How is Altor different from a docs chatbot?

Docs chatbots answer 'how does this work?' from your knowledge base. Altor answers 'why is this broken for this customer right now?' by querying their actual API logs, checking your bug tracker, and verifying their billing status — across 6+ systems in under 2 minutes.

How is Altor different from our support platform?

Pylon, Plain, Zendesk — they route and manage tickets. Altor investigates them. We plug into your existing support tool as the investigation layer. You keep your workflows, we add the diagnosis.

Will Altor take actions without asking?

Not by default. Altor starts as a read-only investigator — it surfaces a diagnosis for your team to review. You control which action types graduate to auto-approval. Destructive actions are never automated.

What if our stack isn't listed?

If an API exists for your system, we can integrate it. The architecture composes tools — it doesn't hardcode connectors. We've yet to encounter a B2B stack we can't connect to.

What does Altor pricing look like?

Usage-based — you pay per investigation, not per seat. Pricing is quoted in USD ($) with no minimum commitment. We'll scope pricing during the demo based on your ticket volume and systems.

Data infrastructure tickets are always multi-system investigations.

When customers report pipeline failures, slow queries, or connector errors, the root cause spans your query engine, connector framework, scheduling system, and their source/destination configs. Altor investigates all of them simultaneously.

Data infrastructure tickets that take 30+ minutes each

These are the tickets that sit in queue until an engineer has time to investigate:

•"Our nightly sync to Snowflake failed with a schema mismatch" — source schema changed, connector type mapping issue, or a Snowflake DDL permission error?
•"Query performance degraded 10x after upgrading to your v3.0" — query plan regression, missing index rebuild step, or new memory allocation defaults?
•"CDC pipeline stopped capturing deletes from our Postgres source" — WAL level setting, replication slot full, or a connector bug with logical decoding?
•"Scheduled job runs but outputs zero rows — it was working fine last week" — upstream table renamed, partition filter mismatch, or credential rotation?

What Altor investigates for data infra teams

Altor connects to the systems where your investigation data actually lives:

•Query engine logs — execution plans, memory usage, partition pruning effectiveness, timeout traces
•Connector framework — sync history, schema evolution tracking, error patterns by source type
•Scheduler — job run history, dependency chains, resource contention, retry outcomes
•Customer configuration — source/destination credentials, schema mappings, transformation rules
•Linear / Jira — known connector bugs, version-specific regressions, migration blockers

40 min

average pipeline failure investigation time

< 3 min

Altor's investigation with full dependency trace

systems checked per data infrastructure ticket

70%

of pipeline tickets follow repeatable investigation patterns

"Our tickets are investigations, not FAQs. Nobody else could even attempt to answer them automatically. Altor can because it queries our actual production data."
— Engineering lead, Portkey

Why data infrastructure is ideal for Altor

Data infrastructure companies have deeply structured operational data — query logs, sync histories, schema evolution trails — that Altor can query precisely. Every pipeline failure follows a diagnostic tree: check the source, check the connector, check the destination, check the schedule.

Your support engineers run this same diagnostic tree manually, 30-40 minutes per ticket. Altor automates the entire tree and delivers a root cause with evidence.

Altor for ClickHouse teams Altor for AI infrastructure Portkey case study

See Altor investigate a real ticket from your queue

We'll connect to your systems and run a live investigation. Your data, your ticket, diagnosed in 2 minutes during EST or PST hours.

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