AI Suite
Build, fine-tune, and deploy intelligent workflows at scale.
All Systems Operational
#about.ai-console
v2.4.1
➜ #about.ai init --workspace="production"
✓ Workspace connected. Models ready.
➜ analyze sentiment("customer feedback batch_q3.csv")
Processing 14,203 records... Confidence: 98.2% | Latency: 42ms
➜ generate insights --format=pdf
✓ Report generated. Awaiting export...
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Core Capabilities
View Architecture →Neural Inference
Low-latency model serving with auto-scaling GPU clusters and dynamic batching.
p50 < 18msContinuous Fine-Tuning
Automated RLHF pipelines and drift detection to keep models aligned with your data.
Zero-configEnterprise Guardrails
Built-in toxicity filters, PII redaction, and compliance audit trails (SOC2, GDPR).
AuditableVector Search
High-dimensional similarity search with hybrid retrieval and semantic caching.
10B+ embeddingsProduction Use Cases
Case Studies →Intelligent Support
Deflect 68% of tier-1 tickets with context-aware RAG agents that access your knowledge base in real-time.
Predictive Analytics
Forecast churn, demand, and cash flow with custom-trained time-series models and anomaly detection.
Content Automation
Generate, localize, and optimize marketing copy at scale with brand voice consistency and SEO scoring.
API & Quick Start
Full Documentation →
POST /v1/completions
200 OK
// Initialize and stream response
import { Client } from "@#about/sdk";
const ai = new Client({ apiKey: process.env.ABOUT_KEY });
const stream = await ai.chat({
model: "#about-v2-turbo",
messages: [{ role: "user", content: "Summarize Q3 metrics" }],
temperature: 0.2,
max_tokens: 512
});
for (const chunk of stream) {
process.stdout.write(chunk.delta);
}