Service / AI consulting
Give AI a specific job. Decide who checks it.
People are trying AI tools. The company still needs to agree what those tools can read, what they can do, and who checks the result. Rever helps define and test those working rules.
Discuss this workBest fit
Who it is for
Founder-led teams that can see practical uses for AI but need to connect tools, knowledge, permissions, and day-to-day work without creating a parallel system nobody can trust or maintain.
The aim
AI work people can check, using approved sources and named owners.
Common problems
Does any of this sound familiar?
- People use AI individually, but the company has no shared rules for context, tools, sensitive data, review, or ownership.
- The answer is somewhere in the documents, messages, or someone’s head. Every request starts with finding it again.
- There are more drafts and updates, but decisions and handoffs are still stuck.
- Meetings repeat because nobody can find the current status, the decision owner, or where to raise a problem.
- Leaders want agents and automation but cannot yet state which actions are allowed, who reviews them, or when the system must stop.
Method
Build around the work people actually need to do.
Map one valuable workflow, its source knowledge, permissions, decisions, risks, current effort, and accountable owner before selecting tools.
Design the smallest useful system: approved context, a bounded agent or workflow, explicit human checkpoints, an output contract, and failure handling.
Pilot with synthetic or appropriately approved data, evaluate quality and access boundaries, measure the change against a baseline, and decide whether to scale, revise, or stop.
What the work can include
Documents and tools the team can use.
These are examples. The scope determines what we agree to deliver.
- A map of where AI might help and which result to measure
- Approved knowledge sources and who can access them
- Codex, Claude, and tool-specific operating standards
- Written agent purposes, permissions, and limits
- Human review, escalation, incident, and retirement rules
- Templates for work queues, handoffs, and written decisions
- Repeatable checks and a pilot scorecard that protects private data
- Instructions for taking over and maintaining the system
How the work moves
Start small, then adjust.
The steps below are a guide. Timing depends on the scope, who is available, and what we find.
- 01
Choose one piece of work
Choose one low-risk workflow where better access to knowledge or a bounded AI-assisted step can create a measurable improvement.
- 02
Design the controls
Define sources, permissions, provider boundaries, allowed actions, human approvals, evaluation criteria, and a safe rollback path.
- 03
Pilot in real operating conditions
Run the workflow with controlled inputs, inspect failures and rework, and compare cycle time, effort, quality, and risk with the baseline.
- 04
Transfer, scale, or stop
Record what worked and what failed. Train the people who will own it, then decide whether to expand, change, or stop.
Operating-system map
How priorities become finished work.
Priorities guide decisions. Reviews show what is moving and what is stuck. What you learn changes the next plan.
What to look for
What a useful change could look like.
- A teammate asks a question and gets an answer with current, approved sources, or a clear statement that the system cannot answer.
- A role agent produces a defined output from allowed inputs, while material decisions remain visible to and owned by a named person.
- A recurring status meeting becomes an asynchronous artifact with a response expectation, decision right, escalation path, and accountable owner.
- Codex or Claude work happens inside provider-specific access, context, review, and logging rules rather than personal improvisation.
- The team has the instructions and access to check, fix, maintain, and retire the workflow itself.
Boundaries
What this work does not replace.
- AI does not replace leadership accountability, specialist judgement, or human approval for legal, HR, financial, security, production, or other high-impact decisions.
- A company knowledge system is not an omniscient “company brain” and does not replace authoritative systems of record.
- Agents are not given broad autonomy by default. Their purpose, inputs, tools, outputs, review rules, and stop conditions stay bounded.
- Faster output is not treated as proof of better decisions or business results; each pilot needs its own baseline and evaluation.
Questions
FAQs
Is this AI strategy or implementation?
It can include both. Start with a specific problem, then agree whether the work covers choosing a use, setting rules, configuring tools, running a small pilot, or handing it over.
Do you work with Codex and Claude?
Codex and Claude are among the intended tools. Each needs separate decisions on account ownership, data use, retention, residency, permissions, review, and fallback. Approving one does not approve the other.
Can you build a company knowledge system?
The work can design and pilot permission-aware retrieval over approved sources, with citations, freshness signals, and an unanswered-query path. Access boundaries and source ownership are defined before non-synthetic company data is used.
Will AI agents replace roles or meetings?
That is not the premise. Agents support bounded work while people retain outcome ownership. A meeting changes only when a durable artifact and explicit decision, response, escalation, and ownership rules can perform its function more reliably.
What is a sensible first pilot?
Usually one read-only, measurable workflow with an accountable owner, approved sources, low-risk inputs, a repeatable evaluation set, human review, stop conditions, and a rollback path.
Start with the problem