ai & machine learning

Technical customer support for AI platforms and model APIs

AI agents and technically trained specialists explain outputs, troubleshoot integrations, and handle API questions in 65+ languages — bridging the gap between complex ML products and everyday users.

Get a Quote See pricing

A programmer in a blue shirt coding on an iMac. Perfect for technology or work-related themes.

how it plays out

Real contacts, minute by minute.

Web Chat English

"The model keeps returning low-confidence predictions on our dataset"

Technical accuracy concern from ML engineer

AI asks for model version, dataset sample size, and feature configuration
Reviews prediction confidence distribution against expected baselines
Identifies data preprocessing mismatch between training and inference pipelines
Shares documentation link for schema alignment and offers to connect with ML support engineer

Root cause identified Engineering handoff prepared

Email Mandarin

"We're hitting rate limits on the API and our batch jobs are failing"

API throttling during production workload

AI checks account tier and current usage against rate limit thresholds
Identifies burst pattern from parallel batch requests exceeding concurrency cap
Recommends request queuing strategy with exponential backoff sample code
Initiates temporary rate limit increase while long-term plan upgrade is processed

Resolved automatically Batch jobs running again

Phone Korean

"We need to understand the pricing model before committing to enterprise"

Pre-sales technical and commercial evaluation

Agent explains token-based pricing model in Korean with usage examples
Estimates monthly cost based on customer's projected inference volume
Compares enterprise tier benefits against current pay-as-you-go usage
Sends customized pricing proposal and schedules technical architecture review

Proposal delivered same day Enterprise deal advanced

Slack Spanish

"Our fine-tuned model performance degraded after the latest platform update"

Regression after platform version change

AI cross-references customer model version with platform changelog
Identifies breaking change in tokenizer defaults affecting fine-tuned models
Provides config override to restore previous tokenizer behavior
Escalates to platform team to add migration guide for affected customers

Handled without escalation Performance restored

the team behind it

Faces, not a queue.

real members of the global team

ML-Trained Specialists

API integration support, rate limit troubleshooting, and SDK usage guidance
Model output interpretation, fine-tuning assistance, and performance debugging
Technical pre-sales consulting including pricing, architecture review, and POC planning

65+ Languages

Human agents providing support in 65+ languages
Real-time AI translation for rare language pairs
Cultural context training for every market we serve

Follow-the-Sun Coverage

Operation centres in Canada, Bolivia, Nicaragua, the Philippines and China
Smooth handoff between time zones with shared context
Holiday and weekend coverage without overtime costs

the part nobody prices in

Give engineering a usable support record

An API failure, a billing question and an unexpected model response need different investigations. Agree the categories your product team uses, then capture the project reference, model or API version, event time and error details permitted by your support policy. Account-access and usage questions can follow approved guidance; a reproducible product issue goes to the engineering owner with the steps already attempted.

Configure HeroDash custom fields for product version, enquiry category and escalation status. Cascading choices can distinguish authentication, rate limits, billing and model-output feedback. Reference-format checks can help agents capture the identifiers your team needs. A configured status trigger can tag a ticket and email its assigned agent when a reply is needed, with the execution recorded in the conversation timeline.

For a model-quality report, ask for the expected behaviour, observed result and a sanitized example that your policy permits. Keep API keys and sensitive prompts out of the support conversation. Product specialists decide whether the report requires a documentation change, configuration review or model investigation. The customer update should explain the confirmed finding, any approved workaround and who owns the next step.

what comes off the call

The AI product support record

Agree these fields during setup and keep them available to the team receiving the case.

Verified project or account reference
Model, API or product version
Occurrence time, error code and permitted request identifier

Expected behaviour, observed result and sanitized reproduction steps
Approved troubleshooting attempted and engineering owner
Confirmed finding, approved workaround and next customer update

Review the required fields and handoff owners during your support walkthrough.

questions

What buyers ask before they sign.

How much does it cost?

AI voice is US$0.15 per minute and web chat is US$0.15 per session. Live agents start at US$650 per month including 1,500 minutes — about US$0.43 per minute — or from US$5.80 per hour for a dedicated agent working only your account. The minimum engagement is half a seat, not a five-seat block.

How fast can you start answering our phones?

Five days is our normal window to staff a live account — that is five days to find and place an agent who fits your business, not five days of software setup. AI voice can go live sooner because there is no hiring step.

Do you answer in Spanish?

Yes, on the same number, with agents in our Bolivia and Nicaragua operation centres — native Spanish, not machine translation. English and Spanish share one queue, and we cover 60+ more languages if you need them.

Can I mix AI and live agents on one number?

That is the normal setup. You decide which call types go to AI and which reach a person, and you can change the split as your volume changes. Nothing about that decision is locked in at signup.

What should users provide for an API issue?

Your support policy should specify the permitted project reference, API or model version, occurrence time, error code and reproduction steps. Ask for a sanitized example where needed. Users should not paste API keys or sensitive prompts into the support conversation.

Who decides how a model-quality issue is resolved?

The product or engineering owner reviews the report and chooses the appropriate investigation. Support gathers the approved context and communicates confirmed findings or workarounds. The scope and access needed for that handoff are agreed during implementation.

also worth reading

start

Plan support around your AI product

Bring your API support guide and escalation categories to map the handoff from users to engineering.

Get a Quote