AI reply drafts · Agent assist · Franchise + multi-unit support

The draft cites the corporate refund policy. The customer is at a franchise location with a different one. Catch it before the reply ships.

You run customer support across 50-1,500 franchise locations. Generic AI replies draft from the ticket text + a global knowledge base. They are policy-blind, jurisdiction-blind, and offer-freshness-blind. The draft cites the wrong location’s refund policy, surfaces a SAVE15 code that expired last month, or makes a claim that’s fine in 47 states and prohibited in 3. The agent reviews quickly, sends. Damage lands hours later. Pre-gate every draft on location-policy + jurisdiction + offer-catalog freshness before it reaches the agent UI.

Audit your reply-drafting gates (Tier 1)Explore Fractional CMO operation

Published May 30, 2026

Three failure scenes you already know

The policy mismatch.A customer at a franchise location asks about returning a product. The draft surfaces the corporate refund window (30 days). The franchisee’s actual policy is 14 days. The agent sends. The customer comes back at day 21 expecting the refund the corporate policy implied. The franchisee eats the loss or refuses the refund and earns a 1-star review.

The deprecated-offer reference.The KB still contains a SAVE15 entry from last quarter’s promotion. The draft offers SAVE15 to a complaining customer. The code is dead in your offer-execution platform. The customer hits checkout and gets an error. The complaint loop reopens.

The per-state claim violation. The draft makes a wellness claim that is allowed in 47 states. The customer state is one of the 3 where it is restricted. The agent does not know which states are restricted; the draft does not flag it. The reply ships. Regulatory exposure begins quietly.

The gap is not the AI-reply primitive. The gap is that the draft was generated without the per-location policy, the customer’s jurisdiction, or the live offer catalog as constraints.

We’ve built the drafting layer for multi-unit operators. Here’s what we know.

You probably already use a reply-suggestion feature in your support platform — Salesforce Einstein, Zendesk AI, Intercom Fin, Ada, Forethought, Drift, Front. Each is good at the suggestion primitive. The gap at multi-unit scale is the three-gate pattern that runs before the agent sees the draft: a location-policy gate, a per-jurisdiction gate, and an offer-catalog gate. Each gate is operator-side wiring; the pattern is what we bring.

We have built this for franchise and multi-unit operators across verticals. We know which gate flags most drafts in the first 30 days (usually the offer-catalog gate, because the deprecated-offer drift accumulates faster than operators audit). We know the per-jurisdiction overlay encoding patterns that hold up when regulations change. We bring the gate runbooks.

How we get from generic AI replies to gated per-location drafts

Step 1 — Tier 1 AI Readiness Assessment ($10k, 2-3 weeks). We audit your current reply-suggestion surface. We sample your last 30-90 days of agent-sent replies and run a three-gate diff: how many would have been flagged by location-policy, jurisdiction, or offer-catalog gates? Output: the per-location policy registry spec, the per-jurisdiction overlay starter, the offer-catalog integration spec, and a per-channel rollout plan.

Step 2 — Tier 2 AI Swarm Setup Sprint ($25-50k, 4-8 weeks). We build the drafting layer end-to-end: per-ticket per-location context payload assembly, per-vertical tone overlay, the three-gate pipeline, audit-trail event publisher, and the false-positive review workflow. Your engineering team receives the running system, all source code, all credentials.

Step 3 — Tier 3 Fractional CMO with AI Swarm ($15-25k/month, 6-month minimum, 1-2 days/wk). We operate the layer in production. Maintain the per-location policy registry as franchise agreements change. Update the per-jurisdiction overlay as new state rules go effective. Run the weekly false-positive review with your support leadership. Roll up a monthly gate- evaluation report.

What changes for you

You stop discovering the franchise-vs-corporate policy mismatch in Monday’s 1-star review queue. The gate caught it Tuesday morning at the draft stage.

You stop reading agent-sent replies for compliance spot checks. The per-jurisdiction overlay runs on every draft; legal counsel reviews the audit trail post-hoc instead of gating campaigns pre-hoc.

You can answer the question your support leadership asks every quarterly review: which gate flagged the most drafts this quarter, and what was the root cause. The audit-trail rollup is the answer.

You can onboard a new vertical or a new franchise concept without re-writing every reply template. The per-location context payload + per-vertical tone overlay carry the variation; the gate pipeline stays the same.

Frequently asked

How is per-location reply drafting different from the generic AI replies my support platform already ships?

Generic AI replies draft from the ticket text + a global knowledge-base lookup. The output reads plausibly but is context-blind. At a single-location business that is enough. At 50-1,500 locations, the same customer message can route to a different location with different policy, different store hours, different staffing model, and different active offers. The draft needs to be pre-loaded with the affected location identity, the customer state at ticket arrival, the per-location knowledge-base scope, and the per-location compliance overlay before the language model runs. The per-location pre-load is operator-side wiring on top of whichever AI-reply primitive your platform ships.

What does the three-gate pattern (location + jurisdiction + offer-catalog) actually do?

The location-policy gate checks the draft against the policy of the location handling the ticket. A franchise location has different refund + exchange + hours-of-operation than a corporate-owned location; the gate flags drafts that reference the wrong one. The jurisdiction gate checks the draft against per-state advertising + consent + claim restrictions for the customer state. Drafts that violate a per-state rule get rewritten (insert required disclosure) or blocked (request operator review). The offer-catalog gate checks every offer mentioned in the draft against the live save-offer library; deprecated codes get removed before the agent sees the draft. The three gates compose; the draft surfaces in the agent UI only after all three pass.

What does Completions commit to on Tier 3 if we run this layer in production?

Tier 3 process commitments include: gate evaluation at sub-second p95 latency per draft; per-location policy registry maintained on a documented quarterly cadence as franchise agreements evolve; per-jurisdiction overlay updated within 5 business days of new per-state rule going effective; offer-catalog freshness coordination with your save-offer library on every catalog change; weekly false-positive review of drafts that were modified or blocked by the gate. We commit to the operating discipline. Per-vertical recall + precision are tuned against your ticket corpus and recorded as engagement KPIs.

Why does per-vertical tone overlay matter? Isn’t a brand voice guide enough?

A brand voice guide is a document. The drafting layer needs constraint as data the model can consume at generation time: per-vertical preferred sentence-length distribution, per-vertical permitted + prohibited phrases (a financial-services operator avoids prescription language; a healthcare operator avoids any claim language; a high-end retail operator avoids casual greeting patterns), contractions policy, emoji policy, disclaimer language. The brand voice guide gets encoded as the overlay during the Tier 2 build. The overlay produces consistent voice across the draft volume your team handles. Without the overlay, the draft volume reads as inconsistent because the model is interpreting the guide instead of being constrained by it.

Who owns the policy data, the gates, the overlay, and the labels post-engagement?

Your team owns the per-location policy registry, the per-jurisdiction overlay rules, the per-vertical voice overlay encoding, the agent rosters, and the credentials. Completions owns the orchestration knowledge: the gate-evaluation runbook, the false-positive triage playbook, the per-vertical overlay tuning history. At engagement end we transition operational ownership back to your team over 30-60 days with documented handover.

How does the drafting layer connect to the rest of the support + retention stack?

The drafting layer subscribes upstream to the sentiment-intent classification layer (so it knows the multi-label urgency the draft is replying to). It subscribes to the save-offer library (so it knows which offers are active). It subscribes to the per-jurisdiction overlay (so the gate has current rules). It publishes downstream: every gate decision emits a typed event into the audit-trail pipeline; every sent reply emits a typed event into the recovery-rate dashboard. Six contracts, one drafting layer. The drafting layer owns the gate; each consumer owns its own subscription.

Start with the audit

Tier 1 AI Readiness Assessment ($10k, 2-3 weeks): we audit your current reply-suggestion surface, sample your last 30-90 days of agent-sent replies, run the three-gate diff, and produce the per-location + per-jurisdiction + offer-catalog integration spec. If you decide to build, Tier 2 ships the gated drafting layer. If you decide to operate it with us, Tier 3 runs it in production. You choose the next step at each gate.