Two operating models

Keep the workspace with each employee—or make the runtime a shared company capability

Shared databases, files, and model servers are optional in the first model. The second centralizes CanDoItAll itself and lets existing company applications become the working surface.

01

Employee workspaces

Each person runs a focused local instance. Company-hosted models, databases, or storage can be added independently when policy, scale, or operations justify sharing them.

Independent employee CanDoItAll workspaces connected to optional shared company model, database, and file services — Local working surfaces remain independent; company infrastructure is shared only where it adds control or operational value.

02

Shared agent platform

CanDoItAll runs inside the controlled network with prepared agents and shared services. Employees reach those capabilities through the company portals and information systems they already use.

Central protected CanDoItAll agent runtime connected to private infrastructure and employee-facing company applications — One governed runtime can serve several applications without forcing every employee into another standalone interface.

Three adoption views

Start with the people, then share what should be shared

Enterprise adoption does not need to begin as one large platform program. A controlled individual workspace can prove value before infrastructure is centralized.

01
Employee workspace

Specialists and managers can organize activities, projects, meetings, files, and follow-up work in a local CanDoItAll instance. Scoped agents can help prepare, research, plan, review, and execute while the employee remains responsible for the result.
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02
Shared company infrastructure

Local instances can use company-hosted models, managed databases, network or cloud storage, and approved plugins. Central operations can own backups, endpoint availability, credentials, and policy while employees keep a focused working surface.
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03
Embedded agent platform

Run agents, workflows, and APIs inside the private network, then integrate governed “chats and workflows” into an existing company application or a new internal UI. This is often the strongest route when company systems already own the user experience.
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Deployment topology

Centralize services without forcing every workspace into one shape

Choose each boundary independently. A private model server does not require the database, file storage, and user interface to live on the same machine.

AI providers: Run local models on each workstation, provide one central private model server, or allow approved cloud providers. Employees can connect their local instance to the endpoint selected by company policy.
Files and knowledge: Use local storage, private company-network storage reached directly or through VPN, or an approved cloud store. Keep storage permissions and backup responsibility explicit.
Operational database: Keep PostgreSQL local for an individual workspace or host it on a managed network server with correct backup, monitoring, and recovery procedures.
Sharing today: Export and import data for deliberate hand-offs. Multiple instances can point to the same database, but users must coordinate writes to avoid conflicts. The current app does not yet provide complete conflict-safe, real-time collaboration across local clients.
Shared server-side use: A server-side CanDoItAll instance is the better basis when a group needs one shared working surface. Full multi-user conflict coordination requires an additional service that is planned for later in 2026.

Shared agent runtime

Prepare company-specific agents once

A controlled server-side deployment can provide tuned agents, reusable workflows, and approved tools to a department or operating group.

Teams can prepare named specialists around company roles, models, skills, capabilities, tools, and access boundaries. The built-in HR agent can inspect that catalogue, recommend an existing specialist, or help shape a new one while its request and response remain visible beside the available team.

Explore the agents module

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The HR agent recommends a suitable specialist without hiding the conversation or the catalogue it used.

Cost and performance visibility

Turn agent activity into managed operating evidence

CanDoItAll stores detailed usage and expense observations for agent conversations and for agent work performed through workflows and processes.

CanDoItAll agents dashboard with total expense, token use, sessions, provider distribution, most-used agents, and failed runs — Agent overview **See total agent usage and spend at a glance** Total expense, token use, sessions, provider ratio, most-used agents, and failed runs reveal the operating mix behind AI work.

CanDoItAll project manager summary with historical and planned costs, cost ratio, recent activity, and expense trend — Project costs **Connect actual spend to the remaining project plan** Known and estimated history sit beside planned cost, category ratios, recent activity, and trends for the selected project scope.

CanDoItAll project Gantt with task, dependency, effort, assignment, date, and expected-cost information — Plan exposure **Put expected cost beside schedule and effort** The Gantt view keeps task dependencies, delivery and pure effort, assignments, dates, and expected cost in one planning scope.

CanDoItAll manager summary activity dialog with filters, aggregate costs, durations, and a detailed list of agent runs — Activity costs **Inspect the individual runs behind a total** Filter agents, workflows, or processes by outcome to review activity time, known cost, recorded estimates, and status.

CanDoItAll process history graphs with cost, tokens, runtime activity, manager signals, and tool usage — Process trends **Search process cost and telemetry over time** History filters reveal cost, token, runtime, manager-signal, and tool-usage patterns across process runs.

CanDoItAll process manager chat explaining the cost and token breakdown of a selected process run — Ask a run **Talk to the process manager about one run** A run-scoped manager can explain actual cost, token breakdown, outcome, and execution context without leaving process history.

Where model pricing is configured, each execution can retain its provider, model, token usage, calculated cost, timing, outcome, and project or run context. Aggregated views make the same evidence useful for financial control and management decisions.

Explore agents analytics

Trace the expense

Follow cost from an individual conversation or execution into its workflow, process, agent, provider, and model context.

Compare the operating mix

Aggregate usage and expense by agent, provider, model, time window, run status, or project context to see where capacity and money are going.

Build decision KPIs

Turn the records into measures such as cost per accepted outcome, provider mix, agent throughput, failure rate, retry cost, or automation cost avoided.

Planned · target later in 2026

Deeper project economy

The backend already contains many economy-related functions, but not all of them are exposed in the UI yet. Ledger and Economy drivers are being prepared to bring deeper project budgeting, economic planning, simulation, and execution into the same governed operating model. Roadmap dates may move as implementation evidence improves.

APIs, integration, and identity

Give every integration a machine-readable contract and a clear place in the enterprise namespace

CanDoItAll exposes a substantial HTTP API surface with OpenAPI-standard descriptions. Integration teams can inspect contracts, validate requests, and generate clients with common tooling instead of reverse-engineering endpoints.

OpenAPI contract

Integrate from a standard description

Use the machine-readable API description for discovery, generated clients, request validation, testing, and documentation. SharedInfo keeps the cross-repository API guidance and responsibility boundaries visible.

Unified namespace

Fit the runtime into an Industry 4.0 data spine

An orchestration or unified-namespace layer can mediate naming, events, transformation, policy, and reliability between CanDoItAll, business systems, and operational technology.

Direct and plugin edges

Connect narrowly where that is enough

Direct API connections and typed plugin executors remain valid for focused integrations with SAP, Microsoft Dynamics, Odoo, HELIOS, proprietary applications, and many other systems.

Planned · target later in 2026

Identity and industrial connectivity

A proxy service is planned to connect server-side runs to identity management and finer access control. The intended integrations include Microsoft Entra ID, Keycloak, similar identity systems, and a default built-in identity option.

MQTT and OPC UA support is also planned for later in 2026. These protocol integrations will provide a smoother path into Industry 4.0 unified namespaces, industrial event streams, and equipment-level data without requiring custom adapters at every edge. Roadmap dates may move as the public alpha produces better evidence.

Enterprise engineering foundation

A mature stack for systems that must last

C# and .NET combine mature enterprise tooling, strong runtime performance, and cross-platform deployment with one language across a broad application stack.

Inspect the framework

Cross-platform and full stack

.NET runs across major operating systems. Blazor and .NET MAUI support web, desktop, and mobile application layers; nanoFramework extends the C# ecosystem into constrained embedded devices.

Lower lifecycle cost

Shared language, types, libraries, and engineering practices across layers can reduce duplicated implementation and make long-term maintenance easier than a fragmented stack.

Strong feedback for AI-assisted development

Runtime type information, reflection, Roslyn analyzers, compiler diagnostics, and explicit contracts give coding agents useful feedback. C# can express enterprise structure without the ceremony common in some comparable stacks, helping conserve context and tokens.

A base, not only a tool

Organizations can operate CanDoItAll as provided, adapt it into an internal system, or use its foundations to build a product for their own customers.

From adoption to ownership

Use the platform—or make it the foundation of what you build next

The source is free to evaluate and use commercially. Focused paid training, architecture work, or implementation support can help your team choose a defensible boundary and reach the first useful outcome faster.

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Business cooperation

Would you like to explore business cooperation?

Bring a bounded enterprise problem, an integration boundary, or a delivery standard your teams already follow. We can discuss architecture, implementation, training, and operational support around a concrete outcome.

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Adopt governed AI at the boundary you control