45 minutes to 2.
Portkey's support team spent 45 minutes per ticket manually querying ClickHouse, Linear, Stripe, and GitHub to investigate production issues. After deploying Altor: 2 minutes. Across 200+ tickets. Zero changes to existing workflows.
Live investigation demo
Real investigation types from Portkey's support queue. Times shown are actual medians.
"Altor diagnosed in 2 minutes what used to take our engineers 45 minutes of copying data between tabs. Our tickets are investigations, not FAQs — nobody else could even attempt to answer them automatically."
The problem: every ticket was a 45-minute debugging session.
Portkey is an AI gateway platform handling billions of API requests from AI-first companies. Their customers are engineers — they don't file vague tickets. They report exact symptoms: "my p95 latency jumped 200ms," "my Llama 3 fallback stopped firing," "I'm getting 429s from the gateway."
These tickets cannot be answered from a knowledge base. Every one required Portkey's team to open ClickHouse, run queries against the customer's API logs, check Linear for known bugs, look at recent GitHub deploys, and verify billing in Stripe. One ticket. Six browser tabs. 20–45 minutes. Every time.
At Portkey's scale, investigation time was the single largest bottleneck in their support operation. Not response time. Not ticket routing. The investigation itself.
The deployment: 2 weeks from kickoff to production.
Mapped Portkey's ClickHouse schema, Linear project structure, Stripe billing setup, and GitHub deploy cadence. Identified the top 5 ticket types by volume.
Read-only connections established to all 6 systems. First investigations running on real tickets from Portkey's active queue.
Investigation logic refined against actual ticket patterns. By week 4, 80% of ticket types had reusable investigation playbooks. Median time: 2 minutes.
A real investigation
Rate limit regression. Diagnosed in 94 seconds.
Customer reports: "My API calls are returning 429s. This started about 2 hours ago." Altor receives the ticket and the customer's account ID. It runs the following in parallel:
429 error rate for this customer spiked from 12% to 43% at 09:14 UTC. Spike on a specific endpoint.
LIN-482 "rate limit regression on /v1/chat" — open, priority urgent, assigned.
Plan active, usage within limits. Not a billing-related rate limit.
PR #891 "fix/rate-limit" — currently in review, expected merge in 3 days.
Diagnosis — 94 seconds
Known regression LIN-482 causing elevated 429s on /v1/chat since 09:14 UTC. Patch in PR #891, ETA 3 days. Workaround: reduce concurrency or add exponential backoff. No billing issue involved.
The result.
After 200+ tickets diagnosed across all major ticket types, the investigation phase stopped being a bottleneck. Support agents receive a structured diagnosis before they finish reading the ticket. Engineers are no longer pulled in for routine investigations.
Your stack looks like Portkey's.
See what Altor finds in your queue.
We'll connect to your systems and run a live investigation on a real ticket during the call. Your data. Your stack. Diagnosed in real time.