Clearvalue AI

What we do

One focused engagement, from first conversation to working tools.

Most of our work begins with a pilot, where we learn how your business works and find where AI can help. We then work with you to blueprint, iterate, and deploy solutions with measured impact.

What you get

What you walk away with

Readiness assessment

A comprehensive picture of where each part of your organization stands with AI today, and what's missing.

Roadmap

A ranked list of opportunities that shows which ones are quick wins and which are bigger projects.

Working AI tools

Select tools we build and hand to a controlled group of your teams, with results measured against how things worked before.

Security and governance guidelines

Clear rules for handling data, controlling access and staying compliant, written around how your team works.

The process

Assess, then build

What we learn in the first phase decides what we build in the second. We check in with your team every week, so nothing comes as a surprise.

Phase I

Assess

First, we learn how your business works and where AI fits.

  1. 1

    Kickoff

    We agree on goals, which teams are involved, who our contacts are and how data will be shared.

    • A note from leadership so people know why we're asking questions
    • A main contact and a technical lead for each team
  2. 2

    Discovery

    We have short conversations with team leaders and the people doing the day-to-day work, and watch a few workflows in action.

    • What's frustrating, what's repetitive, which tools you use and how people already use AI
    • We read process docs, sample work and your list of systems
    • Each week we add to a running list of opportunities
  3. 3

    Scoring the opportunities

    We rate each idea on what it's worth, how hard it is to build and how risky it is. At the same time, we start drafting rules for data and security.

    • Anything too risky gets reworked or dropped
    • Guidelines for data handling, access and compliance
  4. 4

    Picking what to build

    We show you where each team stands and rank the opportunities. Then we decide together which ones to build.

    • Quick wins and longer-term projects, side by side
    • Two or three ideas chosen for the build phase

Phase II

Build

Then we build the best ideas and test them with your people.

  1. 5

    Prototype

    We map out how the task is done today and time it, so we have something to compare against. Then we build the tool and refine it every week with the people who'll use it.

    • A short brief that spells out what success looks like
    • A working tool that handles the core task, with a person checking the output
  2. 6

    Small rollout

    A handful of people on your team use the tool on real work while we track how it goes.

    • We compare time per task, rework and how people feel about it against the old way
    • We keep refining until the output is good enough to trust
  3. 7

    Results and next steps

    We walk you through what we found, demo the tools and agree on what's worth scaling.

    • Final assessment, guidelines and roadmap
    • Anything beyond the pilot is scoped and priced separately

How we prioritize

How we decide what's worth building

We look at what each idea is worth, how hard it would be and how risky it is. Risk comes first: if something isn't safe, we rework it or set it aside before ranking anything.

Value

What we look at

Time saved, revenue, fewer errors, better compliance, happier clients

For example

How often the task happens, how long it takes, how many people do it, how often it goes wrong

Feasibility

What we look at

Whether the data exists and is usable, whether we can get into the systems, and whether your team can run it after we leave

For example

Data sources, sample work, systems to connect, number of users, training needed

Risk

Must pass

What we look at

Exposure of sensitive data, legal and contract limits, how accurate it needs to be, and where a person has to review the output

For example

Client contracts, regulations, audit requirements, the cost of getting it wrong

Everything that clears the risk bar is then ranked on value and how hard it is to build.

Where you stand

How ready is your team for AI?

We place each team or business unit on a five-step scale, so you can see where you are today and what it would take to move up.

  1. 01

    Ad Hoc

    A few people use AI on their own. There's no development setup, and data is hard to get to.

  2. 02

    Experimenting

    Some basic guidelines exist. Development is informal, and data is scattered across systems.

  3. 03

    Enabled

    There's an approved AI platform, data is centralized and easy to reach, and development has a proper home.

  4. 04

    Scaled

    AI tools are part of everyday IT workflows, there's a written AI policy, and data lives in a platform built for AI.

  5. 05

    AI-native

    AI is part of how core work gets done across the business. Results are measured, and the tools keep getting better.

Flexible buildout

How closely we plug into your systems

Some tools are best built on the side. Others need to live inside your systems. We'll recommend the right fit for each one, and it's often a mix.

Level 1

Sandbox

Separate from your systems

A standalone prototype or mockup that shows how the tool would work.

  • Quickest to build
  • No IT setup on your end
  • Gets you to a demo fastest

Level 2

Replica

A copy of your setup

Code tested on a copy of your tech setup, with instructions for installing it.

  • Shows it'll work with your systems
  • Includes a check of your IT readiness
  • We never need access to your systems

Level 3

Full integration

Inside your systems

Built directly in your environment, with temporary access for our developers.

  • Proven in your real environment
  • Nothing to rebuild or reinstall later
  • The fullest picture of what it takes

Results

Case studies

A few examples from past work. We've left out client names to protect their privacy.

PE-backed portfolio technology company

Automated monthly reporting

70%less time spent drafting monthly reports

The problem

  • The finance team spent a big chunk of every month on reporting
  • The write-up mostly repeated the numbers instead of explaining them

What we built

  • An AI agent that reads last month's materials and the new data
  • Writes a first draft of the report
  • Points out changes that need explaining

What changed

  • Drafting time dropped from 30–35 hours a month across the team to 8–12
  • It caught things the old reports had missed, like customer churn trends and vendor price increases

Healthcare equipment and logistics company

Replacing vendor tools with an in-house one

80%less spent on outsourced contractors

The problem

  • The team paid for a pile of outside tools, each with its own license and support fees
  • Budgets were tight and something had to give

What we built

  • Tested the vendor tools on real day-to-day work
  • Found that an in-house AI workflow would do the job better
  • Built it with the team's input and retired the vendor tool

What changed

  • The team owns the workflow now
  • No licenses to renew and no vendor to depend on

Emerging manager private equity firm

AI in the diligence playbook

$400K+saved per year, without hiring another junior analyst

The problem

  • Every deal brought a stack of sell-side materials: CIMs, financial diligence reports, data files
  • The deal team was already stretched across other work

What we built

  • Added an AI review step to the firm's investment playbook
  • The agent checks materials against a set of rules and flags anything unusual
  • The deal team reviews only what gets flagged

What changed

  • The team handles more deals without adding headcount
  • Partners start from a clear summary instead of a pile of documents

Upper middle market private equity firm

Judging AI's impact on every deal

5AI questions asked of every deal in the pipeline

The problem

  • There was no consistent way to judge whether AI would hurt or help a target company
  • AI came up in diligence and IC memos, but only as anecdotes

What we built

  • A framework built around the criteria the investment committee already uses
  • Every deal is checked on five things: business model, product roadmap, what makes it hard to copy, its place in the value chain, and risk from customers and suppliers

What changed

  • AI's impact now shows up in the financial model instead of being argued about separately
  • Each deal is modeled for both the upside and the downside of AI

Where AI fits

Where AI tends to help

Some common examples to get you thinking. Every business is different, which is why we look closely before we build anything.

Sales & marketing

  • Meeting prep pulled from your CRM
  • Lead scoring and suggested next steps
  • Campaign analytics
  • Competitor insights from sales calls

More deals closed, less time on outreach

Customer service & operations

  • Sorting and routing tickets
  • Call transcripts and summaries
  • Spotting customers at risk of leaving

Handle more requests, faster

Finance

  • Invoice and expense processing
  • Catching overdue payments
  • Forecasting and variance analysis

Much less manual drafting

HR & talent

  • Screening candidates
  • Collecting interview feedback
  • Onboarding and training
  • Answering benefits and policy questions

Applications handled faster

IT & engineering

  • Coding assistants
  • Moving off legacy code
  • Summarizing and sorting incidents

More time for developers to build; small issues fixed automatically

Legal & compliance

  • Contract review and first drafts
  • Tracking regulatory changes
  • Finding past clauses and precedents

A clearer view of compliance across the business

Procurement

  • Spend analysis
  • Vendor onboarding checks
  • Replacing or consolidating vendors

Less wasted spend and lower costs

Executive office

  • Performance reports from one consistent source
  • Board pack preparation
  • Meeting summaries and action items

Less admin, more time for decisions

Frontline & field operations

  • Assistants that help diagnose and fix equipment
  • Better scheduling and dispatch

Faster fixes and more productive technicians

Curious where AI could help your team?

Let's talk. Tell us what you're working on, and we can discuss what makes sense for you and your teams.

Get in touch