Route tickets, draft replies, detect sentiment, redact PII, and handle customers in 9 languages using 40+ LLMs through a single OpenAI-compatible API. No rewrites required.
Try Brainiall free for 7 daysCustomer support is one of the highest-leverage places to apply large language models. Tickets arrive in bursts. Agents face the same questions repeatedly. Tone mismatches between agent and customer escalate issues that could have been resolved in one message. Response time directly affects satisfaction scores, and satisfaction scores directly affect revenue.
LLMs address each of these pressure points. They can read an incoming ticket, classify its urgency, detect the customer's emotional state, draft a reply that matches your brand voice, and flag any personally identifiable information before the ticket even reaches a human agent. All of that happens in under two seconds at a cost measured in fractions of a cent per ticket.
The challenge has always been integration complexity. Most teams end up stitching together separate APIs for classification, sentiment, generation, and translation. Each vendor has its own SDK, its own billing, its own rate limits. Brainiall replaces that stack with one endpoint, one API key, and 104 models you can swap between without changing a line of code.
Support is not a single task. It is a pipeline: receive, classify, enrich, draft, review, send. Different steps in that pipeline call for different models. A fast, cheap model is fine for classification. A more capable reasoning model is better for drafting a complex refund policy explanation. Brainiall lets you assign the right model to each step without managing multiple vendor relationships.
The free NLP tier is especially useful here. Toxicity detection, sentiment analysis, PII detection, and language identification are all available at no cost. That means you can build a real-time triage layer that flags abusive messages, routes angry customers to senior agents, strips credit card numbers from ticket text, and detects whether the customer is writing in Portuguese or Arabic, all before you spend a single token on generation.
Best for drafting empathetic, nuanced replies to complex or emotionally charged tickets. Strong instruction-following keeps tone consistent with your brand guidelines.
Fast and cheap. Ideal for ticket classification, intent detection, and generating short acknowledgment messages at high volume.
Strong reasoning model. Use it when a ticket involves a policy edge case or a multi-step refund calculation that requires logical chain-of-thought before drafting a reply.
Open-weight model with solid multilingual performance. Good for high-volume triage in Spanish, Portuguese, and Indonesian where cost per ticket matters most.
Reliable for structured output tasks like extracting order numbers, product names, and complaint categories from unstructured ticket text.
Strong on multilingual content. Particularly useful when you serve customers in Arabic, Vietnamese, or Turkish and need replies that read naturally in those languages.
This prompt runs on every incoming ticket. Use Claude Haiku 4.6 for speed and low cost. The output is a structured JSON object your routing logic can consume directly.
SYSTEM:
You are a customer support triage assistant. Analyze the ticket below and return a JSON object with these exact fields:
- category: one of ["billing", "shipping", "technical", "returns", "general"]
- urgency: one of ["low", "medium", "high", "critical"]
- sentiment: a float between -1.0 (very negative) and 1.0 (very positive)
- summary: a single sentence describing the core issue
- suggested_team: one of ["tier1", "tier2", "billing_specialist", "escalation"]
Return only valid JSON. No explanation.
USER:
Hi, I ordered a laptop three weeks ago (order #48821) and it still hasn't arrived.
The tracking page has said "in transit" for 11 days. I need this for work and I've
already lost two client meetings because of this. Nobody from your team has responded
to my last two emails. This is completely unacceptable.A well-behaved model returns something like this:
{
"category": "shipping",
"urgency": "critical",
"sentiment": -0.87,
"summary": "Customer has not received order #48821 after 3 weeks; tracking stalled for 11 days; two prior emails ignored.",
"suggested_team": "escalation"
}That object goes directly into your routing logic. No parsing of free-form text, no regex on sentiment words. The model does the extraction; your code acts on the result.
This runs on tickets classified as shipping + high/critical urgency. Use Claude Sonnet 4.6. Pass the ticket text, the triage JSON, and a snippet of your tone guide in the system prompt.
SYSTEM:
You are a senior customer support agent at an electronics retailer.
Tone guide: direct, empathetic, no corporate jargon, always acknowledge the specific impact
the customer described, never use phrases like "we apologize for any inconvenience."
Offer a concrete next step in every reply. Keep replies under 180 words.
Context about this customer:
- Account tier: standard
- Order #48821 placed 2026-04-03, estimated delivery 2026-04-10
- Carrier: FedEx, last scan: Chicago IL, 2026-04-13
- Previous tickets: 2 unanswered emails sent 2026-04-14 and 2026-04-17
USER TICKET:
Hi, I ordered a laptop three weeks ago (order #48821) and it still hasn't arrived.
The tracking page has said "in transit" for 11 days. I need this for work and I've
already lost two client meetings because of this. Nobody from your team has responded
to my last two emails. This is completely unacceptable.A good Claude Sonnet 4.6 response acknowledges the missed client meetings by name, confirms the last known tracking scan, states exactly what action is being taken (carrier trace filed, resolution in 48 hours or replacement shipped), and gives a direct contact for follow-up. It does not use filler phrases or hedge with "we hope to resolve this soon."
When a ticket involves a refund request that sits at the edge of your return policy, DeepSeek R1's chain-of-thought reasoning produces more defensible answers than a pure generation model.
SYSTEM:
You are a support agent with access to the return policy below. Reason through whether
this request qualifies for a full refund, partial refund, or no refund. Show your
reasoning steps, then state your final recommendation and the exact reply to send.
Return policy summary:
- Full refund within 30 days if item is unopened
- Full refund within 14 days if item is opened but defective
- Store credit only for opened, non-defective items returned 15-30 days after purchase
- No returns after 30 days unless covered by manufacturer warranty
USER TICKET:
I bought a wireless keyboard 22 days ago. I opened it and used it for a week.
The left Shift key started double-registering keystrokes. I have a video of the issue.
Can I get a full refund?If you already use the OpenAI SDK anywhere in your stack, connecting to Brainiall is two lines of configuration. Everything else stays the same.
from openai import OpenAI
client = OpenAI(
base_url="https://api.brainiall.com/v1",
api_key="brnl-your-key-here" # get yours at app.brainiall.com/signup
)
def triage_ticket(ticket_text: str) -> dict:
response = client.chat.completions.create(
model="claude-haiku-4-6",
messages=[
{
"role": "system",
"content": (
"You are a customer support triage assistant. "
"Return a JSON object with fields: category, urgency, "
"sentiment (float -1 to 1), summary, suggested_team. "
"Return only valid JSON."
)
},
{"role": "user", "content": ticket_text}
],
temperature=0.1,
max_tokens=256
)
import json
return json.loads(response.choices[0].message.content)
def draft_reply(ticket_text: str, triage: dict, customer_context: str) -> str:
response = client.chat.completions.create(
model="claude-sonnet-4-6",
messages=[
{
"role": "system",
"content": (
"You are a senior support agent. "
"Tone: direct, empathetic, under 180 words, concrete next step. "
f"Customer context: {customer_context}"
)
},
{"role": "user", "content": ticket_text}
],
temperature=0.4,
max_tokens=512
)
return response.choices[0].message.content
# Example usage
ticket = "My order #48821 hasn't arrived in 3 weeks. I need it for work."
triage = triage_ticket(ticket)
print(triage)
# {"category": "shipping", "urgency": "critical", "sentiment": -0.82, ...}
if triage["urgency"] in ["high", "critical"]:
# Use a stronger model for high-stakes replies
reply = draft_reply(ticket, triage, "Account tier: standard, 2 prior emails ignored")
print(reply)
model parameter. No other code changes needed. Brainiall normalizes the response format across all 104 models so your downstream parsing always works.
| Capability | Single provider (e.g. OpenAI only) | Brainiall |
|---|---|---|
| Model choice per pipeline step | Locked to one family | 104 models, swap per request |
| Free NLP tier (sentiment, PII, toxicity) | Paid or separate vendor | Included free |
| Multilingual support | Varies by model | 9 languages, Qwen3 + Llama 4 strong coverage |
| LGPD + GDPR compliance | GDPR only (US-centric) | Both, deployed in US + Brazil |
| Audio: voice cloning + STT | Separate API required | XTTS v2 + Whisper + 54 TTS voices built in |
| Cost for Pro access | $20+/month per seat | R$29/month (~US$5.99) |
| SDK migration effort | N/A (baseline) | Zero: same OpenAI SDK, new base_url |
| Studio: compare 8 outputs at once | Not available | Yes, 1 prompt, 8 models simultaneously |
Teams often pick one model and route every ticket through it. This overloads an expensive model with classification tasks that a cheap, fast model handles just as well. Use Claude Haiku 4.6 or Llama 4 for triage, and reserve Claude Sonnet 4.6 or DeepSeek R1 for drafting replies to complex or high-urgency tickets. Your cost per ticket drops by 60-80% without any quality loss on the generation step.
If you log raw ticket text for analytics or fine-tuning, you will accumulate credit card numbers, passport numbers, and health information in your database. Use Brainiall's free PII detection endpoint on every ticket before it hits your log store. Strip or mask detected entities. This is not optional if you serve EU or Brazilian customers under GDPR or LGPD.
Classification and structured JSON extraction should use temperature 0.0 to 0.2. Higher temperatures introduce randomness that causes the model to occasionally return a different category for the same ticket text. Set temperature explicitly in every API call, especially for triage prompts where consistency is required for routing logic to work correctly.
AI-drafted replies for billing disputes, legal threats, or safety-related issues should always pass through a human agent before sending. Build a confidence score or urgency threshold into your pipeline. Tickets above the threshold get queued for human review; the AI draft is pre-loaded in the agent's compose window, saving time without removing human judgment from high-stakes interactions.
If your triage prompt asks for JSON and the model returns a markdown code block wrapping JSON, your parser will fail. Explicitly instruct the model to return only valid JSON with no surrounding text. Test with at least 50 real ticket samples before deploying. Add a fallback parser that strips markdown fences if present, and log any parse failures so you can improve the prompt over time.
Both. The API is stateless and works for any text input you send it. For live chat, you maintain the conversation history on your side and pass the full message array to the API on each turn, exactly as you would with the OpenAI chat completions endpoint. Response latency for Claude Haiku 4.6 is typically under 800ms for short messages, which is fast enough for real-time chat.
Brainiall supports 9 languages natively: Brazilian Portuguese (pt-BR), English, Spanish, Arabic, French, German, Indonesian, Turkish, and Vietnamese. The free NLP tier detects the customer's language automatically. Models like Qwen3 and Llama 4 perform particularly well on Arabic and Indonesian. You can instruct any model to reply in the detected language by including the language code in your system prompt.
Yes. Brainiall is deployed in both US and Brazil regions and is designed to meet LGPD and GDPR requirements. The free PII detection endpoint helps you identify and redact sensitive data before it is stored or processed by generation models. For Brazilian companies processing customer data under LGPD, you can configure your API calls to route through the Brazil region. Review the full compliance documentation at app.brainiall.com for data processing agreements.
Toxicity detection, sentiment analysis, PII detection, and language identification are all available on Brainiall's free tier with no monthly subscription required. You create an account at app.brainiall.com, generate a brnl-* API key, and call the NLP endpoints. There is no credit card required to access the free tier. Rate limits apply; see the API documentation for specifics. Generation tasks (drafting replies, classification using LLMs) require the Pro plan at R$29/month.
Yes. Brainiall includes Whisper speech-to-text for transcribing customer voicemails or call recordings, and neural TTS with 54 voices across 9 languages for generating audio responses. XTTS v2 voice cloning lets you create a custom voice from a 10-second audio sample, which is useful for maintaining a consistent brand voice across automated phone interactions. These features are accessible through the same API key and base URL as the text models.
The free tier covers NLP triage at no cost. The Pro plan at R$29/month unlocks all 40+ generation models. You can be running ticket classification and reply drafting in under an hour using your existing OpenAI SDK setup.
Get your free API key See pricing