Case Studies

A consistent format (context, role, deliverables, and outcomes).

Cross-Department SEO Workflow

SEO Reporting Workflow Overhaul

Anonymized Mid-Market Marketing Agency

Service: AI Content Ops Setup + SOPs + Fractional Strategy
Focus: Operational alignment + reporting reliability + stakeholder communication
Role: Cross-functional operator (strategy + process design + implementation)

Context

The SEO team did not have a clear workflow for monthly reporting and cross-team communication was fragmented. Account managers didn't know when SEO reporting was due, SEO specialists weren't aligned on production timelines, and clients didn't know when to expect deliverables. Reporting delivery was inconsistent despite clients paying for regular reporting, and strategy discussions were reactive instead of proactive because performance data wasn't reliably surfaced.

This created internal friction, client confusion, and missed opportunities to tie SEO insights into omnichannel strategy.

The Challenge (Detailed)

We identified the operational breakdown and escalated it to the executive team with a clear problem statement and proposed path forward.

The core issues included:

  • Account managers didn't know when SEO reporting was due
  • SEO specialists weren't aligned on when to produce reporting
  • Clients didn't know when to expect deliverables
  • Reporting delivery was inconsistent despite clients paying for regular reporting
  • Strategy discussions were reactive instead of proactive

The Solution

We helped design and implement a cross-department SOP and workflow system that clarified:

  • Who owns what — SEO specialists vs account managers vs leadership oversight
  • When work happens — Standardized timelines and deadlines
  • How communication flows — Handoffs, status updates, client-facing messaging
  • How questions get answered — Clear channels + response expectations
  • How reporting is packaged — Consistency and data accuracy

Implementation Components

1. Reporting Calendar

A clear schedule for when reports are produced, reviewed, and delivered. A shared system that account managers and SEO specialists could reference.

2. Cross-Functional Handoffs

Standard handoff points so account managers knew what to expect and when. Prep steps so account managers could lead better client conversations.

3. Client Communication Protocol

Consistent client messaging about reporting timelines. A predictable rhythm that built trust and reduced escalations.

4. QA + Data Consistency

Checks to ensure accuracy of reporting inputs. More reliable performance narratives that could support broader strategy.

5. Strategy Enablement

The new process ensured reporting arrived on time, enabling account managers to connect SEO insights to omnichannel execution.

Tools & Artifacts Created

  • SEO Reporting SOP (step-by-step)
  • Reporting schedule / timeline map
  • Handoff checklist for account managers
  • QA checklist for reporting accuracy
  • Communication guidelines for client reporting cadence

Outcome

Operational Outcomes

  • Reporting became predictable and easier to coordinate
  • Teams were aligned on deadlines and expectations
  • Account managers were prepared for client conversations

Client Experience

  • Clients received reporting consistently as scoped
  • Questions were handled more systematically and proactively

Strategic Impact

  • Reporting accuracy and timeliness improved decision-making
  • Performance insights could be integrated into broader marketing strategy

This wasn't "just process." It was operational surgery that restored trust, improved delivery reliability, and created the conditions for stronger strategy execution across channels.

Web Build Quality Control

Web Build QA/QC Workflow Redesign

Anonymized Local Service Business (Multi-Location)

Service: SOPs + Workflow Design + Fractional Digital Strategy Lead
Focus: QA/QC + cross-department alignment during web design/build
Role: Cross-functional lead (requirements → process design → implementation)

Context

During a website build, quality control broke down because workflows were misaligned across teams, web development, design, content, and SEO. The gaps created fragmented communication and unclear handoffs, inconsistent ownership of quality checks, and multiple content errors discovered late in the build. This led to increased rework, delays, and client frustration.

Teams were optimizing for "getting tasks done" rather than aligning to the client's goals and vision. The client experience suffered because the process was mechanical and reactive, not intentional and collaborative.

The Challenge (Detailed)

We surfaced the client pain points and traced them to the root cause: workflow gaps and an unclear QA/QC system.

Specific problems included:

  • Fragmented communication and unclear handoffs between disciplines
  • Inconsistent ownership of quality checks
  • Multiple content errors discovered late in the build
  • Increased rework, delays, and client frustration
  • Teams optimizing for task completion rather than client goals

The Solution

We led the creation and rollout of a new workflow that defined clear checkpoints, separated QC responsibilities by discipline, ensured teams connected at the right moments, and centered the client's business story and goals throughout the build.

Department-Specific QC Lanes

Technical QA

Functionality, responsiveness, performance basics, broken links, forms, tracking, redirects

Design QA

Spacing, typography consistency, layout fidelity, accessibility checks

Content QA

Accuracy, tone, grammar, brand voice, service details, compliance-sensitive wording

SEO QA

Metadata, headings, internal linking, indexability basics, page intent alignment

This separation reduced confusion ("who checks what?") while keeping everything synchronized through shared milestones.

Shared Workflow Checkpoints

A single, agreed workflow that determined:

  • When design is "ready" for content placement
  • When content is "ready" for SEO review
  • When pages are "ready" for technical QA
  • When final sign-off occurs and what "done" means

Stronger Stakeholder Alignment

Instead of focusing only on production tasks, we realigned the build around:

  • The client's goals
  • Their vision and voice
  • What mattered most to their customers

Dedicated Content Writer

We introduced a dedicated writer to support the design/build process, including direct client discovery:

  • Company story, language, and differentiators
  • Community involvement, awards, longevity, achievements
  • Services, positioning, and customer pain points

This created a more customized site experience that actually reflected the business—reducing content errors and elevating quality.

Tools & Artifacts Created

  • Web Build QA/QC SOP (step-by-step)
  • QC checklists per department (technical, design, content, SEO)
  • Milestone and handoff workflow map
  • Client discovery + content intake template
  • Definition of Done (final sign-off criteria)

Outcome

Delivery Outcomes

  • Reduced cross-team confusion and prevented late-stage surprises
  • Improved quality consistency across design, content, SEO, and technical implementation
  • Fewer content-related errors surfaced late in production

Client Outcomes

  • A more custom, authentic site narrative aligned to client goals
  • Improved stakeholder confidence due to clearer communication
  • Fewer "fire drills" and last-minute revisions

Operational Outcomes

  • A repeatable SOP that can be applied across future builds
  • Cleaner handoffs and fewer redundant revisions
  • Process can be scaled to other web projects

This was a systems fix, not a band-aid. By separating QA responsibilities and synchronizing workflows, we improved quality, reduced rework, and made the client experience feel intentional—grounded in their story and goals, not just production output.

AI Content Operations

Client-Specific AI Chatbots + Prompt Library

Anonymized Local Services Brand (Growth-Focused)

Service: AI Content Ops Setup + Prompt Library + Knowledge Governance
Focus: Business-specific content, reporting, and strategy outputs (non-generic AI)
Role: AI ops lead (information architecture → knowledge system → QA + maintenance)

Context

The client needed faster content production and sharper strategic insights, but generic AI tools produced outputs that were not specific to the business or its customers, inconsistent with brand voice and positioning, not grounded in competitor reality or current performance context, and risky to use without verification due to accuracy concerns.

The result: time lost rewriting outputs, uncertainty about what was true, and limited trust in AI.

What We Did

We built a system of client-specific AI chatbots designed to function like a mini marketing agency—while prioritizing accuracy, trust, and repeatability.

This included three core components:

  • Information Architecture (IA) for the knowledge base — Designed a clear structure for information the bots rely on, so context was organized, searchable, and maintainable over time
  • Fact-checked business intelligence — All research inputs were validated and QA'd to reduce hallucinations and prevent inaccurate recommendations
  • Custom prompt library tied to real workflows — Prompts built to produce consistent outputs for specific tasks, with guardrails that enforced brand voice and required the model to cite provided context

The Solution

1. Knowledge Base Information Architecture

We created a structured IA to organize the client's "source-of-truth" context, including:

  • Business overview + service catalog
  • Differentiators, strengths/weaknesses, and positioning
  • Target customer segments and language patterns
  • Competitor landscape and comparative insights
  • Brand voice rules + messaging do's/don'ts
  • Visual guidelines (brand colors, style constraints)
  • Reporting context (what the business tracks and why it matters)

This structure reduced drift, improved relevance, and made updates straightforward.

2. Fact-Checking + QA Process (Trust Layer)

To address accuracy concerns, inputs were:

  • Reviewed for consistency and correctness before being added
  • Updated regularly to prevent outdated information from driving outputs
  • Governed by QA checks (e.g., "confirm against source notes," "flag uncertainties," "avoid unsupported claims")

Where the bot could not confirm something from the approved context, prompts instructed it to ask clarifying questions rather than guess.

3. Prompt Library Built for Production Work

A prompt library was created for repeatable tasks, such as:

Web Copy

Service pages, FAQs, landing pages

SEO Content

Briefs, topic clusters, on-page suggestions

Campaign Concepting

Ads, emails, repurposing strategies

Reporting Narratives

What changed, why, recommended next steps

Strategy Options

Prioritization, experiments, channel alignment

Prompts were designed to:

  • Reference approved context first
  • Maintain brand voice
  • Produce output formats the team could use immediately (templates, outlines, briefs)

4. Ongoing Maintenance + Governance

The system included an update cadence and a lightweight governance checklist so the AI stayed current and consistent.

Tools & Artifacts Created

  • Knowledge base IA (structured context categories + update workflow)
  • Client-specific AI chatbot system (guardrails + approved context)
  • Prompt library by function (content, SEO, reporting, strategy)
  • QA checklist for research inputs + output review
  • Maintenance cadence (keeping info current and accurate)

Outcome

Efficiency + Quality

  • Reduced time spent drafting content and reporting narratives
  • Improved consistency across messaging and deliverables

Higher Trust

  • Fact-checked inputs + QA guardrails reduced inaccurate and generic results
  • Teams were more willing to use AI because outputs were reliable and specific

Strategy Enablement

  • Outputs were grounded in the client's market realities, positioning, and goals
  • More actionable recommendations tied to real business context

Most AI adoption fails because teams don't trust the outputs. This worked because it treated AI like operations: structured information architecture, fact-checked inputs, repeatable prompts, and governance—so the client got human-centered outputs that were specific, reliable, and aligned to their customers.

Organizational Operating System

Org-Wide Operating System Overhaul

Anonymized Multi-Department Marketing Organization

Service: Fractional Digital Strategy Lead + AI Content Ops + SOP/Workflow Architecture
Focus: Cross-department cohesion, role clarity, repeatable onboarding, operational performance
Role: Fractional operations/digital lead (discovery → audit → system design → rollout)

Context

The company was operating with inconsistent, person-by-person processes across departments. Each team had its own way of working, and individual contributors often relied on informal "what works for me" methods. This created company-wide friction including ambiguity in role expectations and ownership, unclear authority vs. accountability, inconsistent handoffs and duplicated effort, difficult onboarding where new hires couldn't "plug in" quickly, and leadership goals losing traction due to lack of standard operating rhythm.

The executive team wanted a repeatable organizational system where every department had clear responsibilities, defined authority and accountability, workflows that supported consistent delivery, and an onboarding-ready structure that scaled.

What We Did

We led a structured discovery and audit across the organization:

Discovery Process

1. Executive Discovery

Met with the executive team to document pain points, business goals, and what "good" looked like operationally.

2. Department Leader Interviews

Met with individual department managers to understand reality on the ground: bottlenecks, handoff issues, tools, and current workflows.

3. Cross-Level Audit

Mapped gaps between leadership expectations and operational execution, highlighting where ambiguity was causing breakdowns.

4. Anonymous Employee Feedback

Created surveys by department to capture what people actually do day-to-day, where processes fail or slow down, what would reduce friction, and where responsibility/ownership is unclear.

The Solution

Using the audit findings, We designed and implemented a company operating system centered on clear roles, repeatable workflows, and accessible documentation.

1. Information Architecture for Source-of-Truth Library

We created a structured information library (a searchable internal database) so knowledge didn't live in people's heads. It was designed to be:

  • Easy to navigate by role, department, and workflow
  • Usable for onboarding and ongoing reference
  • Versioned and maintainable over time

2. Department-Level AI Content Ops Systems

For each department, we created AI-enabled content/ops support (where appropriate), including:

  • Workflow templates and standardized briefs
  • Prompt libraries aligned to department tasks and brand voice
  • QA guardrails (accuracy, consistency, tone, handoff readiness)
  • Repeatable "how we do this here" guidance that removed guesswork

3. SOPs + Workflows Aligned to Business Goals

Working with exec leadership and department heads, we translated the operating model into SOPs that clarified:

  • Job responsibilities and expectations per role
  • Ownership of key deliverables
  • Authority vs. accountability at each step
  • Cross-team handoffs and communication checkpoints
  • Definitions of "done" so quality and outcomes were consistent

4. Rollout + Training Enablement

Leadership trained teams on the new structure and workflows using the documented system. The goal was adoption, not just documentation,so teams could execute with clarity immediately.

Tools & Artifacts Created

  • Org-wide discovery audit (exec + managers + employee feedback)
  • Role clarity framework (responsibilities, expectations, authority/accountability)
  • Department SOP library + workflow maps
  • AI prompt libraries and templates by department
  • QA standards and checklists (accuracy, readiness, handoff criteria)
  • Structured internal knowledge base (information architecture + governance)

Outcome

Operational Cohesion

  • Teams aligned around shared processes and expectations
  • Reduced ambiguity across roles and departments
  • Clearer handoffs improved cross-team delivery

Onboarding Readiness

  • New employees could ramp faster using the source-of-truth library and SOPs
  • Less dependence on tribal knowledge

Performance Outcomes

  • Increased productivity through reduced friction and duplicated work
  • Improved delivery consistency supported stronger results
  • Contributed to increased profitability through better operational execution

This engagement wasn't about adding more documentation—it was about building a repeatable operating system. By combining fractional leadership with AI-enabled content ops, structured information architecture, and enforceable SOPs, the company gained clarity, cohesion, and scalable execution.

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