# Sales AI Operating System

> A reference design for a Sales AI system that qualifies inbound leads, prepares follow-ups for review and keeps CRM records current — with a human approving every outbound message.

**Author:** Adil Mektoub · **Reviewer:** Tanguy Clément · **Published:** 14 July 2026

**Canonical:** https://monacops.com/reference-architectures/sales-ai

**Category:** Reference Architecture


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## Executive summary

A reference design for a Sales AI system that qualifies inbound leads, prepares follow-ups for review and keeps CRM records current — with a human approving every outbound message.

## Business problem

- Inbound leads are qualified inconsistently and follow-ups are slow.
- CRM data drifts out of date, obscuring the real state of the pipeline.

## In scope

- Lead qualification and scoring support
- Follow-up drafting for human review
- CRM update suggestions
- Opportunity flagging

## Out of scope

- Autonomous outreach without approval
- Pricing or contractual commitments
- Unsupervised CRM writes to sensitive fields

## Architecture

- Interface layer: Rep-facing assist inside CRM and email.
- Orchestration layer: Coordinates qualification, drafting and approval steps.
- Knowledge layer: Retrieval over playbooks, product info and account history.
- Integration layer: Connectors to CRM, email and enrichment sources.
- Governance layer: Identity, guardrails, audit logging and observability.

## Data sources

- Inbound lead and enquiry data
- CRM account and opportunity records
- Sales playbooks and product information

## Enterprise integrations

- CRM
- Email (Microsoft 365 / Google Workspace)
- Calendar

## Identity & permissions

- Scoped identities per integration; writes limited to approved fields.
- Rep-level access is respected; the system does not expand a rep's visibility.

## Human approval

- Any outbound message to a prospect — approver: Sales representative
- Writing to sensitive CRM fields — approver: Sales representative

## Security

- Least-privilege access and constrained tool use (OWASP LLM Top 10 — excessive agency).
- Access control and monitoring aligned with NIST CSF.
- Personal data minimised and lawfully processed (GDPR).

## Observability

- Logs of qualification decisions and drafted actions.
- Approval trail linking each sent message to its approver.
- Pipeline hygiene and response-time dashboards.

## Failure handling

- Escalate low-confidence qualification to a human.
- Never auto-send; queue drafts for review.
- Degrade gracefully when enrichment or CRM is unavailable.

## Evaluation protocol

- Qualification agreement: Share of qualification decisions a rep agrees with (measured during a pilot).
- Draft acceptance rate: Share of follow-up drafts accepted with no or minor edits.
- CRM freshness: Reduction in stale records against the current baseline.

## Deployment options

- Managed cloud
- Customer cloud tenancy
- Hybrid

## Limitations

- A reference design, not a deployed system; validate per client.
- Output quality depends on CRM and playbook quality.
- Supports reps; does not replace human selling and relationships.

## Implementation checklist

- Define qualification criteria and scoring
- Set approval points for outbound actions
- Scope CRM write permissions
- Agree pilot metrics and success threshold
- Instrument logging and approval trails
- Run a bounded pilot before rollout

## References

- [OWASP Top 10 for Large Language Model Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/) — OWASP Foundation, 2025.
- [Cybersecurity Framework (CSF) 2.0](https://doi.org/10.6028/NIST.CSWP.29) — U.S. National Institute of Standards and Technology (NIST), 2024-02.
- [Regulation (EU) 2016/679 (General Data Protection Regulation)](https://eur-lex.europa.eu/eli/reg/2016/679/oj) — European Union, 2016-04.

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Published by MONACOPS — Monaco's Agentic AI Operating Systems Company. https://monacops.com