01
Starting from the tool
The session opens in whichever assistant is nearest, before anyone has said what the work actually is.
Practical AI support for your work
What are you working on, and how can AI help?
Pilaro identifies your tasks, creates the right AI skills, and coaches you inside the tools you already use.
This starts with the work in front of you today, not with a tool, a model, or a programme. You describe what you do, Pilaro proposes how each task could be approached, and you decide what to accept.
Task by task, inside ChatGPT, Claude, Codex, Slack, and the other environments your organization approves.
Where most people are today
AI arrived in your work faster than any guidance for using it.
Tools appear, models change, and colleagues pass around prompts that worked for them. The practical question stays unanswered: for the work in front of you today, what should AI do, what stays yours, and how do you know the result is good enough to send?
Most people end up opening a tool first and only then working out what they were trying to produce.
Too many tools and modelsUncertainty about appropriate AI useWeak prompts and thin contextInconsistent outputsResults that are hard to validateEvery task starting from zero
What makes AI harder than it should be
The difficulty is rarely the model. It is how the work is framed.
Six patterns show up in almost every role. Each one is fixable, and each one is about the task rather than the technology.
02
Activities never split into tasks
One request hides three pieces of work, each needing a different approach and a different check.
03
Unclear expected output
Without a defined result, the answer reads well and still cannot be used for anything.
04
Missing information and context
The model gets the question but not the account history, the policy, or the document that decides the answer.
05
Weak quality checking
Output is judged by feel rather than against criteria, so mistakes surface later, in front of someone else.
06
Learning that is not retained
The prompt that finally worked lives in one chat history and is gone by next week.
The reframe
You do not need to master every AI tool. You need the right support for the task in front of you.
Support means a clear task, the context it depends on, a sensible model and prompt, and a check on the result, prepared for the specific work you are doing now.
What Pilaro does with you
From a vague request to work you can stand behind.
Pilaro works through the task in a set order. You remain the person who decides, edits, and approves at every point.
Identify the task
Separate what you were asked for into distinct pieces of work, each with its own outcome.
Clarify the outcome
Agree what a good result looks like before anything gets generated.
Determine the execution mode
Decide whether this task is manual, assisted, orchestrated, or delegated.
Create or select a skill
Reuse a prepared skill when one fits, or build one that can be used again.
Choose model, prompt, context, and tools
Match the setup to the task instead of defaulting to whatever happens to be open.
Coach the execution
Guidance while the work is happening, in the environment you are already in.
Validate the result
Check the output against the expected result and the criteria set up front.
Remember what worked
Keep the version that succeeded so the next run does not begin from nothing.
Develop your Skill Shift
See how your own way of working is changing and what is worth learning next.
Example
What Pilaro prepares once it knows where you work
An illustrative example for a fictional company, shown so the shape is clear before you enter anything. Add your organization's website and this card fills with real public context, every field labeled as fact, inference, or proposal.
Example - not a real customer
Organization
Example Consumer Products Group
Example - illustrative only
Roles in the value network
Brand owner and product developer with outsourced manufacturing
Example - illustrative only
Products and services
Consumer goods developed in-house, produced with contract manufacturers, and sold through retail and online channels
Example - illustrative only
Terminology in use
SKU, tech pack, bill of materials, gate review, launch window
Example - illustrative only
Likely function families
Product development, sourcing, quality and compliance, supply, commercial
Example - illustrative only
Public context
Announced a program to shorten product-development lead times
Example role profile
The profile Pilaro proposes before you correct it
An illustrative profile for a fictional role at a fictional company. Your version is built from your organization, department, and role, and stays a proposal until you confirm, edit, or reject it.
Proposal - you confirm or correct
Role
Customer Operations Specialist
Confirmed by you
Department
Customer operations
Proposal - medium confidence
Responsibility
Owns day-to-day service for a group of retail client accounts, from order questions through to delivery exceptions.
Inference from organization context
Terminology
Service window, exception, wave, client account, credit note
Proposal - medium confidence
Likely outputs
Client updates, exception summaries, weekly service reports, internal escalations
Proposal - medium confidence
Likely stakeholders
Client contacts, warehouse leads, transport planners, finance
Example tasks
Five tasks, the way Pilaro would propose them
For the example role above, Pilaro proposes a short set of concrete tasks rather than a long inventory. Each one names the purpose, the expected output, and where AI could genuinely help. You confirm, edit, reject, split, add, or pick one to start with.
Proposal - high confidence - daily
Answer a delivery exception query
Purpose: give the client an accurate account of what happened and what comes next. Expected output: a client-ready reply with confirmed facts. AI can assemble the timeline and draft the reply; you decide what is committed to the client.
Proposal - high confidence - weekly
Prepare the weekly service report
Purpose: show a client account how service performed and where attention is needed. Expected output: a short report with figures and commentary. AI can structure the report and draft commentary from the underlying numbers.
Proposal - medium confidence
Summarize an escalation for the account review
Purpose: make a recurring problem understandable to people who were not involved. Expected output: a one-page summary with cause, impact, and proposed action. AI can compress the history; the judgment about cause stays yours.
Proposal - medium confidence
Check a disputed charge before it reaches finance
Purpose: establish whether a charge is correct before it becomes a credit discussion. Expected output: a short position with the supporting evidence attached. AI can locate and cross-check the relevant records.
Proposal - lower confidence
Brief the site on a new client requirement
Purpose: turn a client agreement into instructions the warehouse can act on. Expected output: a working instruction with the changed steps marked. AI can translate the agreement into operational steps for you to verify.
Execution mode
How much of the task should AI carry?
Four stable modes. A recommendation is only made once a task has enough context behind it, and it stays a recommendation: you can choose a different mode at any point and change it later.
Manual
You do the work. AI stays out of it, usually because judgment, relationships, or sensitivity dominate the task.
Recommended for the example task
Assisted
You lead and AI supports specific steps: drafting, structuring, checking, or finding the context the task depends on.
Orchestrated
AI runs a defined sequence of steps using a prepared skill. You set it up, review at the checkpoints, and approve the result.
Delegated
AI completes a bounded, well-understood task within explicit limits. You set those limits and remain accountable for the outcome.
Reusable AI skills
A skill is a task done well, written down once.
When an approach works, Pilaro captures it as a reusable skill instead of leaving it in a chat history. Every skill has the same structure, which is what makes it safe to reuse and safe to share.
Task objective
What this skill is for, and the situation in which it applies.
Instructions
The steps to follow, in the order that produced a good result.
Required context
The information the task cannot be done properly without.
Prompt
The wording that works, maintained as part of the skill rather than retyped each time.
Model
Which model suits this task, and the reason that choice was made.
Tools
The systems, documents, and functions the skill is allowed to use.
Output contract
The exact form the result must take to be usable by whoever receives it.
Quality checks
The criteria the output is tested against before anyone else sees it.
Human review
The moments where a person has to look at the work and decide.
Escalation conditions
What happens when the situation falls outside the limits of the skill.
Ownership and version
Who maintains the skill, and which version you are running.
The AI Work Coach
Seven stages, every time you bring work to it.
The coach does not replace your judgment. It makes sure the task is understood before the work starts, and checked before the work is used.
01
Understand the request
Take in what you were actually asked for, in the words you would use yourself.
02
Identify and clarify the task
Separate the request into distinct tasks and name the outcome each one has to produce.
03
Gather the right context
Collect the information, documents, and history the task depends on.
04
Recommend the execution approach
Propose the mode, the skill, the model, and the tools that suit this particular task.
05
Guide the work
Stay alongside while the work is done, in the environment you are already using.
06
Check the output
Test the result against the expected output and the quality criteria agreed up front.
07
Capture learning
Keep what worked so the next run, by you or a colleague, starts further ahead.
Where this happens
In the tools you already use, not in another place to check.
Pilaro does not ask you to move your work. It remains the coaching, skill, context, governance, and learning layer behind whichever approved environment you are working in.
ChatGPT
Bring a task into a familiar assistant with the right skill, context, and quality checks attached to it.
Claude
Work through longer documents and analysis with the same task definition and the same output contract.
Codex
Technical work stays in the environment built for it, governed by the same skill structure.
Slack
Pick up a task, get coached, and confirm a result in the channel where the work is already being discussed.
Other approved work environments
Your organization decides which environments are approved. Pilaro adapts to that list rather than adding to it.
Your own development
You can see your own way of working change.
Skill Shift is the movement of your tasks across four modes over time. It is a picture of capability, not a performance score, and it belongs to you.
What changes for you
Practical outcomes, on the work you are doing this week.
Help today
Something useful on the task in front of you, not after a programme finishes.
Less tool confusion
One decision about which model and setup to use, made with you and explained.
Better prompts and context
The information the task needs, gathered before the work starts.
More reliable outputs
Results checked against a defined expectation instead of a general impression.
Less repetitive work
The parts you have done many times stop consuming the same effort each time.
Reusable skills
What worked once becomes something you and your colleagues can run again.
More confidence
You can explain why the work was done this way and how the result was checked.
Clear personal development
A visible path for your capability, described in the work you actually do.
Privacy and trust
Your work stays yours.
This page is used by people inside organizations, so the boundaries have to be explicit. They are structural, not a preference someone can quietly change.
- Personal by default: what you capture here is yours unless you decide otherwise
- Transparent capture: you can always see what has been recorded about your work
- Explicit sharing: nothing reaches a colleague or a team without your decision
- No automatic manager surveillance: your tasks and progress are never reported upward on their own
- Proposal validation: role profiles and tasks remain proposals until you confirm or correct them
- No promotion into organizational truth without permission: your work becomes shared context only when you allow it
Who do you work for?
Let Pilaro understand the organization where you work
Start with your organization's website. Pilaro reads public sources and prepares the context first, so the page becomes useful before you have said anything about yourself. It then asks which department fits your work, and after that your role. Both are optional, both can be corrected, and neither is needed to begin.
Pilaro only reads public information about your organization. You see what was found, where it came from, and what is proposed rather than known.