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.

Support Capacity Planning

Support capacity planning is the process of forecasting ticket volume, allocating agent headcount, and scheduling shifts to maintain SLA targets across expected demand patterns. It converts ticket demand into staffing and queue coverage decisions.

Why it matters for B2B support

B2B support demand is more predictable than most teams model: ticket volume spikes on Monday mornings, on release days, and in Q4 when enterprise customers are evaluating renewals. Teams that do not model those patterns over-staff low-volume windows and under-staff peak hours, which is why Monday P2 queues often break first. A 10% capacity shortfall during peak periods causes SLA breaches that take roughly 3× longer to recover from because backlog compounds across the day. In practice, the inputs usually live across Zendesk volume history, Salesforce account calendars, and product-release schedules, but few teams combine them into one planning model.

Key benchmarks

22%

average overstaffing cost in B2B support teams without demand-based capacity models

10%

capacity shortfall threshold that triggers compounding SLA breach cascades

3×

longer recovery time from capacity shortfall vs. the shortfall duration itself

$8,500

average monthly cost per misallocated FTE in US B2B support (salary + tools + overhead)

How Altor helps

Altor improves capacity planning by shortening the investigation portion of work, which lets teams model real throughput instead of guessing at manual diagnostic time. It also exposes which ticket categories create the largest hidden capacity drain.

FAQ

What signals should B2B teams use for capacity planning?

Use historical contact volume, release calendars, onboarding cohorts, renewal seasonality, and known incident windows. Relying only on average weekly volume hides the spikes that actually breach SLA.

Why is a small shortfall so damaging?

Because ticket queues compound. If the team is 10% short during a peak hour, unresolved work carries forward and slows every subsequent ticket, turning a short gap into a multi-day backlog.

How often should capacity models be refreshed?

Weekly for queue-level forecasting and monthly for headcount planning. Any team shipping frequently should also re-run plans before major product releases.

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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