Engagement 01
Automation feasibility sprint
A bounded diagnosis of workflow economics, data readiness, risk and the smallest useful pilot.
- Workflow map
- Cost model
- Risk boundary
- Pilot specification
04 / Capability
AI automation for a real bottleneck, not a presentation slide.
AI is our highest-value but most selective practice. We use it where the economics, data and human-review model are clear. That can mean research support, content intelligence or workflow orchestration, but never automation theatre for its own sake.
Marketing Central / AI workflows & automation / 04
When this becomes useful
Start here when the problem looks like this.
01
A frequent research, review or coordination task consumes senior time
02
The workflow has enough repetition and data to evaluate reliably
03
A human-in-the-loop pilot is more valuable than a broad transformation programme
04
The team needs an independent feasibility view before buying more software
What it includes
A complete working system, not a menu of disconnected outputs.
01
Workflow and feasibility diagnosis
02
Research and knowledge systems
03
Content intelligence tooling
04
Human-in-the-loop agent workflows
05
Evaluation and approval frameworks
06
Prototype-to-production planning
Typical engagements
Three useful ways to begin.
The exact scope follows the problem. These formats make the first commitment easier to understand and evaluate.
Engagement 02
Human-in-the-loop pilot
A narrow working system tested with real inputs and explicit evaluation criteria.
- Prototype
- Integrations
- Review interface
- Evaluation harness
Engagement 03
Content intelligence system
Research, retrieval and performance intelligence for teams producing high-stakes content at scale.
- Knowledge layer
- Research workflow
- Quality checks
- Learning signals
How it works
From ambiguity to a repeatable way of working.
Price the bottleneck
We quantify the time, delay, error or opportunity cost before proposing any technical system.
Design the human boundary
We define what the system may suggest, what it may execute and what always requires approval.
Prove the narrow workflow
A useful pilot solves one expensive problem with real inputs and measurable evaluation criteria.
Operationalise deliberately
Only after the workflow is proven do we address integrations, monitoring, permissions and ongoing ownership.
What should change
Outcomes the work is designed to create.
- A precise view of what should and should not be automated
- A smaller pilot tied to a measurable operational constraint
- Human review preserved at the decisions that carry risk
- A path to production that accounts for data, evaluation and maintenance
What we watch
Measurement that fits the actual job.
M01
Human time released
M02
Error and review rate
M03
Cycle-time reduction
M04
Cost per completed workflow
Questions before we begin
Useful answers, before a sales call.
01Do you begin with a specific AI model or vendor?
No. We begin with the workflow, economics, data and risk boundary. Model and infrastructure choices follow from the job the system must perform and the level of control the organisation needs.
02Can you help when an existing AI API is unavailable?
Yes. Depending on the workflow, alternatives can include a deterministic product flow, another hosted model, a smaller self-hosted model or removing AI entirely where it does not improve the experience.
03How do you reduce hallucination and quality risk?
We constrain scope, ground outputs in approved sources, define evaluation criteria, preserve provenance and keep human approval at decisions where a plausible mistake would be costly.
04Will you automate the entire workflow immediately?
Usually not. We prove the narrowest valuable unit first. That exposes data, evaluation and adoption problems before they become expensive platform problems.
Continue with context
Related routes through the work.
Related capability
Corporate communications that make expertise impossible to overlook.
Distinctive executive voices, credible thought leadership and corporate narratives that earn attention without sounding manufactured.
Related capability
SEO built around the questions buyers actually ask.
Editorial systems built around real search behaviour, category authority and content that compounds instead of disappearing after publication.
The next useful step
Bring us the version that is not ready yet.
We can help determine whether this needs a focused project, a repeatable system or a smaller first experiment.
Start the brief