one you can change freely.

Support works the opposite way. Vinaka is at home in the cases where you have little to no access to the customer’s environment: no root shell, no telemetry you control, just a ticket, some logs, and a version number. When you have to drive the investigation blind, matching against how this exact setup broke before is worth more than runtime telemetry. Vinaka is built for that.

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.

→ If that fits, grab a spot below.

Stop re-solving.
Start compounding.

The queue isn’t getting smaller.
Resolve the next one faster.