Methodology

How engagements actually run

Four stages from discovery to compounding results — with deliverables, review cadence, and a clear role for AI at every step.

The four stages

Depth overguesswork

Whether we are automating a service inbox, rebuilding a conversion path, or scaling Google Ads, the rhythm is the same: understand deeply, plan precisely, ship carefully, and improve relentlessly.

01DiscoveryWeek 1–2

Map how work moves — before we propose a stack.

We interview operators, shadow intake channels, and audit the tools already in play. The goal is not a generic SWOT slide: it is a ranked list of friction points where automation, site, or acquisition will pay back first.

What we do: Stakeholder workshops and funnel / ops walkthroughs · Channel, CRM, and workflow audits · Baseline metrics: volume, cycle time, conversion, cost

Deliverables
  • Opportunity map ranked by impact vs. effort
  • Audience and competitor snapshot
  • Constraints register (data, compliance, integrations)
AI in this stage

In the loop: AI assists document and inbox sampling so we spot patterns faster — humans still own judgment on what matters.

02StrategyWeek 2–3

Turn insight into a phased roadmap with clear KPIs.

Strategy decides sequence: which workflows to automate first, what the site or campaign must prove, and how reporting will prove it. Everything is scoped so engineering, design, and growth share one plan — not three conflicting briefs.

What we do: Service mix and phase prioritization · KPI framework and instrumentation plan · Integration and data-flow design

Deliverables
  • Channel & automation roadmap
  • Success metrics and review cadence
  • Phased timeline with named owners
AI in this stage

In the loop: We specify where models extract, score, or draft — and where a human must approve before systems of record update.

03ExecutionSprints ongoing

Ship focused sprints — demos you can react to early.

Designers, engineers, and growth specialists build against the roadmap. You see working slices early — intake flows, landing pages, campaigns, or dashboards — not a big reveal at the end. Status stays transparent so scope stays honest.

What we do: Sprint builds with weekly demos · QA, analytics, and access setup · Internal training and handoff docs

Deliverables
  • Live workflows, sites, or campaigns
  • Instrumentation and exception queues
  • Playbooks for your operators
AI in this stage

In the loop: Production prompts, scoring rules, and fallbacks are tuned on your real samples — then monitored like any other product surface.

04OptimizationOngoing

Treat launch as the starting line, not the finish.

We watch conversion paths, ad efficiency, SEO movement, and automation exception rates. Experiments land in a backlog; winners get promoted. Budget and feature recommendations follow the data, not gut feel.

What we do: KPI reviews against the agreed framework · A/B tests and workflow refinements · Scale / pause recommendations

Deliverables
  • Performance reviews with next actions
  • Prioritized improvement backlog
  • Scaled spend and feature proposals
AI in this stage

In the loop: Models are retrained or re-prompted when error rates climb; humans keep ownership of edge cases and policy.

How we stay aligned

Cadence you cancount on

Process only works if communication does. These rituals keep strategy, build, and results in the same conversation.

  1. 01

    Kickoff clarity

    Goals, access, and success metrics are locked in writing before the first sprint so “done” is unambiguous.

  2. 02

    Weekly demos

    You see what shipped, what is blocked, and what we need from your side — no black-box status emails.

  3. 03

    Monthly KPI reviews

    We read the numbers together, decide what to double down on, and adjust roadmap priorities in the open.

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