AI Consultancy Practical, safe, measurable

Make AI useful in your business — without the hype.

[COMPANY_NAME] helps teams adopt AI in a way that’s repeatable, auditable, and actually saves time. Strategy, workflows, and implementation support — tailored to your tools and people.

LLM workflows Prompt systems Integration & automation Governance & safety
Talk to me What I do
Based in: [CITY, COUNTRY] • Typical engagement: 2–6 weeks • Remote / onsite: [REMOTE/ONSITE]

What you get in week one

  • AI opportunity scan (quick wins + high-value bets)
  • Risk & data-safety checklist (what not to do)
  • Two pilot workflows your team can ship immediately
  • Measurement plan (time saved, quality, adoption)
[X]%
placeholder KPI
[Y] hrs
placeholder KPI
Credibility note
Replace with something concrete: “Shipped AI-assisted workflows using structured prompts + human review.”

Services

Pick one, or we blend them into a single engagement.

AI Strategy (No fluff)

Map where AI helps, where it harms, and what “success” means.

  • Use-case prioritisation
  • Data & risk assessment
  • Tooling recommendations

Workflow Design

Turn ad-hoc prompting into a repeatable system.

  • Prompt templates + guardrails
  • Structured outputs (JSON, docs)
  • Human-in-the-loop review

Integration & Delivery Support

Embed AI into your stack so it saves time every day.

  • API + automation patterns
  • Internal tools and portals
  • Rollout & adoption

Governance & Safety

Avoid the classic “we leaked something” moment.

  • Data handling rules
  • Evaluation checklists
  • Policy + training

Team Training

Get your people competent fast — not dependent.

  • Prompting fundamentals
  • Use-case clinics
  • Playbooks & templates

Rapid Pilot (2 weeks)

Ship something tangible quickly to prove value.

  • 2 workflows shipped
  • Measurement + ROI story
  • Next-step roadmap

Evidence

This section is written to sound credible without inventing claims. Swap placeholders with your real examples.

How I make AI reliable

  • Constraints: clear roles, inputs, and output formats.
  • Repeatability: templates and guardrails — not one-off prompts.
  • Verification: human review where accuracy matters.
  • Integration: AI connects to your existing tools and data.
  • Measurement: time saved, quality improved, adoption tracked.

Proof points (placeholders)

[Example 1: workflow shipped]
What it did • Who used it • Outcome
[Example 2: prompt system]
Inputs • Guardrails • Output format • Why it reduced errors
[Example 3: integration]
Stack • Trigger • Destination • Business impact

Case studies

Short, specific, and measurable beats flashy every time.

[Case Study A]

Placeholder

Problem → Approach → Outcome.

  • Problem: [ ]
  • Approach: [ ]
  • Outcome: [ ]

[Case Study B]

Placeholder

Problem → Approach → Outcome.

  • Problem: [ ]
  • Approach: [ ]
  • Outcome: [ ]

[Case Study C]

Placeholder

Problem → Approach → Outcome.

  • Problem: [ ]
  • Approach: [ ]
  • Outcome: [ ]

FAQ

The stuff clients actually ask (and it signals you’re sensible).

Both. I can run strategy and workflow design, or support implementation with your team (or trusted dev partners). We keep it practical and measurable.

Usually it replaces parts of tasks, not whole roles. The best ROI is often in summarising, drafting, classifying, searching, and improving consistency — with human oversight.

We start with a data map, define what can/can’t be used, choose tools accordingly, and add process guardrails. High-risk outputs always stay human-reviewed.

Before/after time studies, cycle time reduction, quality checks, and adoption. If we can’t measure it, we treat it as “nice-to-have”.

Contact

For now, this is deliberately simple: a mailto link. Later you can wire up a proper form endpoint.

Email me Book a call
Email: hello@example.com
Location: [CITY, COUNTRY]
Company: [COMPANY_NAME] • [COMPANY_NUMBER] (optional)

Quick “AI readiness” checklist

This is a subtle credibility flex: useful, grounded, not salesy.

  • Which tasks are repetitive and text-heavy?
  • Where are mistakes expensive or risky?
  • What data must never leave your environment?
  • How will we measure improvement?
  • Who approves outputs in week one?
Next enhancement idea
Add a “Resources” page with: AI policy template, evaluation checklist, prompting playbook.