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ARCTUS AI

How the work is done

Four services, each a fixed-scope decision. Here is the kind of problem each one takes on, and what it looks like in practice.

Opportunity Audit

Find the AI applications worth funding, and the return each should produce.

Safe-Box Prototype

Design a sandboxed prototype, its guardrails, and the tests it must pass.

Production Readiness

Specify what a working prototype needs to run safely at volume.

Fractional AI Director

Senior AI judgment on architecture, vendors and build-versus-buy, part time.

Opportunity Audit

Problems it takes on:

  • A list of AI ideas and no way to rank them by money.
  • A department carrying a manual load nobody has measured.
  • AI tools already paid for, with no evidence they are working.

What you get:

  • The Business Impact Matrix: A clear visual map separating high-value quick wins from costly technical distractions.
  • The Data Gap Assessment: An honest, plain-English report showing what data is ready to use today and what needs to be cleaned first.
  • The 90-Day Execution Roadmap: A highly tactical, step-by-step plan to move your first high-impact system from concept to a live deployment.

Sales · In practice

Ranking where AI pays in a sales team

Result: Two applications came out on top, a speed-to-lead model and a prospect recommendation tool. Together they produced an estimated 8% conversion lift in the targeted segments.

Challenge: Reps spent hours researching prospects and often reached out at the wrong time.

Approach: Analysed more than 2 million customer interactions to find where timing and targeting actually moved conversion, before anything was built.

Safe-Box Prototype

Problems it takes on:

  • An idea worth testing, and no safe way to try it on real data.
  • Leadership wants a demo, and nobody has said what counts as success.
  • A prototype that needs private customer data kept away from outside AI models.

What you get:

  • The Prototype Blueprint: The architecture, data flows and model choices for a sandboxed prototype, written so any competent team can build it.
  • Data Privacy Guardrails: The rules and filters that mask private customer data (PII) before it ever touches a public AI model.
  • Acceptance Tests: A written pass/fail test set the prototype must meet before anyone spends money taking it to production.

Marketing · In practice

A campaign recommender with an approval gate

Result: Targeting moved from guesswork to evidence, and volume grew without public discounts.

Challenge: Facing a revenue gap, marketers fell back on blasting the whole database or discounting deeply, which tired the audience and cut margin.

Approach: Designed a recommender that matched a short-term revenue goal to small, pre-built customer segments. A person approved every recommendation before any content was generated or sent.

Production Readiness

Problems it takes on:

  • A prototype that worked in a demo and has never touched real systems.
  • No plan for how anyone will know when accuracy slips.
  • No clear point where a person approves what the system does.

What you get:

  • Production Architecture: Integration design for your existing databases and systems, sized for real volume, with human-in-the-loop approvals and kill switches built in.
  • Monitoring Plan: The accuracy and drift measures to track, the thresholds that trigger an alert, and who acts when one fires.
  • Go-Live Review: A written readiness review against the specification, so the decision to launch rests on evidence.

Operations · In practice

An internal assistant for frontline teams

Result: Manual procedure lookups dropped, and frontline staff got to answers faster.

Challenge: Frontline teams lost hours working through complicated internal revenue procedures.

Approach: Took a secure assistant grounded in internal documents and data from prototype to daily use by sales and customer success staff.

Operations · In practice

Turning customer feedback into directives

Result: Operators received specific directives in place of dashboards they had to interpret.

Challenge: Satisfaction surveys showed scores falling but could not say what to fix.

Approach: Paired standard attribution models with an AI step that read thousands of free-text comments to find the root causes behind the scores.

Fractional AI Director

Problems it takes on:

  • AI decisions made by whoever is loudest, with no record of why.
  • Vendors promising more than their software does.
  • A board asking what the AI spend is returning.

What you get:

  • Vendor Vetting Reports: Unbiased, technical evaluations of third-party AI tools before you commit to expensive software contracts.
  • Architecture Decision Records: A written ruling each month on the design and build-vs-buy decisions in front of your team, so choices are documented once rather than relitigated every sprint.
  • Ongoing ROI Tracker: Continuous tracking of system performance, operational hours saved, and cost-per-task metrics to report back to your board.

Leadership · In practice

Bringing order to AI decisions

Result: AI work moved from scattered experiments to a governed portfolio that reached production.

Challenge: AI interest spread across many teams with no shared standard for deciding what to build.

Approach: Led enterprise AI programs in regulated industries: an intake process, architecture standards, vendor evaluation, and a written case for each project.