Project facts & technologies
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- Project name
- Agentic AI System for Quotation SLA Monitoring — Automated Quotation Intelligence
- Industry
- B2B Sales, Commercial Operations, Cross-Industry
- Use case
- Autonomous quotation email triage, SLA tracking, AI-drafted responses, manager escalation
- Core technology
- n8n workflow orchestration, LLMs for classification and drafting, PostgreSQL state store
- Integrations
- Gmail API, Outlook / Microsoft 365 API, sales shared mailboxes
- SLA tiers
- 6 hours first response · 12 hours follow-up · 24 hours manager escalation
- Agent behaviors
- Email triage, intent classification, customer matching, SKU extraction, priority, drafting, follow-up, escalation, reporting
- Human-in-the-loop
- Salesperson reviews and approves every LLM-drafted quotation before send
- Manager outputs
- Live SLA dashboard, salesperson scorecards, performance summaries, quote-to-win analytics
- Stakeholder users
- Sales reps, sales managers, commercial leadership, audit & compliance
- SLA outcome
- 95% SLA compliance across quotation channels
- Speed outcome
- 50% reduction in quotation response time
- Governance
- Role-based access control (RBAC), full audit log, tamper-evident records
- Deployment
- Cloud-native, encrypted in transit, integrates with existing sales mailboxes
Why are quotation SLAs such a hard operating problem for B2B sales?
In every B2B sales operation, the quotation cycle is the single most important commercial workflow — and the most leaky. A customer sends an email asking for a quote on a specific product, configuration, or service. From that moment, every minute of delay erodes two things: the probability that the customer buys from you, and the probability that they trust you to deliver. The faster the quote, the higher the conversion, the higher the customer satisfaction, the more credible the brand. And yet, in most B2B operations, the quote takes far too long.
The reasons are structural, not personal. Sales reps live in inboxes that receive dozens of internal and external emails per day; a quotation request is one stream among many. Managers don't know a quote was even requested until someone surfaces it. And the SLAs themselves are often informal — never quantified, never tracked, never escalated. Agentic AI changes the economics by autonomously monitoring inbound email, classifying quotation requests in seconds, opening explicit SLA timers, drafting responses for human review, and reporting on performance in real time.
What problem does the Agentic AI System solve?
AiSPRY's commercial client needed to turn quotation handling from a reactive, inbox-dependent practice into a governed, AI-augmented workflow. Several structural challenges had to be solved together.
Key challenges
- Quote requests buried in busy inboxes — salespeople receive dozens of emails a day; a quotation request often sits unseen for hours before anyone acts on it.
- Informal, untracked SLAs — quote turnaround expectations are stated but not tracked; nobody knows whether a quote is late until it has already cost the deal.
- No manager visibility until breach — by the time a manager learns a quote is overdue, the customer has often already moved on; escalation is reactive, not preventive.
- Manual drafting overhead — every quotation email is typed from scratch, even when 80% of the response is the same product / pricing context the rep wrote yesterday.
- Slow response erodes conversion — B2B conversion is directly correlated with quotation speed; every additional hour reduces close probability.
- Cross-mailbox fragmentation and no audit trail — quotation requests arrive across Gmail and Outlook, shared inboxes, and personal addresses; nothing unifies the view and there is no continuous record of who responded when.
How does the Agentic AI System work?
AiSPRY built an autonomous AI agent that owns the entire quotation triage and SLA workflow. The agent continuously monitors inbound email channels, identifies quotation requests using an LLM-based intent classifier, opens explicit SLA timers, drafts tone- and brand-aligned responses with LLMs, escalates delays to managers, and produces real-time performance summaries — orchestrated on n8n with RBAC and full audit.
Email triage and SLA tracking
- Autonomous email triage — Gmail and Outlook monitoring, intent classification, customer and contact matching, product / SKU extraction, priority scoring, and auto-routing to the right sales owner
- SLA tracking and escalation — 6h first-response, 12h follow-up, and 24h manager escalation timers persisted in PostgreSQL, with live breach alerts and tier-aware urgency cues
AI drafting, manager console, and governance
- AI-assisted quotation drafting — tone- and brand-aligned LLM drafts with inline product and price lookups, clarifying questions where ambiguous, and follow-up nudges as SLA tiers advance
- Salesperson-in-the-loop — every draft reviewed, edited, and approve-and-sent from the rep's own inbox; the AI assists, the human owns the customer relationship
- Manager console and reporting — live SLA dashboard, salesperson scorecards, performance summaries, and quote-to-win analytics
- Governance and audit — RBAC across sales, management, and audit; tamper-evident audit log of every agent action
See the quotation SLA agent in action
A walkthrough of the Agentic AI System for Quotation SLA Monitoring — autonomous Gmail and Outlook triage, LLM-drafted quotations landing in the rep's inbox for approve-and-send, the 6h / 12h / 24h SLA timers running in PostgreSQL, and the live manager dashboard with breach alerts and scorecards.
Agentic AI for quotation SLA — orchestrated, audited, AI-drafted
- Autonomous triage — quote intent classifier, customer matching, and SKU extraction in seconds
- Governed SLA timers — 6h / 12h / 24h tiers persisted in PostgreSQL with tier-aware nudges
- LLM-assisted drafting — tone- and brand-aligned drafts ready for approve-and-send from the rep's inbox
- Manager console — live SLA dashboard, scorecards, and quote-to-win analytics with RBAC and audit
What does the Agentic AI architecture look like?
The platform follows a five-stage agentic pipeline that takes inbound customer email and converts it into a fully tracked, SLA-governed, AI-drafted, manager-visible quotation workflow. Email channels (Gmail and Outlook) feed the agent's inbox triage on n8n, an LLM intent classifier routes requests, a PostgreSQL state store tracks SLA timers and fires manager escalations, LLM drafting lands quotations in salesperson inboxes for approve-and-send, and a manager console with RBAC and audit exposes SLA breach status, scorecards, and quote-to-win analytics.
How does the platform handle salesperson trust, SLA governance, and audit?
Building an agentic AI system that operates on live customer email and represents the organization to customers imposes constraints that an ordinary automation script cannot meet. AiSPRY engineered around four.
Salesperson-in-the-loop, always
- Every LLM-drafted quotation is reviewed and approved by a human before send
- The rep owns the customer relationship — the AI assists, it doesn't replace
- Replies are sent from the rep's own inbox to preserve continuity for the customer
- Escalations route through managers, not around them
SLA-governed and built on n8n
- Every quotation request gets an explicit SLA timer the moment it is identified
- Tier-aware nudges keep salespeople ahead of breaches, not behind them
- Manager escalation fires automatically as breaches approach — no surprise overdues
- Workflow orchestration on n8n means every step is visible, editable, and auditable
Governance and audit by design
- Role-based access control separates sales, management, and audit roles
- Full audit log of every agent action — triage, draft, escalate, send, report
- Tamper-evident records of customer-facing replies and manager escalations
- Encrypted in transit between mailbox APIs, agent, state store, and dashboards
What measurable results does the Agentic AI System deliver?
The platform was engineered against two headline metrics — SLA compliance and response time — both moved sharply in the right direction. Beyond those, the system also shifts the operating practice of B2B quotation handling from reactive and manual to governed and AI-augmented.
SLA compliance and response speed
- 95% SLA compliance across quotation channels — measured, not estimated
- 50% reduction in quotation response time versus the manual baseline
- Every quotation request opens a tracked SLA timer the moment it is identified
- Tier-aware nudges keep salespeople ahead of breaches
Customer experience and conversion
- Faster responses translate directly into higher customer satisfaction
- Higher quote-to-win conversion through faster, on-brand replies
- Tone- and brand-aligned drafting produces a consistent customer experience
- Customer relationship continuity preserved through send-from-rep-inbox design
Sales productivity and governance
- Significant reduction in manual inbox triage burden across sales reps
- Salespeople spend less time typing and more time selling
- Better SLA governance through live dashboards and scorecards
- Full audit log of every agent action supports commercial governance
Quotation SLA Agent — frequently asked questions
Below are the most common questions about how the agent works, what it does autonomously, where the human stays in control, and how it is deployed across a B2B sales operation.