AI agents for Marketing.
Production-grade agents engineered for marketing and content teams.
SmartDuke builds ai agents for marketing and content teams — systems that autonomously complete multi-step workflows that previously required human orchestration. AI search is disrupting traditional SEO. Citation rate is replacing rank position, and the brands building for GEO/AEO now will own the next decade of inbound. Our approach: Eval coverage on every loop, observability into every tool call, and explicit guardrails around action surfaces before the agent ever touches production.
The problems
we keep solving.
AI search is disrupting traditional SEO. Citation rate is replacing rank position, and the brands building for GEO/AEO now will own the next decade of inbound.
Content velocity is the new moat
Brands that can produce more high-quality, original content per week win compounding advantages in both search and AI citations.
AEO/GEO is a new discipline most teams aren't trained for
Optimizing for ChatGPT, Perplexity, and Google AI Overviews requires a different playbook than classical SEO.
Campaign analytics are fragmented
Pulling insights across paid, organic, email, and social means stitching dashboards together manually every week.
Three things we'd build
first.
Concrete starting points for ai agents in marketing and content teams. We pick the one with the highest leverage and the cleanest measurement story.
- Idea 01
Content-generation agent grounded in brand voice, with built-in editorial review loop
- Idea 02
AEO-optimization copilot that scores content against AI-citation patterns and suggests rewrites
- Idea 03
Campaign-analytics agent that synthesizes weekly performance and recommends reallocations
Production-grade,
from day one.
We use planner-executor, ReAct, or supervisor-worker loops depending on the problem shape — and resist multi-agent orchestration when a single-agent loop solves it.
Evals before launch.
Every loop, tool call, and structured output is graded with a frozen test set and an explicit rubric. Failed evals block the deploy.
Telemetry from day one.
Traces, latency budgets, token costs, and error rates wired up before the first user touches the system.
Guardrails as architecture.
Input validation, output verification, escape hatches, and human handoff paths designed in — not bolted on after incidents.
Boring stack on the edges.
Cutting-edge model in the middle. Reliable infrastructure around it. Stability where it earns its place.
- ×Runaway loops on ambiguous inputs
- ×Tool-call failures invisible to standard APM
- ×Context truncation as conversations grow
- ×Hallucinated actions when grounding is weak
We've shipped this.
GenRasi — a brand-native generative AI product, end to end.
Brand identity, product UX, and a custom generative AI experience launched on a single Next.js + Supabase stack.
See case studyOptimize your site for the engines that answer, not just the ones that rank.
A free AI tool that helps brands win citations in ChatGPT, Perplexity, and Google AI Overviews.
See case studyCommon questions.
01How long does it take to build ai agents for Marketing?
How long does it take to build ai agents for Marketing?
Discovery is one week. A working prototype (Spark) is 2–3 weeks. Full production Build for marketing and content teams typically runs 8–12 weeks depending on data complexity, integrations, and compliance scope. We commit to a precise timeline at quote stage.
02What does pricing for ai agents typically look like?
What does pricing for ai agents typically look like?
Every engagement is scoped to outcomes, not hours. Discovery starts in the low four figures. Spark and Build are priced per project. Embed retainers are monthly. We return a quote within 24 hours of inquiry.
03Can you take over an existing agents project that's stalled?
Can you take over an existing agents project that's stalled?
Yes — it's a common engagement. We review what's there, tell you honestly what stays and what we'd rebuild, then ship it to production. Marketing engagements often start this way.
04What's different about your approach to ai agents?
What's different about your approach to ai agents?
Eval coverage on every loop, observability into every tool call, and explicit guardrails around action surfaces before the agent ever touches production. We hold the same engineering bar across every engagement, regardless of industry — but the specifics for marketing and content teams are tuned to your trends and pain points.
Same capability, different industry.
Same industry, different capability.
Have an AI product
that needs to ship?
Tell us where you are — early concept, broken prototype, or scaling something that already works. We'll come back within 24 hours with a take and a quote.