Which customer opportunities deserve attention?
Analyze market trends and customer demand to assess sales priorities and proposal terms.
Discuss the application scope →Business-led. Knowledge-powered.
Build an Agentic Enterprise
around your business knowledge.
We help companies put AI to work using their own data, business rules, and expertise. Our team works with you from defining the problem through building and running the application.
AI in business workflowsAgent-based Business Apps
A shared business knowledge baseEnterprise Knowledge Foundation
AGENT ENGINEERING
Business understanding.
Working agents.
Agent design starts with understanding the work: the decisions it requires, the criteria and exceptions that matter, and the consequences for the business.
Our experience in business operations and consulting shapes what we build. We use it to decide which steps belong in code, which judgments need a model, and where agents need room to explore—then test those choices against real business cases.
Explore six elements of agent design. A task may call for several of them together.
Agent design · Implementation · ValidationWorkflow · Graph Engineering
Which steps must be followed?
Make required steps, branches, and approval conditions explicit in the execution structure. Each step can run calculation code, call a model for a decision, or use an agent that explores within its scope.
Execution structure
Task steps, state, conditional branches, and loops
Decision boundaries
What code determines and what the model may choose
Validation checks
Skipped steps, incorrect branches, and exception handling
Services
Help with each stage of putting AI to work
Some projects start with a strategy question. Others need better data, clearer business rules, or an application that people can use. We agree the scope around the problem and the systems you already have.
AI Strategy & Architecture
Define priority AI initiatives, platform choices, and an implementation roadmap around business objectives and operating requirements.
Explore →Enterprise Knowledge Engineering
Build agent-ready knowledge through LLM-based extraction, ontology and semantic models, explicit business rules, and validation.
Architecture & Design Example →Data Platforms & Engineering
Integrate SAP and enterprise data, with data models and semantic definitions designed for analytics and AI.
Explore →Agentic Analytics & Applications
Build agents and applications for analysis, forecasting, simulation, and comparison of business alternatives.
Explore →AgentOps & Managed Operations
Manage quality, permissions, execution history, and change, with support for adoption and ongoing improvement.
Explore →Data platform expertise: Snowflake Services →
Explore All Services →Applications
From market opportunities to planning and performance
We develop analytical features and business interfaces using the client’s metrics and rules. Definitions used across applications are maintained consistently.
What can we promise for this order?
Compare supply options across demand, inventory, production, and procurement against delivery and cost constraints.
SCM Applications →Where is business performance changing?
Investigate performance drivers and compare responses across products, customers, and channels.
Profitability & Portfolio Analytics →These are potential application areas, with scope defined for each engagement. Each page describes the relevant features and delivery scope.
AI-Native Consulting & Delivery
Define the decision criteria.
Test them against real cases.
We define the business problem and define criteria for the client using business evidence, practitioner experience, consulting methods, and industry best practices. We implement those criteria in calculations, analyses, and workflows, then validate the results against real cases.
Business Reasoning Design
Define decision criteria using business knowledge and consulting methods, then design the investigation, calculations, and scope of automation.
Explore Business Reasoning Design →Test as We Build
Agree what a good result looks like, test against real cases, and use the findings to improve the next version.
Our development process →People & Experience
People who know the work
and help build the system.
Our core team brings experience in corporate operations and global consulting across strategy, process, data, and systems transformation. Consultants take a hands-on role in design and implementation, working with specialist data, AI, and IT engineers.
Our experience in business analysis and systems implementation informs the work from problem definition through development.
Meet Nex & Bridge →Insights
Selected writing
All posts →If the data is standardized and stored in a warehouse, is it ready for AI?
Can we accept this order? Inventory balances alone won't tell you. Committed stock, production capacity and substitution rules all matter, and the gaps often surface only when you test a real business question.
ArticleRead →
Are We Building AI—or Just Another Enterprise System?
Add enough prompts, rules and workflow branches, and an AI project can start to look like the system it was meant to improve. The useful question is which tasks belong with software, agents or people.
ArticleRead →
The Product with the Highest Margin Is Not Always the Most Profitable
One product contributes $30 but takes three hours to make. Another contributes $20 in one hour. When production time is scarce, the higher unit margin can point you toward the wrong product.
ArticleRead →
Contact
Tell us what needs to change
Tell us what's not working and what you want to change. We'll look at the process, data, and systems with you and agree where to start.
Contact Us →