Give your AI the expertise to do the work.

Fully Powered Capabilities turn business expertise into reusable AI workflows. The instructions, context, tools and quality checks travel together, so your team can share a way of working and get back work that's ready to review.

We 10X the ROI of your existing AI spend by equipping it for the work your business actually needs to do.

Book a 30-minute fit call.See what's inside a capability

Built for the AI you already use.

  • ChatGPT
  • Claude
  • Copilot
  • Gemini

From “I prompted it” to “It's ready to review.”

Your team gives the AI the job. The capability brings the method: what to look for, which rules to apply, what to produce and where a person needs to decide.

Illustrative capability · Monthly billing review · Invented inputs and output

“Review this month's billing before invoices go out.”

The agreed rate for matter M-204 is $300/hour. Entry B-041 bills 2.0 hours at $325/hour. Entry B-067 says only “Case work,” but the firm's billing policy requires a specific description of the activity.

  1. Check entries against the agreed rates and requirements.
  2. Identify missing detail, inconsistent coding and possible duplicates.
  3. Prepare exceptions with references to the source entries.
  4. Check the packet for completeness and route decisions to the reviewer.

Billing review packet

Rate exception · B-041
Matter M-204: $650 billed; $600 at the agreed rate. A $50 difference. Confirm the applicable rate before correcting the entry.
Question for the timekeeper · B-067
What activity does “Case work” refer to? Provide a specific description to satisfy the firm's billing policy.
Responsible reviewer
Billing manager. Confirm the rate, resolve the missing description and approve the corrections.

The manager approves changes and releases invoices. AI prepares. People decide.

A complete method, packaged for your AI.

A capable system needs to know more than the next instruction. We assemble the working parts around a defined business outcome. Each capability includes the components the job requires.

Activation and scope

When to use the capability, what it can handle and which requests belong elsewhere. Its description helps the AI select the right method when someone asks for work.

How the right method gets selected

We write discovery metadata around recognizable requests and business intent. Testing includes direct requests, different wording and nearby tasks that should use another method. Selection happens within an AI session; a schedule or business event needs a separate arrangement to start that session.

Procedures and decision logic

The sequence of work, the dependencies between steps and the exceptions that change what happens next. We distinguish rules that must hold from decisions that need judgment or an answer from your team.

How we make expertise executable

Instructions specify required inputs, branching conditions, completion criteria and recovery paths. A missing engagement term in a billing review becomes a question for the owner. It never becomes an invented rate. Delicate operations get explicit constraints; interpretive work gets criteria and room for judgment.

Business context and references

Your terminology, policies, examples and company context, organized so the AI can find the information relevant to the job. Related capabilities can draw on the same approved understanding of the business.

Why context is loaded in layers

The open format supports progressive disclosure: small discovery information first, the selected instructions next, then supporting material as needed. We separate the core method from detailed references so every task doesn't begin with the entire library in its context window.

Read the standard's context-loading guidance.

Templates and output contracts

A defined shape for the result: a review packet, a research brief, a spreadsheet or a handoff another person can use. Required fields, source references and unresolved questions become part of the deliverable.

How outputs connect to the next job

Templates establish a consistent structure. Schemas define fields and types where structured data matters. Examples show the expected level of detail. When capabilities work in sequence, the reviewed output from one supplies agreed inputs to the next, with dependencies made explicit.

Tools and executable checks

Code handles repeatable transformations and objective checks where it improves the work. Approved connections can retrieve records or use business systems. The capability coordinates the steps and handles the results.

Where scripts and MCP fit

A script can check required columns, reconcile totals or verify that an output file contains the expected fields. Tool connections, including those using Model Context Protocol (MCP), provide permitted access to external systems. We define tool inputs, expected responses, failure handling and approval points for actions that change records.

The AI workspace controls execution and access. We configure the capability around those permissions and keep credentials out of its instructions. How instructions and connections work together.

Evaluations and operating guidance

Test cases, review criteria and an explanation of how to run the capability. The handoff makes the expected result, known limits and update responsibilities visible to the people who will use it.

What makes the result reviewable

We check observable output properties, inspect the meaning of the work and test the awkward cases. A well-formed package passes a format check; a useful capability also has to perform against the business's agreed standard. A reviewed version and recorded evaluation results give the next revision something concrete to improve on.

Explore evals, validation and verification.

The right knowledge, at the right point in the run.

  1. Discover

    The AI sees which capabilities are available and what each is for.

  2. Load

    It brings in the selected procedure and the resources relevant to this job.

  3. Prepare and check

    It follows the method, uses permitted tools and prepares an output for review.

The package supplies the method. Your AI workspace supplies the model, execution environment and access to tools.

A deep library. A specific fit.

We've built a research database of 8,000+ distinct workflow examples and organized reusable methods into a working library. That gives us a substantial head start on finding, assembling and adapting the right approach for your business.

Capture the expertise you already have.

Your SOPs, your best people's know-how and the informal processes that keep work moving. Fully Powered Studio is our technology for turning that expertise into instructions, templates and checks your team can share and run.

Your people establish what good looks like. We capture the distinctions that make the work yours.

Build the capability you're missing.

A new marketing function. Better management reporting. A more disciplined recruiting process. We draw on the library to select best-in-class methods, assemble the relevant workflows and adapt them to your company's context.

Source quality, suitability and reuse terms inform what we choose. Private client procedures stay specific to the company they came from.

The research gives us range. The engineering makes it fit. Your team gets a capability built for its work, with the complexity handled by us.

Explore how we build what's missing

From business need to working capability.

We do the engineering alongside your team. Your people help define the work and judge the result; we turn that understanding into a capability they can use.

  1. Define the job and the standard.

    Start with an outcome that matters: an intake decision prepared, a monthly report assembled, a billing review ready. Agree the inputs, useful output, reviewer and criteria for success before building.

    The result: a clear definition of the work and what a good answer must contain.

  2. Establish the method.

    For existing work, Studio captures the procedure and surfaces the gaps, exceptions and judgment calls. For a new capability, we select relevant methods from our library and shape them around the business. Your team resolves the decisions only it can make.

    The result: an agreed approach, grounded in your company's context.

  3. Engineer the working parts.

    Build the instructions, organize references, prepare output templates and add scripts or tool connections where needed. Make dependencies, missing-information behavior and review points explicit. Start with the demanding part of the job and establish a working path through it.

    The result: a versioned package with the components the task requires.

  4. Test the behavior and the work.

    Check whether the capability activates for the right requests and produces useful results. Use representative and imperfect inputs, compare against an agreed baseline and inspect failures. Separate building from grading so the instructions have to stand up to an independent check.

    The result: evaluation evidence, corrections and clearly recorded limits.

  5. Run it with your team.

    Configure the capability for the target AI product, available tools and workspace permissions. Your team runs real work in its own account while we observe. The person who knows the job reviews the result against the agreed standard.

    The result: a working handoff, with guidance on inputs, use and review.

  6. Improve it from use.

    A correction becomes a more precise instruction, a better reference or another test case. Keep reviewed versions and rerun relevant checks when the method, model or connected tools change. Expand into adjacent work as priorities develop.

    The result: a capability your team can maintain and build on, with our help when needed.

Test the capability against the job.

Quality has several layers. We choose checks that match the consequences of the work and agree what acceptance means with the people responsible for it.

Selection
Does it recognize the right requests and leave unrelated work to the right method?
Correctness
Are required details present, calculations checked and source references traceable?
Exceptions
What happens with incomplete inputs, conflicting information or unavailable tools?
Usefulness
Can the reviewer act on the result, and does it improve on the agreed baseline?

See how we evaluate the work

Built on an open standard. Prepared for your business.

Fully Powered Capabilities use Agent Skills, an open standard supported across leading AI platforms. It gives reusable expertise a common structure. We supply the business method, implementation and testing that make it useful to your team.

We work in the AI your team already uses, including ChatGPT, Claude, Copilot and Gemini. We configure and test the capability for the specific product, account and tools available to you.

A practical starting point is files and exports in your own workspace. Live system connections, scheduled runs and broader orchestration can be added when the work calls for them, with their scope agreed separately.

The technical format behind a capability

The standard defines a package containing discovery metadata and a core instruction file, with optional supporting scripts, references and assets. The instruction file is named SKILL.md. That common structure lets compatible agents discover and load the method. Read the Agent Skills specification.

Our work is the engineering inside and around that structure: selecting the right method, capturing business logic, organizing context, designing outputs, testing behavior and making the whole package work in your environment. We maintain an identifiable version, its dependencies and the guidance your team needs to operate it.

How support differs across AI products

Native support is documented in ChatGPT and Codex, Claude, GitHub Copilot and Gemini CLI. Distribution and available tools differ by product.

Microsoft 365 Copilot declarative-agent skills are currently in Frontier preview. Gemini Apps also documents native skill imports. We verify account availability, supported files and tool access in your workspace when scoping the build.

Portability comes from reusable instructions and resources. We check activation, scripts, dependencies, connectors and permissions in the destination environment. Support for a shared format does not make execution identical across products.

Further reading: the architecture and engineering

Our approach draws on the open specification, current platform documentation and our own capture and evaluation system. These public references explain the underlying technology:

What should your AI be able to do for your business?

Bring a recurring job, a process your best person knows by heart or a capability you've been meaning to establish. We'll help you see what it could become.

Book a 30-minute fit call.Explore workflows for your business