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Powered by FutureProof LLC
We partner with leadership teams to find the highest-impact workflows, build them in production, and set up governance so AI scales across the enterprise — in weeks, not quarters.
Each layer produces tangible deliverables and executive decision points. No slide decks that sit in drawers.
Translate opportunity into a governed portfolio with clear economics and sequencing.
Embed AI into the work — not next to it. Instrument outcomes and iterate.
Define how AI gets built, reviewed, and improved across functions and regions.
Engagements are structured around measurable outcomes: cycle time reduction, productivity lift, quality improvement, and cost to serve.
Most clients start with a portfolio and 1–3 deployed workflows, then scale with a formal operating model.
Leadership-ready strategy with quantified value cases and a 90-day plan. We interview stakeholders, score use cases by impact and feasibility, and deliver a roadmap your leadership team can approve and fund.
Ship workflows into production with guardrails and measurement. We don't just recommend — we build. Live automations inside your existing tools, running within weeks.
Process mapping
Redesign workshops that identify exactly where AI fits.
Prompt standards
Evaluation loops and quality benchmarks baked in.
Integrations
Connected to your CRM, docs, data, and existing stack.
Rollout & iteration
Enablement, training, and continuous improvement cadence.
Define ownership, policy, and decision rights for scaling adoption. Without this, pilots stay pilots.
Governance structure
Roles, decision rights, and accountability across functions.
Risk tiers
Approval pathways matched to workflow sensitivity.
Vendor management
Model selection, cost tracking, and switching criteria.
Executive dashboard
Measurement cadence with adoption, lift, and cost metrics.
Typical starting point: 6–10 weeks to deliver a governed portfolio plus 1–3 deployed workflows with measurable outcomes.
Each phase ends with executive decisions and a tangible artifact set. No ambiguity about what's been built or what's next.
Establish baseline, define objectives, and build the value case portfolio. Align leadership on prioritization, risk posture, and success metrics.
Deploy workflows and supporting controls. Instrument performance and run a structured iteration cadence.
Institutionalize standards, training, and governance so adoption expands without quality drift.
We build measurement into what ships. Leaders receive a consistent view of performance across all value cases.
Role-specific workshops where people build real outputs — not watch demos. Marketing ships content 3× faster. Sales researches accounts in minutes. Ops automates the boring stuff.
We don't teach "prompt engineering." We teach your teams how to produce real output at 3–5× speed using the tools they already have.
Once people start using AI, you need rules. We set guardrails so quality stays high and risk stays low — without killing momentum.
Reusable templates
Prompt libraries and output checklists your whole team can use.
Quality checks
Review rubrics so AI output gets better over time, not worse.
Clear rules
What data can be used, what needs approval, what's off-limits.
Usage dashboards
See who's using AI, how much time it's saving, and where to invest next.
We build guardrails that are practical for operators and visible to leadership. Fast doesn't mean sloppy.
Define what data can be used, where it can flow, and how it's handled.
Evaluation loops that prevent drift as usage increases across teams.
Clear pathways when systems should defer to humans.
Most programs begin with a 6–10 week phase to produce a governed portfolio and deploy the first workflows. Scale follows based on outcomes and risk posture.
We score by impact, feasibility, time to value, and risk. The result is a sequenced portfolio tied to owners, metrics, and a benefits model.
We implement evaluation rubrics, test sets, release gates, and human review for higher-risk workflows. Quality is monitored continuously after deployment.
Adoption improves when workflows fit existing tools and teams have standards. We pair deployment with role-based training, templates, and an operating cadence.
No. Services are modular. Many clients start with a roadmap and one deployment sprint, then add governance and training as usage scales.
We work across industries wherever knowledge work can be transformed: marketing, finance, operations, research, sales, and customer support.
We'll recommend a starting portfolio, identify the first deployment sprints, and define the governance required to scale adoption with control.