Your support,
on autopilot.
cognilogs reads every ticket from your ticketing system, does the work behind it, and either resolves it or gives your team a case with the evidence already assembled. It runs on your servers or inside your network, with the local model you choose.
Any ticketing system. Any local model. A person can still take over.
A preview of the support chat
Evidence, confidence, and next action in one place
Works with your stack.
Any ticketing system. The evidence and action tools your team already checks.
Your ticketing system stays the system of record. See the stack
The reply is the visible part.
The answer is what the customer sees. The work that makes it true happens in the logs, the code, the database, and the approval trail behind it.
cognilogs is being built with early teams who care about the tickets that need a real investigation, not another draft. Read how a ticket moves through the loop.
What the customer sees
“The certificate had rotated before the new metadata was published. I restored it, paused the rollout, and corrected the cohort. Your finance team can sign in again. The RCA is attached.”
Clear, specific, and backed by the checks behind it.
What it takes to get there
Evidence, judgment, and a person who can sign off.
“Zero avoidable handoffs.”
Runs inside your network.
The first cognilogs deployments are local. The app, the SLA worker, and the run history stay on your servers or inside your network. You choose the local model; stronger models produce better work. The deployment and operations guide covers what runs where, and the vendor comparison shows how that differs from cloud support AI.
cognilogs app
Runs on your host
Ticket + run data
Stays in your environment
SLA worker
Checks every 15 minutes
Local model
Your choice of model
Security review
What local deployment changes.
Local deployment puts the host, network, credentials, and retention policy under your team's control. Your security team gets deployment documentation and review support.
- Ticket content and run evidence stay close.
- Your security team gets deployment documentation and a direct review.
- Audit logs cover model actions, tool calls, and approvals.
Know when to stop.
A confident answer is useful. A confident guess can cost more than a slow one. The confidence gate decides whether cognilogs replies or sends the case to a person with a private note.
No silent guesses.
Below the line, nothing reaches the customer.
A complete handoff.
The run, sources, and suggested next step travel with the case.
A line you control.
The default is 0.85. Your team can make it stricter.
Review threshold
The evidence score stays fixed. Move the line past 0.92 to see the handoff.
SSO loop after certificate rotation
Audit event matched
Cohort query verified
Metadata fix checked
Auto-reply + resolve
The evidence clears the line. cognilogs sends the reply and moves the ticket to resolved.
Questions, answered.
The short version of how the loop works, what it checks, and where a person stays in charge.
Read the product guideWhat does cognilogs automate?
The work around a support ticket: polling your ticketing system, sanitizing the content, classifying the request, retrieving knowledge, investigating issues across logs, code, databases, and observability tools, enabling features with approval, drafting RCAs, watching child-ticket SLAs, and following up with CSAT.
Which ticketing systems does it support?
cognilogs is built to poll any ticketing system. Zendesk, Intercom, Jira Service Management, HubSpot, and Salesforce are examples. Your ticketing system stays the system of record.
Do we have to replace our support team?
No. The agent takes the work that can be verified and leaves the judgment calls to a person. A person can review, override, or take over at any point. RCAs always receive human review before they are sent.
Where does it run?
The first deployments are local. The app, the SLA worker, and the run history run on your servers or inside your network. You choose the model, including local and open-weight models. Stronger models produce better work; the evidence and approval rules stay the same.
What happens when confidence is low?
The customer does not get an automated reply. cognilogs posts a private note with the run, evidence, and suggested next step, and assigns the case to a human queue. The default threshold is 0.85.
How does it investigate an issue?
It reads audit logs, searches the codebase, runs read-only database queries, and checks Kibana, Sentry, or Datadog. If the fix is in scope, it verifies the change before replying. If it is not, it opens a child ticket with the evidence attached.
Can it enable a feature for a customer?
Yes, when the workflow allows it. It can use an admin API or browser automation to enable a feature on your internal panel or the customer portal. Sensitive changes can wait for an approver before the agent acts and replies.
How does SLA monitoring work?
Every child ticket enters an SLA tracker by severity and priority. The owner is notified by Slack or email, and a manager is brought in after three notifications without movement. When the child is resolved, cognilogs checks whether the answer can be shared with the customer and drafts the reply.
What does it cost?
Early customers can get a ticket analyzed for as low as ₹10. The final price depends on the local model, infrastructure, and the depth of each analysis. You own the model and infrastructure costs.
How do we get early access?
Join the waitlist or email team@cognilogs.com. We are opening local deployments in small groups so each loop is proven before the next one.
Early access · local deployment
Put the queue on autopilot.
cognilogs is opening a small group of local deployments for teams with tickets that need real investigation. Tell us where the work gets stuck.
A ticket, handled end to end
Live loopTicket in
any system
Agent run
local model
Evidence
sources attached
Human review
when needed
Request early access
One email. A real reply from the team.
A local deployment pilot
Shape the paths and approvals
A direct line to the team
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