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.

Queue Depth

Queue depth is the number of tickets currently open and awaiting agent handling, measured at a point in time or as an average over a period. It is a leading indicator of whether the team is about to miss service targets.

Why it matters for B2B support

Queue depth predicts SLA breaches before customers feel them. If a team is processing 12 tickets per hour while 30 tickets per hour are arriving, a queue depth of 40 can swell past 160 in roughly 3 hours. Teams that watch queue depth in real time can trigger surge responses like bringing in off-shift agents, pausing non-support work, or fast-routing simple tickets before the backlog cascades. In B2B SaaS, queue-depth spikes above 3× baseline are one of the most reliable indicators of a product release issue or emerging P1 incident.

Key benchmarks

3×

queue depth spike threshold that reliably predicts P1 incident or product surge

average tickets processed per agent per hour in B2B technical support

68%

of B2B SLA breaches preceded by a queue depth spike > 2× baseline within 90 minutes

25 tickets

maximum queue depth target for maintaining P1 SLA in 10-agent B2B teams

How Altor helps

Altor lowers queue depth by accelerating the diagnosis step that keeps technical tickets sitting in open status. Faster investigation means tickets leave the queue instead of aging inside it.

FAQ

Why is queue depth a better early-warning signal than SLA breach count?

Because queue depth moves before SLA timers expire. Once you are counting breaches, the operational problem has already reached the customer.

How often should queue depth be monitored?

Continuously during support hours and at least every 15 minutes during releases or incidents. Real-time visibility is what makes queue depth useful operationally.

What should a team do when queue depth spikes?

Trigger surge staffing, pause non-essential meetings, and prioritize technical diagnosis on the oldest or highest-risk tickets first. The goal is to stop compounding backlog, not just work harder.

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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