Your employees have AI.
Most of them aren't using it.
Our method: “The ONE SYSTEM” SM
We understand your workflows,
We train your people,
We encode your SOPs into the systems they already use,
We optimize your tokenization / credit use per task,
and roll them out by department, so what you pay for delivers maximum output based on your rules.
Teams we've done this work for
Ranked opportunity roadmap Example client, 420 employees
Prepared by GYA Media
Page 1 of 14
- GDPR exposure review
- EU AI Act readiness
- Access mapped to your SSO
- Audit trails on regulated workflows
- How we run it
Most enterprise AI integrations fail in 6 months.
Corporate growth goes hand-in-hand with technological innovations, yet during a pilot it is critical for your employees - the users - to understand how to best make use of this new type of resource. If this fails at any stage or level, they get disheartened and the migration fails, costing the business operational and resource drainage.
The tools are meant to cut resource allocation in half, while boosting productivity and efficiency. Not the other way around. Whether you use Claude, Copilot, or any other tool, every frontier model is capable of doing “a” job. What is usually missing from corporations is the layer between the tool and the company: employees who know what to actually do with it, and a system that already knows how your company operates instead of starting from zero on every single use.
That exact gap is what we come to bridge in GYA Media with our AI Implementation solutions for Enterprise adoption. We close the loop, fine-tune results, and deliver the system you imagined having before you started. Here is what bridging this gap looks like for one workflow.
Three things have to happen for AI Engines to actually change how your company operates.
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Your people learn to use it.
Not a company-wide email with a login link. A role-specific training, an internal champions program, and a 90-day adoption checklist benchmark so usage doesn’t decay the way it does after every generic AI rollout your competitors do.
- Company-wide literacy: what to prompt, what never to paste, how to draw results.
- Role-specific workshops: a sales session looks nothing like finance.
- One or two champions per department, trained to keep the momentum alive and in-house for when we are not physically there with you.
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Your AI , Your company, as ONE.
We turn your existing SOPs, playbooks, and institutional knowledge into something the AI can actually work from, so its output reflects how your company does things, not a generic ’best practice’ pulled from the internet randomly.
- Knowledge base wired into retrieval.
- Custom Projects, Skills, and agents per recurring task and department.
- A tailored maintenance process for when the SOPs update over-time.
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The ONE SYSTEM scales across every department that needs it.
Sales, marketing, finance, support, operations, engineering, HR. Each function gets a playbook built for how that function actually works. Our role is to provide a coherent task flow and quality verification mechanism to ensure each station gets what it needs as output.
- Written playbooks per function, with an owner-manager and central control.
- Integrations that run inside the CRM, helpdesk, ERP, or other custom connectors you need internally.
- A system that we upkeep for you continuously, minimizing time allocation from your ICT / CIO and Business Managers. Keep everything running smoothly and ensure business continuity.
What we build: The “ONE SYSTEM”.
Our approach: The ONE SYSTEM SM
Combines a set of 9 tailored services into a sequenced program of adaptation for your corporation. Each service produces a working part of The ONE SYSTEM SM, as a deliverable for your best infrastructure and engineering architecture per department.
AI Readiness & Opportunity Audit
A ranked roadmap of where AI saves the most hours or unlocks the most revenue, before you spend a dollar on tools. We interview department heads, shadow 3–5 real workflows per department, inventory your tools and data sources, and score every opportunity by effort-to-value.
- You get
- A ranked roadmap naming the top 8–12 opportunities, each with an effort estimate, expected hours saved or revenue unlocked per month, and a recommended sequence. A document, not a slide deck of generic use cases.
- Why it matters
- It prevents the most expensive mistake in this category: running a pilot in the wrong department first. Companies that skip it routinely lose 3–6 months on a pilot that never scales.
- Runs on
- No infrastructure decision yet. This stage is diagnostic.
Blue: expected value. Grey: build effort. Pilot candidates are high value, low effort.
Employee Training & Adoption Programs
Role-based training that gets employees started in using AI efficiently as day-to-day operations, not a one-off lunch-and-learn scheme. We focus on Company-wide AI literacy, role-specific workshops, and an internal champions program that keeps adoption alive when we are not there.
- You get
- Live workshops, in person or remote. Recorded modules for onboarding new hires. A written usage playbook per role. A 90-day adoption tracking check-in and benchmark.
- Why it matters
- Most AI adoption failures are training and structure failures, not always related as to tool failures. Training turns a sunk subscription credit cost into realized time savings, especially in revenue-facing roles.
- Runs on
- Whatever you already have. If nothing yet, Claude Pro or Team seats for the pilot group.
SOP-to-LLM Customization
Your SOPs, playbooks, past work product, and institutional knowledge turned into something the AI can actually operate from. Structured knowledge bases wired into retrieval, custom Projects and Skills built around recurring tasks, and agent instructions encoded directly from your SOP documents.A quality-control set for optimized outcomes.
- You get
- A working knowledge base connected to your chosen AI environment, a documented custom set of Skills, Projects, and agents mapped to specific recurring tasks, and a maintenance process for keeping it current as SOPs change.
- Why it matters
- Your ONE system knows everyone and every-thing it requires, per task. Nobody re-types context into a chat window on every use. The context is already loaded, which is often the largest time-saved number in the whole engagement, ultimately directly affecting the expected outcome.
- Runs on
- Claude for non-sensitive operational content. Local or private deployment where the data includes PII or restricted material.
- sops / sales4 files
- proposal-standard.mdupdated Aug 28
- pricing-rules.mdupdated Aug 12
- discovery-call-notes-format.mdJul 30
- sops / support6 files
- escalation-matrix.mdAug 20
- tone-and-refund-policy.mdAug 20
- skills3 active
- proposal-draftSales, 11 users
- ticket-replySupport, 14 users
- variance-noteFinance, local
Department-Specific AI Playbooks
One concrete, tested playbook per business function, built directly from the audit findings for your own company. Sales, marketing, finance, customer support, operations, engineering, HR: each department gets a workflow tailored to its actual workload and targeted outputs.
- You get
- A written playbook per department covering the workflow, the prompt or agent setup, the expected output quality bar, and who owns it, plus hands-on rollout support for the first 2–3 weeks of live use.
- Why it matters
- Within in this layer, The ONE SYSTEM turns the phrase: “Our company has adopted AI” into “Our company now closes more deals and retains more customers because of it.”
- Runs on
- Mixed by department. Finance and HR often route through a local model or private deployment. Sales, marketing, and engineering run on standard Claude seats or the API.
- Trigger. Deal enters "Proposal" stage. Owner: account executive.
- Draft. Run the proposal-draft skill against the deal record and discovery notes.
- Review. Edit scope and pricing sections only. Do not rewrite boilerplate.
- Quality bar. Matches proposal-standard.md, pricing within approved bands, no unapproved discounts.
- Send and log. Attach to deal. Usage recorded for the monthly report.
Escalate to sales lead if the deal is above the band or includes custom terms.
Workflow & Agentic Automation
AI wired directly into your CRM, ERP, email, Slack, Teams, and ticketing systems, using MCP servers, your existing automation platform, or custom-built agents, so it runs the process instead of sitting next to it.
- You get
- Example:
Working integrations with your systems, not just a tailored recommendation document. An agent that reads incoming leads, enriches them, and drafts the first outreach inside the CRM. A support agent that drafts grounded responses inside the helpdesk, and more. - Why it matters
- Adoption stops depending on employee discipline. An agent embedded in the CRM runs on every lead, every time, without anyone remembering to open a chat window.
- Runs on
- API-based: the Claude API or a private-hosted local model behind the same integration layer. Agents run programmatically, not through seats.
- 09:14:02New lead received from web form, CRM
- 09:14:02Enrichment: company size, sector, tech stack
- 09:14:03Matched to ICP tier 2, routed to territory EU-South
- 09:14:03First outreach drafted from outreach-sop, in rep's voice
- 09:14:04Draft saved to lead record, task assigned to owner
- 09:14:04Usage logged for monthly report
Infrastructure & Model Strategy Consulting
The ONE SYSTEM SM is the decision engine under every other service.
It is not limited or determined by whether you run on Claude subscriptions, the Claude API, a self-hosted open-weight model, or a hybrid split, and why. That is why we don’t “sell” a default answer. We consult and construct the correct answer for your data sensitivity, volume, latency, and compliance profile.
- You get
- A short, decisive recommendation covering which workloads go where, estimated cost at your actual volume under each option, and a migration and rollout plan. This is the starting point of The ONE SYSTEM SM method.
We do not create a generic 40-page nonsense. - Why it matters
- The ONE SYSTEM SM Audit prevents both expensive mistakes, like maintaining local infrastructure for a workload a subscription would have served for years, or paying enterprise seats for a handful of people who needed a smaller plan, and how they operate.
- Runs on
- This service is the decision. The workload table below is the framework we apply.
AI Governance, Security & Compliance
The policy and control layer that makes AI usage defensible, not just functional. An internal AI usage policy, access controls mapped to your identity system, audit trails for regulated workflows, and an exposure review against GDPR and the EU AI Act.
- You get
- A written AI usage policy, an access and permissions model mapped to your existing SSO and role structure, and a compliance exposure summary flagging any workflow that needs remediation before AI touches it.
- Why it matters
- A documented governance framework is frequently the precondition for AI adoption to be approved at all. It also brings existing shadow AI usage under control rather than banning it into the dark.
- Runs on
- Governance findings determine which workflows are local-only. This service and Model Strategy inform each other.
- 4.1AI tool access follows the existing SSO role structure. No separate login layer.
- 4.2Candidate data, payroll, and health information are processed only on the self-hosted model. No exceptions by request.
- 4.3Integration access is scoped to the named system and time-boxed to the build. Standing admin rights are not granted.
- 4.4Every regulated workflow keeps an access and usage trail, retained for audit.
Managed AI Operations Retainer
Once training, customization, and integrations are live, someone has to keep them working: monitoring adoption and usage, updating the knowledge base and SOPs as they change, re-prompting as models improve, and handling the edge cases that appear at real volume. This task costs time and resources internally to ICT / CIO s that need to allocate it elsewhere. Since we are trained and understand your workflows already, we handle benchmarks and operations on your behalf thereafter, ensuring operational success and KPIs run smooth.
- You get
- A monthly usage and adoption report, ongoing knowledge base and playbook maintenance, a standing office-hours channel for employee questions, and proactive recommendations as new model capabilities land.
- Why it matters
- Adoption decays without an owner. Usage quietly drops back to pre-engagement levels within 2–3 months if nobody maintains it, and the whole initial investment depreciates to zero.
- Runs on
- Whatever was chosen in Model Strategy. Right-sizing seats and catching workloads that outgrew their setup is a recurring deliverable.
- Seats right-sized: 4 moved from Team to Prosaved
- New hires onboarded into the system7
- Office-hours questions answered31
- Recommendation: move Support agent to newer modelqueued
Custom Tool & Agentic AI Builds
Bespoke internal tools built for your own stack and workflows, beyond what a standard integration covers. If you want internal dashboards, purpose-built chatbots, LLMs and RAG systems, or multi-step agents that don’t exist as an off-the-shelf product, we are here to tackle the project with expertise and a quality-first outcome.
- You get
- A working, deployed custom tool or Agent with documentation and a handoff to your team or an ongoing support arrangement.
- Why it matters
- Custom tools solve the specific bottlenecks that generic tooling can’t, at a fraction of the fully-loaded cost of hiring an in-house AI engineering function.
Our expertise...? We run our own day-to-day operations and admin on secure custom tools we built this way for us, too! - Runs on
- Case by case, and the easiest service to architect as a true hybrid: local model for a sensitive core function, Claude API for the rest of the same tool.
- InterfacePanel inside the CRM deal view
- InputsDeal record, product catalog, pricing bands, past quotes
- ReasoningClaude API, proposal and pricing skills
- Customer PIIRedacted locally before any external call
- OutputQuote draft, margin check, approval routing
- HandoffDocs, runbook, your team or our retainer
Not every workload belongs in the same place.
Your best understanding the solutions is key for us, to make the right decision. For example, some of your data can run through a fast, fully-managed AI subscription. Some of it legally or contractually can’t leave your infrastructure. Learn how you can deal with both.
We map every workflow against data sensitivity, volume, speed-to-value, and compliance exposure. Our role is to tell you exactly what runs where, before you buy or execute on anything.
| Workload type | Recommended path | Why |
|---|---|---|
| Sales outreach, marketing drafts, internal analysis | Claude Pro, Max, or Team seats | Fast, zero infrastructure, non-sensitive data. |
| Org-wide rollout with SSO and admin controls | Claude Enterprise or the API | Centralized control at scale. |
| PII, health, financial, or contractually restricted data | Local, self-hosted model | Data never leaves your infrastructure. |
| Mixed environments, which is most companies | HybridRouted automatically per request | Local for the regulated slice, Claude for everything else. |
This is the framework behind Service 6. The written version you receive names each of your actual workflows, its path, and the estimated cost at your real volume.
Why implement The ONE SYSTEM SM, and not hire internally?
- 15 years, 6,000+ projectsDelivered as a data-first execution partner, across markets and languages.
- 79 Enterprise AI implementations since 2025Built on AI implementation work our team has been doing quietly for clients for years. This is the first time we're offering it as a standalone service.
- We built our own systems firstWe run our own operation on a custom setup of the kind we build for you. The ONE SYSTEM is not the same for every partner. That is your unique advantage.
- The audit comes first, alwaysIf we can't show you where AI improves your numbers, you don't pay for a retainer.
Audit. Pilot. Scale.
The same three stages on every engagement, drawn to scale. Nothing gets built company-wide until the pilot is measurably working.
*timelines are indicative
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Audit
2–3 weeksWe map your real workflows, not your org chart, and hand you a ranked roadmap.
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Pilot
4–10 weeksWe build training, SOP customization, and integrations for the one or two highest-leverage departments first.
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Scale
OngoingWe roll the same system out department by department, and keep it running with a managed retainer.
How this is priced.
The ONE SYSTEM SM does not come with a flat package before we’ve seen your actual workflows. The right scope for a 150-person company and a 3,000-person company isn’t the same, and pretending otherwise is how AI budgets get wasted and sunk productions.
The 3-Step Method for AI Implementation (The ONE SYSTEM SM)
- Step 1: AI Readiness AuditThe entry point to every engagement, often credited against a later retainer.
- Step 2: Training, SOP customization, playbooks, integrationsScoped per project, based on audit outcomes.
- Step 3: Managed AI OperationsMonthly retainer once the pilot is live.
The questions we get before an audit is booked.
Getting started
We already have Copilot or Claude seats and nobody uses them.
That's the most common starting point for this exact engagement. The tool was never the problem. Start with the audit.
Our data is too sensitive for this.
That's exactly what Infrastructure & Model Strategy and Governance & Compliance are for. Sensitive workflows can run on a local, self-hosted model that never touches a shared cloud tool.
We want to build this in-house.
We can work alongside your team as a growth partner rather than a replacement, the same way we do on our marketing engagements. Most companies don't have a dedicated AI implementation function yet, and building one from scratch takes longer than this program.
How is this different from just buying more Claude or Copilot seats?
Seats without training and without your own SOPs built in are the reason adoption stalls in the first place. This is the layer between the tool and your actual company.
Management
How long before we see ROI, and how do you measure it?
The Readiness Audit gives you an hours-saved or revenue-unlocked estimate per opportunity before we build anything, so you have the number going in, not just after. Most clients see the first measurable result inside the pilot phase (4–10 weeks), and the Managed Operations Retainer reports on it monthly from then on, not just once at the end.
Will this reduce our headcount, or change anyone's role?
No, and that is not how we sell this. The goal is fewer hours per output and more output per employee, not fewer employees. Every playbook we build is scoped to remove the repetitive parts of a role, not the role itself.
How much of our own team's time does this actually require?
The Audit needs a handful of department-head interviews and workflow shadowing sessions over 2–3 weeks. Training needs the relevant team present for their own sessions, not the whole company at once. Outside of that, the build work happens on our side.
What happens if adoption stalls again after your team leaves?
That is exactly what the Managed AI Operations Retainer exists to prevent. SOPs change, new hires join, and usage quietly decays within a couple of months if nobody owns it. We keep it owned.
Can we start with one department before committing company-wide?
Yes, and we usually recommend it. The Pilot stage of our process runs against the one or two highest-leverage departments the Audit identifies, and scaling to the rest of the org only happens once that pilot is actually working.
IT & Security
Which AI models or vendors do you actually build on, and can we keep our current stack?
We are not tied to one vendor. Our Infrastructure & Model Strategy service starts from what you already have and recommends Claude subscriptions, the Claude API, a local self-hosted model, or a hybrid split, based on your actual data sensitivity and volume, not a preference for one vendor.
Do you need admin-level access to our systems, and how is that access controlled?
Only the access required for the specific integration being built, scoped and time-boxed, never broad standing admin rights. Every access grant is documented and reviewed as part of the Governance & Compliance service, and revoked at the end of the engagement unless it continues under the retainer.
How do you handle data residency? Where does our data actually go, and who can see it?
That is decided workflow by workflow, not once for the whole company. Non-sensitive work can run through a managed Claude subscription. Anything with PII, health data, or contractual restrictions gets scoped to a local, self-hosted model or a private deployment that never leaves your infrastructure. You get a written map of exactly which workflow goes where before anything is built.
How does this fit with our existing SSO and identity management setup?
Access is mapped to whatever identity system you already run, not a separate login layer bolted on top. This is built directly into the Governance & Compliance engagement, so permissions follow your existing role structure from day one.
What audit trail or logging do you provide for compliance review?
Every regulated workflow gets a documented access and usage trail as part of the Governance & Compliance service, built to hold up under an internal or external audit, not just a usage dashboard for our own reporting.
Your competitors already have AI integrations.
Do you?
Start with your tailored ONE SYSTEM SM Audit.
See exactly where AI Engines can move your numbers, before you commit to a larger-scale investment.
Test your operations and team for AI-Readiness!