Artefacto 41 enables every team to access AI under a single structure with defined roles, cost control, and connection to your internal systems. No isolated experiments. With measurable results.
Artefacto 41 helps organizations move from fragmented experiments to governed execution. Strategy, frontline workflows, and measurement stay connected so adoption scales with control.
Strategic alignment
Before enabling assistants or workflows, we define which business variable must improve: cycle time, operating cost, response quality, or role-level productivity. AI is not introduced because it is trendy, but because it delivers measurable impact.
ObjectiveDefine what should improve, where, and by how much.
OutcomeLeadership prioritizes AI investment with financial and operational criteria.
Operational execution
Teams work from a single interface that connects multiple models and providers. From there, they can run governed assistants, RAG on internal documentation, Text2SQL queries over databases, and agents with controlled internet access. Everything in one environment. No tool switching. No knowledge fragmentation.
ObjectiveTurn isolated requests into repeatable workflows embedded in real operations.
OutcomeAI becomes a daily work habit.
Governance and economics
Artefacto 41 lets you define roles, capabilities, and usage limits by area. You can decide which models are enabled, which internal sources can be queried, and how much budget each team can consume. Pay only for what you use, without overspending on individual seats.
ObjectiveProtect data, quality, and budget without blocking speed.
OutcomeLeadership controls cost and risk while teams work with autonomy.
Evolving capability
Artefacto 41 is built to evolve: it adds new tools, automations, and integrations while keeping architecture and user experience consistent. Beyond its core toolbox, the platform supports custom modules connected to your own data sources to run custom workflows.
ObjectiveExpand capabilities quickly without multiplying contracts, platforms, or risk.
OutcomeThe platform becomes durable AI infrastructure for the whole organization.
The goal is not to deploy another assistant. The goal is to redesign how teams decide, respond, and execute with AI embedded in everyday work.
One platform for simple requests, advanced workflows, and organization-wide adoption without seat-based cost penalties.
Who is it for
Adoption does not become cross-functional through licensing alone. It becomes cross-functional when every function can execute meaningful work in a governed environment with the right guidance and visibility.
01 / 09
Why was it created?
In enterprise projects, we see the same pattern: AI is either limited to leadership or rolled out broadly without guidance. The result is predictable: low usage quality, underused licenses, and a gap between spend and business impact.
01
When licenses are enabled only for middle management, the rest of the organization continues with traditional processes. Productivity does not scale.
02
Per-user models create fixed costs regardless of actual usage. Investment does not always translate into operational impact.
03
Usage without clear control over data, models, and sources can create security, compliance, or quality issues.
04
Without a shared organizational foundation, each initiative becomes an isolated experiment. Structural capability is never built.
Pricing
Pricing combines a fixed deployment fee, an optional recurring service fee, and costs tied to additional implementations. Use the calculator below to estimate monthly costs and compare against fixed per-user solutions.
Company details and AI usage
Adjust team size and adoption level to simulate monthly service costs.
Usage intensity
Cost comparison
Compare monthly cost against popular alternatives based on individual seat licenses.
Artefacto41
$1,922
ChatGPT Business
You save 49% with Artefacto 41
$3,750
Microsoft Copilot 365
You save 57% with Artefacto 41
$4,500
Perplexity Pro
You save 36% with Artefacto 41
$3,000
Estimated monthly cost: $1,922
Annual estimate: $23,068
Costs are approximate and use real AI API prices plus usage benchmarks to estimate these totals.
Pricing combines three layers: a fixed deployment fee, an optional recurring maintenance/supervision service, and scoped pricing for additional implementations (new tools, workflows, or integrations). This keeps cost tied to delivered capability, not to simple seat inflation.
Yes. Most clients start with one high-impact use case and one team, then scale in phases. The same governance model, operating standards, and UX patterns are reused as rollout expands, so growth stays controlled instead of fragmented.
Yes. Integrations are a core part of delivery and are prioritized by business impact. Depending on your stack, we can connect to internal data sources, business tools, and workflow systems to keep execution inside your current operating environment.
Your company does. Deployment runs on your infrastructure and under your provider contracts. Artefacto 41 provides the interface and orchestration layer while your organization retains ownership and control over data, access, and compliance boundaries.
No. Conversations are only one interface. The platform also includes document intelligence, SQL and agent-based workflows, governance controls, and operational tooling designed to support day-to-day business execution.
Yes. Alongside the standard toolbox (RAG, Text2SQL, Image Generation, AI Chat, Research Agent/web investigation, and governance controls), we can build custom tools, automations, and workflow-specific modules for your teams. This lets you address company-specific processes without breaking platform consistency or control.
Timing depends on scope and integration complexity, but we usually define a first production milestone quickly and deliver in iterative phases. The objective is to start generating measurable business outcomes early, then expand with evidence.
Adoption is handled as an operating model, not a one-off launch. We align each rollout with concrete roles, real workflows, enablement patterns, and usage visibility so teams know when and how to use AI in routine work.
After go-live, we continue with a structured cadence: usage and quality monitoring, KPI and cost review, workflow refinements, and prioritization of next use cases. This keeps performance improving while governance remains stable.
Yes. The approach is provider-flexible. You can operate with your preferred model and infrastructure contracts and adjust routing over time as costs, quality requirements, or policy constraints evolve.
Contact
Share your situation and we will prepare a concrete implementation and rollout proposal.
