Define the problem and objectives
Analyze the decision process and its constraints to define the problem. Agree on improvement objectives and evaluation criteria with the client.
Consulting & AI Delivery
Frame the right problem.
Test it against the business.
We do not begin by translating requirements into a build plan. Operating and consulting experience informs a working hypothesis; the client's business rules and data determine whether it holds and what should be built.
AI Engineering · Delivery Perspective
From problem framing to AI design and implementation
We draw on operating and consulting experience to define the problem to solve. For each business task, we specify the knowledge and analytical methods required, determine what AI should do, and validate the implementation against real business requirements.
Carry the problem through to implementation
A requirements list rarely captures every exception or the reasoning behind a business decision. At Nex & Bridge, consultants who frame the problem also implement and verify the solution, alongside specialist engineers.
Make business rules explicit
Implement criteria developed from business evidence, practitioner experience, and consulting methods in formulas and processing rules. Implement repeatable calculations in SQL or manage them in a semantic model. Consider ontologies and knowledge graphs for relationship queries, and enforce data constraints and execution permissions in code.
Test whether a change is an improvement
Getting a better answer on a second attempt is not the same as improving the system. The cause may lie in knowledge, retrieval, or calculation. Before-and-after evaluation is part of the development process, so each change can be tested for its effect.
Knowledge design and implementation · Agent evaluation and improvement
Business Reasoning Design
Business Reasoning Design
We define decision criteria using business evidence, practitioner experience, consulting methods, and industry best practices. We validate those criteria against the client’s objectives and constraints, then design the investigation, calculations, automation, and approval requirements.
Derive and define decision criteria
Analyze business evidence and practitioner expertise alongside consulting methods and industry best practices. Define decision criteria and exceptions for the client’s objectives and constraints, then validate them against actual cases and data.
Design AI for each type of decision
Select models and analytical methods for classification, forecasting, causal investigation, and option evaluation. Design model routing, reasoning, and context around accuracy and latency requirements, and use quality evaluations to optimize token usage and processing cost.
Validate and improve using business cases
Use real cases and exceptions to validate calculations, supporting evidence, and compliance with permissions. Analyze errors and revise the relevant criteria or implementation.
Retain existing documents, search, and SQL where they meet the need. Use an authoritative source for formulas and business rules in both explanations and implementation. Keep automation within delegated permissions and identify required approvals and unresolved issues in the results.
Harness, loops, and the two kinds of graph
Harness · Execution environment
Manage tools, permissions, persistent task state, execution records, and isolation.
Loop · Iteration control
Use intermediate results to investigate further, recalculate, or stop, within time, cost, and retry limits.
Execution Graph · Workflow
Define the sequence and branches across agents, tools, verification, and approval, including recovery points.
Execution Graph
Supply calculation → Options → Verification → Approval
How work gets doneBusiness Knowledge Graph
Product ↔ Substitute specification ↔ Customer terms ↔ Evidence
How the business worksEach workflow step retrieves applicable rules and evidence from shared knowledge. Use explicit workflows for known procedures and agent judgment where investigation must adapt. Prompts, RAG, semantic layers, and tool calls remain useful components.
Decision types, model routing, and inference optimization
Design for the decision type
Consider structured outputs and validation for classification and extraction, and statistical methods, ML, or optimization for forecasts and option evaluation. Combine retrieval, tool use, and staged reasoning for complex investigations.
Model routing
Select models by task type, difficulty, and required quality. Evaluate smaller and more capable models, and define when a failed quality check should trigger another model or additional verification.
Reasoning and context optimization
Adjust supported reasoning budgets and iteration limits, and select relevant evidence for context. Assess caching for repeated inputs, and evaluate total cost and latency, including retries and tool calls.
Validate required accuracy and business constraints first. Compare quality, latency, and cost on the same evaluation cases, rather than choosing solely by model price or token count.
From recurring exceptions to reviewed knowledge
Record exceptions and outcomes → Check causes and applicability → Propose a change → Owner review and approval → Version the change → Regression tests → Operational review
A verified relationship may update the ontology or knowledge graph; an approval condition may update policies or business rules. Fix missing data in the data pipeline and calculation errors in tools. Not every exception calls for a graph change.
Record the source, owner, scope, effective date, and version. Preserve individual decisions and execution history as decision memory, separate from approved enterprise knowledge.
Delivery Model
Delivery principles and development methodology
We shape the delivery approach around the client's business and operating context, then validate and scale the work one use case at a time.
01
Experience-Based Problem Framing
Use relevant operating and project experience to form a hypothesis, then test it against the client's business and data.
View Details ↓02
Project Knowledge
Structure business definitions, data meaning, decision criteria, assumptions, and project decisions so they remain reviewable and reusable.
View Details ↓03
Technology & Method Selection
Configure models, agents, knowledge, data, and evaluation methods around the business and operating conditions.
View Details ↓04
Evidence & Governance
Keep sources, validation records, decisions, approvals, and changes linked to the work.
View Details ↓Development Methodology
Eval-Driven Development — run the work, evaluate real outcomes, improve the system, and expand only what has been validated.
Eval-Driven Development — run the work, evaluate real outcomes, improve the system, and expand only what has been validated.
Experience-Based Problem Framing
Form a hypothesis from experience.
Test it against the work and the data.
We do not start by implementing a list of requirements. Relevant operating and project experience informs an initial view of the problem, its causes, and its business impact. We test that view against the client's decision criteria and data.
Problem Hypothesis
Use relevant operating and transformation experience to frame likely causes and business impact.
Business & Decision Context
Examine the workflow, decision criteria, constraints, exceptions, and approval structure around the issue.
Data & Evidence
Test the hypothesis against interviews, source material, and actual data, and separate findings from open questions.
Solution Direction
Use the validated problem to set priorities across process, data, systems, and AI.
Project Knowledge
Make project knowledge reusable and reviewable
Meetings, documents, analysis, and design work should not disappear into one-off deliverables. We structure business definitions, data meaning, decision criteria, exceptions, decisions, and unresolved questions as project knowledge.
Source & Context
Record where information came from and where it applies, including source documents, data definitions, interviews, and analysis.
Working Knowledge
Structure the terms, relationships, business rules, data meaning, and assumptions identified during the project.
Decision & Assumption Log
Track design decisions, selected alternatives, assumptions, and questions that still require confirmation.
Approved Knowledge
Update the client's documents, data standards, and operating rules after review and approval. Where a knowledge graph is used, update the concepts and relationships affected by those changes.
Project Knowledge Sequence
Source → Analysis → Working Knowledge → Review → Decision → Approved Knowledge → Documents · Standards · Relevant Relationships
Source → Analysis → Working Knowledge → Review → Decision → Approved Knowledge → Documents · Standards · Relevant Relationships
The methods and service scope specific to enterprise knowledge are covered in Enterprise Knowledge Engineering.
Technology & Method Selection
Choose the technology and methods to fit the assignment
We continuously assess models, agent architectures, and approaches to knowledge, data, and evaluation. We apply them selectively based on the work, data environment, security requirements, and operating model.
Research & Analysis
Review source material and data, compare issues and alternatives, and check important facts and figures against the original material.
Design & Build
Use requirements and project knowledge to design and implement data models, agent workflows, business interfaces, and system integrations.
Multi-Model Review
Use different models to review important analyses and candidate knowledge independently. Agreement between models does not by itself validate the result.
Reusable Skills & Guidance
Turn recurring analysis, review, and build procedures into reusable skills and guidance so teams can apply consistent criteria across projects.
Development Methodology · Eval-Driven Development
Validate one use case at a time, then scale
We do not attempt to complete an agent in one pass and switch over all at once. We define the workflow and success criteria, test real inputs and outcomes, and improve the work plan, knowledge, data, tools, and interface through each cycle.
01Problem FramingExperience-Based
02Project KnowledgeKnowledge & Decisions
03Technology & MethodsContextual Selection
04Evidence & GovernanceValidation & Control
Development MethodologyEval-Driven Development
01Business
Context
Context
02Work Plan
& Evals
& Evals
03Build
& Run
& Run
04Review
Results
Results
05Controlled
Rollout
Rollout
06Feedback
& Improve
& Improve
Evaluate & Improve Feed real-world results and user feedback into the next work plan and evaluation set.
01 BUSINESS CONTEXT
Understand the Work
Examine the current workflow, decisions, inputs, exceptions, and expected outcomes.
02 WORK PLAN & EVALS
Define the Work Plan & Evals
Set the sequence of work, data and tools, human review points, and evaluation criteria.
03 BUILD & RUN
Build & Run
Run the work plan while implementing the agent, data structures, and business interface.
04 REVIEW & ERROR ANALYSIS
Review Results
Compare outputs with established work products and expert judgment, then classify errors and missing information.
05 CONTROLLED ROLLOUT
Apply the Validated Scope
Move validated use cases into the operating environment after they pass security and operational gates.
06 FEEDBACK & IMPROVE
Improve and Repeat
Add findings to the evaluation set, update the work plan, knowledge, data, tools, and interface, and run the cycle again.
Iteration Cycle
Business Context → Work Plan & Evals → Build & Run → Review Results → Controlled Rollout → Feedback & Improvement ↺
Business Context → Work Plan & Evals → Build & Run → Review Results → Controlled Rollout → Feedback & Improvement ↺
Shared architecture, security, and operational approval gates remain in place. The iteration unit is the business use case; platform controls apply across the program.
Adoption & Business Measures
Verify the system. Assess its use.
Technical acceptance and business impact require different evidence. We agree on a baseline and measurement conditions, then review both release readiness and practical adoption within the project scope.
Baseline and acceptance
Agree on the workflow, target measures, and comparison basis with the client. Test correctness, permissions, and failure handling before release. Processing time, manual effort, and errors are possible measures, selected for the use case.
Adoption and review
Introduce verified workflows with clear operating and exception-handling procedures. Review usage, rework, and processing time. Record the measurement period, data conditions, and external factors before interpreting changes in performance.
Evidence & Governance
Keep each result linked to its evidence, reviews, and change history
Authorship alone does not establish quality. We record who produced and reviewed the work, then maintain its evidence, validation, and approval history.
Evidence
Link key claims, business rules, and data definitions to source material and data.
Validation
Check results against representative business questions, validation cases, data consistency, and system tests.
Review & Approval
Use model reviews as input. Designated reviewers or subject-matter experts approve important business knowledge and design decisions.
Traceability & Change
Maintain relationships among requirements, designs, code, tests, knowledge, and approvals, and assess the impact of changes.
Contact
Tell us what you are evaluating or already working on
Tell us about the initiative, its current stage, and the decision or system the work must support. We can review the service scope and where this delivery model fits.
Discuss Your Project →