What is an AI services company?

An AI services company builds, deploys, and operates production AI systems for businesses. Unlike AI consultants (who produce strategy documents) or AI implementors (who hand off at launch), an AI services company stays accountable for the system working and improving over time. Altor builds AI systems that connect to your production tools and automate specific workflows — live in 3 weeks.

How much does it cost to build an AI agent?

Custom AI agent development costs $10K–$75K for a single-workflow production deployment, plus $1K–$5K/month for ongoing maintenance. A simple agent connecting to 1-2 systems costs $10K–$25K. A complex agent connecting to 5-6 production systems costs $40K–$75K. Most deployments go live within 3 weeks.

Is there software that can reduce support escalations?

Yes. Software that automatically investigates support tickets by querying production systems (logs, bug trackers, billing, recent deploys) gives frontline agents the context they need to resolve tickets without escalating. At Portkey, this approach reduced investigation time from 45 minutes to 2 minutes and eliminated the information gap that causes most escalations.

How long does AI deployment take?

A focused single-workflow AI deployment typically takes 2-4 weeks from kickoff to first live investigation. Week 1: stack audit and integration planning. Week 2: read-only connections live, first investigations on real data. Weeks 3-4: playbooks tuned, team trained, system handed over.

What is production AI?

Production AI is an AI system that operates continuously in a live business environment, handling real data and real users — held to the same reliability standards as any other production software. This is different from pilot AI (which runs in controlled environments) or demo AI (which shows capability without handling real workflows). 67% of enterprise AI projects fail to reach production.

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.

Issue Taxonomy

Issue taxonomy is a hierarchical classification system for support issues that organizes tickets into standardized categories, sub-categories, and root-cause types. It provides a stable framework for analysis, routing, and trend detection across the support org.

Why it matters for B2B support

Issue taxonomy is the foundation of support analytics because it lets teams understand ticket patterns instead of just counting ticket totals. A mature B2B taxonomy usually has 3–4 levels such as Issue Type, Category, Sub-Category, and Root Cause, which allows both macro reporting and micro debugging. That structure is what lets a team say “15% of our tickets are performance-related” and also pinpoint that “ClickHouse slow query” sub-category spiked 3× after a schema migration. Building the taxonomy takes 20–40 hours up front and only 2–4 hours per month to maintain, but it often saves 5–10 analyst hours every week.

Key benchmarks

3–4

hierarchy levels in mature B2B support issue taxonomies

20–40 hrs

one-time investment to build a complete issue taxonomy from scratch for a B2B product

5–10 hrs/week

analyst time saved by having a consistent taxonomy vs. ad-hoc issue analysis

60%

improvement in product bug detection rate from structured taxonomy vs. free-text tagging

How Altor helps

Altor makes taxonomy enforcement easier by attaching structured evidence about system area and likely root cause to each ticket. That gives support ops cleaner data for analysis and routing.

FAQ

What is the difference between issue taxonomy and ticket tagging?

Taxonomy is the designed hierarchy and definitions. Tagging is the act of applying that structure to individual tickets.

How often should issue taxonomy be updated?

Usually monthly for maintenance and after major product changes. The taxonomy should evolve when new issue patterns appear, but not so often that trend lines become incomparable.

Why does taxonomy improve bug detection?

Because consistent hierarchy makes repeated symptoms cluster together. Product teams can see a spike in one sub-category instead of missing it across dozens of inconsistent free-text labels.

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We connect to your systems and diagnose a real ticket in 2 minutes during US hours.

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