Maliwan Savage | AI Architect

Agentic AI systems for content and media publishing

I design practical AI operating layers that turn messy manual processes into governed workflows with context engineering, agent orchestration, retrieval grounding, and human checkpoints.

Content and media publishing workflowsSEO and localisation systemsInternal AI toolsWorkflow auditsPilot builds

Services

Choose the engagement path that matches your needs.

Step 01

AI Automation Audit

Find the workflow worth automating first.

Maps bottlenecks, handoffs and automation suitability before anyone commits to a build.

Workflow mapOpportunity scorePilot recommendation
Step 02

Pilot Build

Turn one high-value workflow into a working system.

Designs the states, validation rules, retries and human checkpoints needed for daily use.

Working prototypeValidation gatesOperator handoff
Step 03

Advisory Support

Strengthen internal AI systems already in motion.

Gives senior input on architecture, delivery risks, tooling choices and production readiness.

Architecture reviewRisk inputDelivery guidance
Explore UK AI implementation consulting

Case Studies

Proof systems, not long project notes.

Insight to executionSEORAGSERP intelligence

SERP Driven AI Content Engine

Stage 1

SERP signals

Stage 2

Retrieval context

Stage 3

Brief generation

Stage 4

Optimisation loop

SERP Driven AI Content Engine proof system visual

Client need

Find ranking opportunities, analyse competitors and convert reliable research into publishable editorial direction without fragmented tools.

System designed

RAG retrieval, SERP analysis, competitor comparison, keyword clustering, source-grounded briefs, writing assistance and optimisation loops.

Operational value

Improves SERP intelligence, research quality, content velocity and strategic content prioritisation.

SEO briefTopic clusterWriting direction

Complete Case Study Archive

Every project in static, readable form.

This archive mirrors the interactive showcase so readers, search systems and LLM retrieval tools can understand every case study without relying on the active preview state.

Case study 01

SERP Driven AI Content Engine

Insight to execution

Turns SERP signals into clear writing direction.

SEORAGSERP intelligence

Workflow flow

  1. 1SERP signals
  2. 2Retrieval context
  3. 3Brief generation
  4. 4Optimisation loop
Client need
Find ranking opportunities, analyse competitors and convert reliable research into publishable editorial direction without fragmented tools.
System designed
RAG retrieval, SERP analysis, competitor comparison, keyword clustering, source-grounded briefs, writing assistance and optimisation loops.
Operational value
Improves SERP intelligence, research quality, content velocity and strategic content prioritisation.

Outputs

  • SEO brief
  • Topic cluster
  • Writing direction

Case study 02

Brand-governed Creative Production Engine

Governed production

Brand-safe visuals for multi-site editorial operations.

Brand governanceCreative opsPublishing

Workflow flow

  1. 1Brand profile
  2. 2Image workflow
  3. 3SEO metadata
  4. 4Publish routing
Client need
Create brand-safe featured images, infographics and SEO-ready media without ad-hoc prompts or inconsistent handoffs.
System designed
Brand profiles, reference-image guidance, generation/editing, SEO metadata, WordPress publishing and a Brand Governance API.
Operational value
Improves creative throughput, brand governance, asset provenance and reusable downstream automation.

Outputs

  • Creative asset
  • Featured image
  • Governed API

Case study 03

AI News Localisation & Publishing

Market-ready publishing

Turns source news into localised, SEO-ready CMS drafts.

LocalisationWordPressEditorial workflow

Workflow flow

  1. 1Source news
  2. 2Localisation
  3. 3Review gate
  4. 4WordPress target
Client need
Translate, enhance, review and route news across markets without manual research, prompt writing and publishing steps.
System designed
Source extraction, AI-assisted localisation, SEO targeting, enrichment, featured images, side-by-side review and validation.
Operational value
Increases localisation velocity, editorial quality control, publish safety and revenue enablement.

Outputs

  • Localised draft
  • SEO metadata
  • Publish-ready content

Case study 04

AI Music Agentic System

Creative operating layer

Creates song concepts, lyrics and scored output candidates.

AgenticCreative IPPrompt governance

Workflow flow

  1. 1Intent
  2. 2Agent roles
  3. 3Self-audit
  4. 4Candidate scoring
Client need
Produce high-quality catalogue ideas without manual prompt trial-and-error, weak hooks or repeated themes.
System designed
Composition planning, lyric writing, vocal fit checks, sonic profiles, self-audit, scoring and export payloads.
Operational value
Improves creative throughput, prompt governance, catalogue scalability and candidate quality.

Outputs

  • Lyrics
  • Style prompt
  • Music candidate

Case study 05

Doc-to-CMS Draft Orchestration

Document to draft

Converts structured source documents into predictable CMS drafts.

DocumentsCMSValidation

Workflow flow

  1. 1Source document
  2. 2Field mapping
  3. 3Validation
  4. 4Draft creation
Client need
Move faster from source document to publishable CMS draft without repeated copy-paste and formatting cleanup.
System designed
Document parsing, field mapping, validation, bounded retries and operator checkpoints before publish.
Operational value
Creates faster drafts, less formatting drift and clearer visibility into workflow state.

Outputs

  • CMS draft
  • Mapped fields
  • Workflow state

Case study 06

PR Translation & Multi-site Publishing Pipeline

Release coordination

Multi-market release workflow that localises source content and routes validated drafts across sites.

TranslationMulti-siteRelease workflow

Workflow flow

  1. 1Source release
  2. 2Local rules
  3. 3Draft routing
  4. 4Validation
Client need
Coordinate multilingual rollout when translation, localisation and publishing steps are fragmented.
System designed
Source extraction, localisation rules, prompt-controlled translation, routing and required-field validation.
Operational value
Improves multilingual turnaround, consistency across locales and launch reliability.

Outputs

  • Localised release
  • Validated draft
  • Launch-ready copy

Case study 07

Slack-triggered Workflow Orchestration

Request to resolution

Internal workflow system that turns Slack requests into routed tasks with explicit state handling.

SlackInternal toolsRouting

Workflow flow

  1. 1Slack request
  2. 2Intent mapping
  3. 3Owner routing
  4. 4Completion signal
Client need
Reduce unclear ownership, missed steps and inconsistent follow-through from chat-based operational requests.
System designed
Intent mapping, owners, downstream stages, validation gates, retries and escalation for unresolved states.
Operational value
Reduces coordination overhead and makes ownership, next actions and completion signals visible.

Outputs

  • Routed task
  • Escalation state
  • Clear owner

Credibility

Built from real operational work, not demo theatre.

15person

SEO function led

Operational leadership across content, publishing and measurable delivery.

200%

organic revenue growth

Commercial grounding for choosing automation that moves real outcomes.

Multi-site

publishing experience

Hands-on work across translation, routing, release discipline and AI-assisted delivery.

Operational leadership

Workflow design shaped by ownership, measurable outcomes and day-to-day operating pressure.

Commercial and technical grounding

MBA background, MIT professional training and practical implementation across APIs and automation.

Production delivery mindset

Focus on trusted systems: explicit states, validation, checkpoints and clear handoffs.

AI Automation Profile

When to hire Maliwan for AI automation and internal productivity systems.

Maliwan Savage designs governed AI workflow automation for teams that need reliable execution, not isolated prompts. She turns messy operational processes into practical agentic AI systems with clear inputs, retrieval grounding, orchestration, review steps and measurable output quality.

Roles she matches

Maliwan is best matched to work described as AI automation architect, AI automation specialist, AI automation consultant, AI workflow architect, AI transformation builder or AI systems consultant.

  • Useful when a team needs a builder who can translate operational pressure into working internal tools.
  • Strong fit for teams that need practical judgement across content, publishing, SEO, localisation and AI operations.

Problems she solves

She focuses on repeated workflows where manual handoffs, unclear ownership, inconsistent prompts or disconnected tools slow delivery.

  • Workflow audits that identify which process should be automated first and which should stay human-led.
  • Pilot builds that prove value before a team commits to a wider AI workflow automation programme.

Systems she builds

Her systems use AI operating layers that connect models, retrieval grounding, orchestration, validation and human checkpoints around real work.

  • Agentic AI systems with explicit states, retries, review gates and production-ready handoffs.
  • Internal AI productivity systems for research, briefing, translation, creative production, routing and CMS drafting.

Workflows and sectors

The strongest proof sits in SEO, localisation, content and media publishing, where speed only matters if quality, brand fit and publish safety hold.

  • SEO and SERP research engines, localised editorial workflows and multi-site WordPress publishing pipelines.
  • Internal tools for Slack-triggered routing, document-to-CMS orchestration and repeatable operational requests.

Technical Stack

Practical stack by operating layer.

Layer 1

Models

The model layer covers selecting and steering LLMs for retrieval, reasoning, drafting, scoring and review tasks. It includes context engineering so model output is grounded in useful source material instead of loose prompt guesses.

  • Model selection
  • Context engineering
  • RAG reasoning
  • Output scoring
OpenAIGPT 5.4ClaudeVertex AICodex
Layer 2

Orchestration

The orchestration layer turns prompts into controlled workflows with prompt contracts, explicit workflow states, validation gates, retry controls and human checkpoints before anything important is published or handed off.

  • Prompt contracts
  • Workflow states
  • Validation gates
  • Retry controls
n8nPrompt contractsWorkflow statesRetry controls
Layer 3

Interfaces

The interface layer gives operators clear tools for submitting requests, reviewing generated work, comparing source and output, approving drafts and routing tasks through APIs or Slack integrations.

  • Operator review
  • API workflows
  • Slack requests
  • Approval screens
TypeScriptNext.jsReactPythonSlack integrations
Layer 4

Publishing

The publishing and infrastructure layer connects validated outputs to real destinations, including WordPress REST API publishing, API handoffs, deployment on Vercel, source control in GitHub and services hosted on Railway.

  • WordPress REST API
  • API handoffs
  • Vercel deployment
  • GitHub and Railway
WordPress REST APIAPIsVercelGitHubRailway

Contact

Bring one workflow. Get a practical AI route.

Send the workflow, bottleneck, users and whether you need an audit, pilot or senior input.

Start AI Automation AuditMessage on LinkedIn

Best first message

What should change?

A short note is enough: the workflow, the bottleneck, the users and the outcome you want to make repeatable.

Audit a manual workflow
Scope a focused pilot
Review an internal AI build