Enterprise integrations, MLOps, deployment, and support

Which audiences and customer segments does AGI, Inc. target?

AGI, Inc. targets end users, knowledge workers, enterprises, product teams, developers, researchers, merchants, app owners/platforms, AI product companies, startups/entrepreneurs, and everyday consumers.

Which specific buyer personas or ICPs are identified for AGI, Inc.'s offerings?

Identified ICPs include merchants (e‑commerce/retail), app owners and platforms, developers, AI product companies, enterprises/IT organizations, research labs, entrepreneurs, and general consumers.

What integrations and environment types does AGI list across its products?

AGI lists integrations with websites and web apps, e‑commerce/payment flows, mobile apps on Android and iOS, desktop applications (e.g., Excel, PowerPoint), browsers via remote VMs, internal enterprise web tools, and third‑party model providers.

What enterprise LLM integration workflows does AGI describe?

AGI describes a validated enterprise LLM integration workflow including discovery and use‑case scoping, data inventory and cleansing, a decision between RAG or fine‑tuning, proof‑of‑concepts with evaluation metrics, secure deployment, and production monitoring with feedback loops (ICP: enterprise IT, product teams, head of AI).

What MLOps and model lifecycle workflows does AGI describe?

AGI describes an MLOps pipeline covering experiment tracking, reproducible environments, automated testing, model packaging (container + schema), CI/CD for model promotions, and rollback/canary controls to reduce release risk (ICP: data science/MLOps teams, platform engineering).

What low‑resource fine‑tuning approach does AGI reference?

AGI references low‑resource fine‑tuning workflows using parameter‑efficient techniques (PEFT / LoRA), including pretrain assessment, adapter selection, small‑batch fine‑tuning, validation on held‑out slices, and cost‑effective deployment (ICP: ML engineers and applied NLP teams).

What secure model serving and rollout practices does AGI recommend?

AGI describes secure serving practices including containerized inference, TLS and authentication, observability, rate limiting, staged canary/A‑B rollouts with automated metrics gating, and automatic fallback to baseline models (ICP: platform engineering, DevOps, security teams).

What data labeling and quality assurance pipeline does AGI describe?

AGI describes an end‑to‑end annotation workflow covering schema design, human‑in‑the‑loop labeling, label review and adjudication, inter‑annotator agreement monitoring, and automated QA to ensure high‑quality training data (ICP: data ops, annotation managers, ML teams).

What model monitoring and drift detection capabilities does AGI describe?

AGI describes monitoring workflows that include input/output telemetry, performance and fairness metrics, concept/data drift detection, alerting, and periodic re‑evaluation and retraining triggers (ICP: MLOps teams, observability engineers, reliability teams).

What privacy‑preserving ML techniques does AGI reference?

AGI references privacy‑first model development practices such as data minimization, differential privacy during training, and secure or encrypted inference where needed, together with privacy audits and documentation (ICP: privacy officers, ML teams, legal & compliance).

What AI governance and compliance workflows does AGI outline?

AGI outlines governance workflows including model inventory, risk assessment, documentation and audit trails, impact/fairness assessments, stakeholder sign‑offs, and policy mapping to standards and regulations (ICP: executives, compliance officers, risk & legal teams).

What enterprise SLAs, uptime guarantees, and support tiers does AGI offer?

Enterprise and scale customers receive custom pricing and premium support, with SLAs and uptime guarantees provided as part of negotiated contracts; starter plans are self‑serve with included quotas.

What developer, community, and enterprise support channels are available?

Developers can use the open‑source AGI SDK on GitHub and community channels for self‑help, while paying customers access API documentation and starter quotas; enterprise customers receive premium, sales‑driven support (contact partner@theagi.company).

How can I contribute to or extend the AGI SDK and REAL Bench?

The SDK and REAL Bench are hosted on GitHub under Apache‑2.0; contributors can open issues and pull requests, run the evaluation harness locally, and submit reproducible results to the public REAL Bench leaderboard.

What logging, audit trail, and monitoring features are available for enterprise customers?

AGI implements audit logging, role‑based access controls, and observability for model telemetry and security; enterprise plans can include enhanced logging, retention policies, and compliance evidence upon request.