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.
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.
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 ↓Project Knowledge
Structure business definitions, data meaning, decision criteria, assumptions, and project decisions so they remain reviewable and reusable.
View Details ↓Technology & Method Selection
Configure models, agents, knowledge, data, and evaluation methods around the business and operating conditions.
View Details ↓Evidence & Governance
Keep sources, validation records, decisions, approvals, and changes linked to the work.
View Details ↓Eval-Driven Development — run the work, evaluate real outcomes, improve the system, and expand only what has been validated.
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.
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
Incorporate validated and approved content into the client's knowledge graph, data standards, and operating criteria.
Source → Analysis → Working Knowledge → Review → Decision → Approved Knowledge → Knowledge Graph · Standards
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.
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.
Context
& Evals
& Run
Results
Rollout
& Improve
Evaluate & Improve Feed real-world results and user feedback into the next work plan and evaluation set.
Understand the Work
Examine the current workflow, decisions, inputs, exceptions, and expected outcomes.
Define the Work Plan & Evals
Set the sequence of work, data and tools, human review points, and evaluation criteria.
Build & Run
Run the work plan while implementing the agent, data structures, and business interface.
Review Results
Compare outputs with established work products and expert judgment, then classify errors and missing information.
Apply the Validated Scope
Move validated use cases into the operating environment after they pass security and operational gates.
Improve and Repeat
Add findings to the evaluation set, update the work plan, knowledge, data, tools, and interface, and run the cycle again.
Business Context → Work Plan & Evals → Build & Run → Review Results → Controlled Rollout → Feedback & Improvement ↺
This approach is not tied to a single platform or framework. It adapts contextual evals, agent loops and evaluator–optimizer patterns, and operational gates and staged rollout to enterprise delivery.
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.
KOAA
KOAA is our methodology for applying business knowledge and decision criteria to Agentic Analytics workflows.
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.
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