ModelRefs / AI SDR — Architecture Blueprint
AI SDR — Architecture Blueprint
Production architecture blueprint for AI SDR: components, deployment patterns, cost & latency optimization, security, observability, and the production launch checklist.
Overview
An autonomous SDR agent qualifies, books and follows up with prospects end-to-end across email, LinkedIn and calendar, with human-approval gates for high-risk actions. Designed for enterprise sales teams requiring CRM integration, role-based access, and data residency controls. Typically deployed behind a managed API with hybrid fallback for offline regions. Outputs are citation-ready and structured for direct ingestion into Salesforce, HubSpot, or comparable CRM pipelines.
Implementation profile
| Category | agentic-models |
|---|---|
| Implementation maturity | production |
| Evidence status | incomplete |
| Primary use cases | agents, enterprise-automation |
| Deployment options | managed-api, hybrid |
| Architectures | serverless-api, managed-container, hybrid-private-cloud |
Candidate models with published references
- BGE-M3
- GPT-5
- GPT-5 Mini
- Claude Opus 4
- Llama 4 Scout
- DeepSeek R1
- Mistral Large 2
- Command R+
- o3
- o4 Mini
- Text Embedding 3 Large
- Claude Sonnet 4
Coverage means the model is a candidate worth evaluating for this workflow, not a ranking or a recommendation. Models whose reference pages are still in review are omitted.
Benchmarks relevant to this workflow
miracl, mkqa, mldr, swe-bench, aider-polyglot, gpqa, aime-2025, tau-bench, browsecomp-long-context, longfact-concepts, terminal-bench, mmmu, mmlu-pro, livecodebench.
Relevance is a coverage signal from the canonical registry. Each benchmark only describes its own protocol and date, so confirm the harness matches your workload before treating a score as evidence.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to AI SDR — Architecture Blueprint.