Value engineering.
Value engineering is an agentic framework that helps agents navigate your business context, best practices and available data. It connects who you are and what your team needs to a practical plan for what to build—and a way to assess what you have and identify the next area of opportunity.
Watch the exampleCapability is becoming abundant,
therefore direction matters more.
For years, being data-driven was a resource problem. Data was expensive to connect, difficult to work with and dependent on teams that never had enough time.
Agents have changed that. More analysis, more context and more capability are becoming possible at lower cost. Work that once competed for scarce resources can now reach more of the business.
But capability needs direction. An agent needs to understand the company, the person it is helping and the decision at hand. It needs clear definitions, relevant best practices and a way to check whether the available data is enough.
Our value engineering playbook provides that guidance. It connects industry knowledge and team priorities to concrete business questions, recommended analyses and the work needed to support them. It also gives agents a framework to assess your current state against best practices and identify the next area of opportunity.
Give the agent context.
Give the work direction.
Example: consumer brand · Helping a marketing lead
read company context + human contextOkay, I’m working with a consumer brand and helping its marketing lead decide where to spend next. I’ll use the company’s context and bring finance in to validate costs and constraints.
Company & human context
Field services · operations lead
Software · finance lead
Reference the playbook
Assortment planning
Retention & lifecycle
Plan
Rebuild existing revenue reports
Recommend spend before checking costs
Building
Next opportunity
What business am I working with—and who am I helping?
A consumer brand selling online. A marketing lead deciding where to spend next, with finance validating costs and constraints. Read both contexts before choosing the guidance.
What does a good path through this work look like?
The playbook guides the agent from contribution economics to customer cohorts, then to a budget recommendation. Each step names the evidence, definitions and checks it needs.
What do we have—and what should we build next?
Orders and spend are available. Connect returns, costs and stock; agree definitions with finance; build the contribution model and cohort dashboard. Then help marketing review a budget recommendation.
Build the agreed plan, with checks along the way.
After the team agrees the scope, the agent connects the missing inputs, builds the contribution model and assembles the cohort dashboard and budget analysis. Validate each output before putting it to work.
Where is the next opportunity to improve?
The agent uses the playbook to identify the next analysis: maximum allowable acquisition cost, based on contribution economics and the agreed payback target. Compare actual acquisition costs by campaign to find what is exceeding the limit and what has room to run. Review where to pull back or test more spend with marketing and finance.