Agentic workflow implementation
We design workflows where agents classify, draft, retrieve, verify, and prepare actions while humans approve external sends and production mutations.
AutoScale AI Partner Practice
Agentic engineering for regulated SMB operations.
AutoScale AI builds production-grade agent workflows with Claude Code, Codex, source-backed retrieval, synthetic evals, and human approval gates for sensitive business operations.
Evaluation-first delivery
We build synthetic evals, scoring rubrics, no-send checks, no-secret checks, and source-grounding tests before treating an agent as production-ready.
Sensitive-data guardrails
Client data, HR records, credentials, and private operational notes stay out of public demos and research-sharing workflows.
What we bring
Production-informed evals, not generic automation demos.
The strongest signal we can provide to model partners is a privacy-safe evaluation practice based on real operating patterns: partial context, bilingual communication, customer urgency, sensitive data, and approval constraints.
Synthetic SMB Operations Agent Eval with 30 validated tasks
Anonymized veterinary-operations case study
Anonymized local-services agent case study
Security and data-handling policy for agentic workflows
Claude Code and Codex orchestration playbooks
Partner fit
- Claude Code and Codex implementation for real SMB workflows
- Human-in-the-loop systems for inbox, CRM, KPI, voice, and support operations
- Bilingual French/English operating environments
- Production-informed evaluation data without customer-data exposure
- Defensive code-hardening and workflow safety patterns
Safety posture
Agents are useful operators, not unsupervised representatives. External communication, CRM mutations, publishing, production deploys, and research-data sharing remain explicitly human-approved.
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