AI for marketing teams, running in days

Working AI workflows — built, governed, and managed on the platforms you already use. Not a prototype. Not a roadmap. A system your team runs on.

Your own AI marketing system LIVE
Your inputs
Brand voice
CRM & data
Research
Past campaigns
Governed Marketing AI
Agents · Brand Rules · Human Review
Operational output
Briefs Campaigns Lead scores Reports

Marketing leaders who ask "where is AI actually delivering value in our function?" often find the same answer: not from the tools, but from how those tools are structured.

Liorant builds and manages AI workflows for marketing functions — content production, campaign briefing, lead qualification, reporting automation, brand consistency — deployed on the platforms your organisation already licences. We deliver a working system in 4–6 weeks. After that, we run and expand it as part of your operations. If your mandate is measurable output, not experimentation, this is where to start.

Engagement Model

From first conversation to managed AI operations

No open-ended consulting programmes. No 12-month transformation roadmaps. No discovery sprints that produce a slide deck. Three structured phases — designed to put working AI in your team's hands fast.

01
Discovery
FREE · 30–60 MIN
We map your workflow and confirm your platform readiness. No presentation. No pitch.
02
Activation
4–6 WEEKS · FIXED PRICE
One working AI system, built on your platform and delivered with full documentation.
03
Operations
MONTHLY · OPTIONAL
We run, optimise, and expand your AI portfolio — with a monthly business outcome report.

Step 1 — Initial conversation

We understand your current marketing operations, existing tool licences, and where capacity constraints hurt output or speed. We tell you upfront whether your Microsoft 365, Google Workspace, or Claude licence already supports what you need.

Step 2 — Activation project

We select the highest-value use case, build it on your existing platform, test it against your real workflows, and hand it over with governance guardrails. You receive a working AI system — not a prototype, not a recommendation.

Step 3 — Managed AI operations

We monitor, optimise, and expand your AI portfolio on a monthly retainer. Each month you receive a business outcome report — capacity freed, content produced, commercial impact — as a leadership narrative.

The Real Challenge

Building an AI system that works across any platform

Most marketing organisations already have AI embedded in their platforms — Copilot in Microsoft 365, Gemini in Google Workspace, Claude through the API. Without a governed implementation, adoption fragments: one person uses it well, two avoid it, and no one can point to a business outcome.

The competitive advantage comes from running AI as a disciplined operational capability, adaptable to any change and any platform — with defined inputs, strcutured data, quality control, and commercial accountability. That is what Liorant builds.

The pattern we see every time

01

Tools available but not configured for specific marketing workflows.

02

Outputs are inconsistent — no governed prompts or brand standards loaded into the system.

03

No audit trail, so compliance and brand risk sit with whoever ran the prompt last.

Use Cases

10 AI use cases for marketing teams

From first activation to full scale. Each starts as a single, scoped deployment with a defined deliverable. All connect into coordinated systems as the portfolio grows.

Group 1

Research, Strategy & Content

Market Research Assistant
Campaign Generator
Content Production
Group 2

Execution & Lead Qualification

Brand & Messaging Assistant
Marketing Workflow Assistant
Lead Qualification Assistant
Sales Materials
Group 3

Intelligence & Adoption

Campaign Reporting Assistant
Processes & Knowledge Assistant
AI Adoption & Team Training
G1Research, Strategy & Content Production
01

Market Research Assistant

The business problem

Research is fragmented across documents, presentations, and individual inboxes. Senior marketing time gets consumed compiling information that should be systematically available. Decisions get made on stale data.

What we build

An AI assistant that consolidates research across sources, synthesises competitor positioning, and produces audience insight briefs in a structured format — accessible in natural language across the team.

Scales toward  Automated competitor monitoring · Audience insight reports per campaign · Research-driven brief generation

02

Campaign Briefing Copilot

The business problem

Campaigns start from scratch every cycle. Briefs are incomplete and depend on senior marketing leaders to add strategic context — creating a recurring bottleneck that slows execution and dilutes quality.

What we build

A structured copilot that generates complete campaign briefs from audience definitions, objectives, and prior performance. Planning becomes consistent, faster, and less dependent on specific people.

Scales toward  Reusable brief templates · Campaign playbooks by channel · Approval flow automation

03

Content Production Copilot

The business problem

Content output is constrained by team capacity. Every piece starts from zero with no systematic reference to brand voice, past performance, or audience context — and never scales beyond headcount.

What we build

A controlled content production workflow configured with your brand guidelines, audience context, and high-performing past examples. Volume increases without headcount. Quality is governed, not random.

Scales toward  Email sequences · Ad copy variant generation · Localisation across markets and languages

G2Brand Consistency, Execution & Lead Qualification
04

Brand & Messaging Assistant

The business problem

Brand consistency erodes at scale. When content is produced across multiple people, teams, and tools, the company's message fragments — each communicator adds their own interpretation.

What we build

An assistant trained on your brand guidelines, messaging pillars, and approved language. Every output — from marketing, sales, or leadership — stays aligned without a brand manager reviewing each piece.

05

Marketing Workflow Assistant

The business problem

Execution time is absorbed by coordination, not work. Approval chains, publication steps, and task routing happen over email and Slack — outside any system, with no audit trail and no accountability.

What we build

An automation layer handling workflow orchestration: routing tasks, triggering approvals, flagging bottlenecks, notifying the right people. Leadership gets visibility. The team gets time back.

06

Lead Qualification Assistant

The business problem

Marketing generates pipeline that sales cannot efficiently prioritise. Without standardised qualification, the highest-potential leads receive the same attention as the lowest — and conversion suffers.

What we build

An AI-powered qualification workflow that scores and categorises incoming leads against defined commercial criteria, integrated into your CRM and connected to source campaign data.

07

Sales Materials Copilot

The business problem

The marketing function becomes a service desk for sales. Teams repeatedly request variations of the same assets — decks, follow-up emails, product sheets — pulling capacity away from demand generation.

What we build

A self-serve sales asset system where the commercial team generates approved variations of marketing materials independently. Marketing sets the guardrails; sales executes within them.

G3Reporting, Knowledge Management & AI Adoption
08

Campaign Reporting Assistant

The business problem

Reporting consumes senior time without improving decisions. Performance data exists across multiple platforms, but translating it into a clear narrative for leadership takes manual effort every cycle.

What we build

An AI reporting workflow that pulls performance data from connected sources, generates narrative summaries per campaign and channel, and surfaces what is working — as a structured monthly leadership report.

Scales toward  Monthly campaign narrative reports · Channel-by-channel summaries · ROI narrative for board reporting

09

Marketing Processes & Knowledge Assistant

The business problem

Operational knowledge lives in people, not systems. New team members depend on managers for onboarding. When key people leave, institutional knowledge walks out with them.

What we build

An internal assistant that makes your operational knowledge available in natural language — processes, approval rules, tool guides, brand standards — without locating and reading a document.

Scales toward  New hire onboarding automation · Internal AI-assisted training · Continuous process documentation

10

AI Adoption & Team Training

The business problem

Without a structured adoption programme, AI use in marketing is inconsistent and ungoverned. Some people use it well; others avoid it or produce outputs that carry brand or compliance risk.

What we build

A structured AI adoption programme custom-built for your marketing function — role-specific prompt libraries, usage governance, and hands-on training on the platforms your team actually uses.

Scales toward  Role-specific prompt libraries · Internal AI champions per function · Governance documentation for audit

See how Liorant structures a 4-week activation for marketing teams
AI Architecture

How AI scales across the marketing function

A full AI capability is a four-layer architecture. Each layer adds a distinct kind of intelligence. Layers are introduced progressively — not everything is needed on day one.

Scale & complexity
Layer 4 · Knowledge System
Proprietary knowledge
Layer 3 · ML & Predictive
Forecast & scoring
Layer 2 · Workflow Automation
Orchestration
Layer 1 · Generative AI
Content · Research · Brand Voice
L1

Generative AI

LLMs — Claude, Gemini, ChatGPT — to generate, summarise, translate, and quality-check at scale. The work is the governance architecture around it: brand voice, prompt libraries, human review checkpoints. This unlocks content throughput, faster campaign research, and brand consistency that does not depend on who wrote the piece.

L2

Workflow Automation & Orchestration

Power Automate, Copilot Studio, LangChain, LangGraph and native connectors route AI outputs into your workflows — CRM updates, approval triggers, publishing pipelines, report delivery. This unlocks execution without manual coordination, approval chains without chasing, and exception-based campaign operations.

L3

Machine Learning & Predictive Analytics

ML models analyse patterns across audiences, channels, and content performance — converting historical signal into forward-looking decisions on budget, lead priority, and content. This unlocks data-led spend allocation, lead scoring that improves by campaign cycle, and earlier performance warnings.

L4

Intelligent Knowledge Systems

Connect AI to your proprietary knowledge — product docs, past campaigns, customer research. AI answers from your data, not generic internet content. This unlocks internal assistants, proprietary research workflows, and sales enablement built from real win/loss history.

This is the layer that makes AI feel like it knows your business — internal assistants, research grounded in your insights, sales enablement from your real win/loss history.

Build knowledge systems with RAG
Activation Process

A marketing AI activation, week by week

Week 1
01
Platform Check
Confirm licences and access.
02
Use Case Selection
Identify highest-value bottleneck.
Week 2–3
03
Workflow Design
Map inputs, process, review points.
04
Build & Configure
Deploy on your platform & data.
Week 4–6
05
Test & Validate
Run against real workflows, QA.
06
Handoff & Ops
Docs, training, live retainer.
1

Platform qualification. We verify your licences and confirm which AI capabilities are available, flagging any changes needed before building begins — no surprises mid-engagement.

2

Use case prioritisation. We map your function against the ten use cases and identify the bottleneck with the clearest commercial value: capacity freed, revenue influenced, or decision speed improved. That becomes the Phase 1 target.

3

Workflow design. We define inputs, the AI process, human review points, and output format — and where governance controls apply.

4

Build and configuration. We configure the system on your platform, load brand context and approved data, and build the automation layer. Two to four weeks by scope.

5

Test and handoff. We run against real data, validate output quality, document the system fully, and deliver with a governance guide and user guide.

6

Monthly operations. We monitor systems, run prompt optimisation as platforms update, add one new use case per month, and deliver a business outcome report for leadership at the end of each cycle.

Platforms

We build on the tools you already licence

We do not introduce new SaaS subscriptions unless your current platforms cannot support the use case.

Microsoft 365 / Copilot Studio

Copilot Studio enables agent-building and workflow automation inside your existing environment. Microsoft 365 Copilot licences typically include the infrastructure for internal AI agents at no additional platform cost.

Google Workspace / Gemini Enterprise

Gemini Enterprise integrates AI across Docs, Sheets, Gmail, and Meet — with Gemini Studio available for custom agent development and workflow integration.

Claude Cowork

For precise brand voice control, long-document processing, or structured content generation at scale. Particularly effective for content production and document-intensive knowledge management.

Orchestration & Automation

For routing between systems, triggering approvals, and connecting CRM to content tools we use Power Automate, LangChain and LangGraph for agent orchestration and multi-step reasoning flows, plus native platform connectors across Google and Microsoft. For more complex cross-platform automation, we scope additional integration tooling to your existing stack.

Why Liorant

How Liorant compares

Hiring an in-house AI capability takes months and carries full employment cost and management overhead. Retaining Liorant delivers a working system in weeks.

Criteria Liorant In-house hire Large consultancy DIY / tools only
Time to first working AI 4–6 weeks 3–6 months to hire 3–12 months Immediate but unstructured
Cost model Fixed project + optional retainer Salary, benefits & overhead Enterprise project pricing Subscriptions + internal time
Marketing-specific use cases 10 frameworks, customised Built from scratch Generic frameworks None
Platform Agnostic Expertise Copilot Studio, Gemini Enterprise, Claude, Power Automate, LangChain — native Varies by hire Varies by team assigned Self-managed
Outcome reporting for leadership Monthly leadership narrative Depends on internal setup Separate workstream None
EU AI Act / USA / El Salvador compliance Included Requires extra expertise Separate track None
Ongoing optimisation Monthly, in retainer Full-time staff cost Separate fee Manual
Multilingual delivery Native capacity Depends on hire Depends on team Not applicable

The build-vs-buy decision for marketing leaders is straightforward: hiring an in-house AI capability takes months and carries full employment cost and management overhead. Retaining Liorant delivers a working system in weeks, at a fraction of the annual cost, with the flexibility to scale or redirect scope as your priorities change.

Start with a free conversation about your team's situation
FAQ

Questions marketing leaders ask

Still unsure where to start? A 30-minute discovery call answers most of these against your specific situation.

How quickly can our marketing function have working AI?

The activation project delivers a working system in 4–6 weeks from a signed agreement. Most clients have their first AI workflow in production within 30 days of kickoff.

We already have Copilot or Gemini — do we need anything else?

In most cases, your current licences already include the infrastructure needed. Before scoping begins, we run a platform qualification check to confirm compatibility and flag any adjustments. If a licence change is needed, we tell you upfront so you can decide.

We tried AI tools before and saw no measurable result. Why is this different?

Unstructured AI adoption — where each person prompts however they choose — rarely produces business outcomes. The difference is implementation: a governed workflow, a tested prompt library, brand context embedded in the system, and defined review points. We do not give you a tool. We give you a process.

What does ongoing management actually include?

Monthly platform monitoring to detect silent system degradation. One new AI workflow added per month. A governance review covering prompts, data access, and compliance posture. A business outcome report written as a leadership narrative — what improved, by how much, and what the next priority is. We also maintain your opportunity backlog and present it at a quarterly business review.

Can Liorant integrate with our existing marketing technology stack?

Yes. We integrate with HubSpot, Salesforce, Google Analytics, and other marketing platforms via Copilot Studio connectors, Power Automate flows, or LangChain integrations. Integration requirements are confirmed and scoped during the activation project.

Is there a minimum commitment?

The activation project is a standalone, fixed-fee engagement with no ongoing obligation. If you choose to continue to a managed retainer, the initial period is three months — after which you can cancel with 30 days' notice.

Get started

Build AI into your marketing function — starting with the constraint that costs you most.

Start with a free 30-minute discovery session. We identify your highest-value marketing automation opportunity and explain exactly how Liorant can help — no slides, no pitch.

Book your session