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.

Ticket Tagging

Ticket tagging is the practice of applying structured labels to tickets so they can be categorized by topic, product area, root cause, and resolution type. Consistent tagging is what turns ticket history into usable operational data.

Why it matters for B2B support

Ticket-tagging quality directly determines the quality of support analytics. A team with inconsistent tags produces noisy trend reports where “API errors,” “integration failures,” and “webhook issues” may all describe the same root problem with different labels. Best-in-class B2B support organizations maintain a taxonomy of 50–100 tags, define each one clearly, auto-suggest tags inside Zendesk or Salesforce, and audit accuracy monthly. Without that discipline, a bug affecting 200 customers can be scattered across 40 differently tagged tickets and never appear as an obvious pattern.

Key benchmarks

50–100

tags in best-in-class B2B support taxonomies (specific enough to be useful, few enough to be consistent)

200

customers affected by bugs that commonly go undetected due to inconsistent ticket tagging

40%

of support tags applied incorrectly in teams without tag definitions and enforcement

3.1×

more product bugs caught per month in teams with consistent ticket tagging vs. ad-hoc tagging

How Altor helps

Altor helps agents apply more accurate tags by surfacing likely root cause, system area, and resolution type from the investigation itself. That reduces free-text guesswork and improves downstream reporting.

FAQ

How many tags should a B2B support team maintain?

Usually 50–100 core tags is enough to be specific without becoming inconsistent. Fewer tags lose signal; too many make accuracy collapse.

What makes ticket tagging fail?

Undefined tag meanings, no QA on tagging accuracy, and forcing agents to choose tags before they understand root cause. Those conditions produce analytics that look precise but are wrong.

See Altor investigate a real ticket

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

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