Applied AI R&D
We help leaders choose the right AI opportunity, validate it with data, and turn it into a pilot that can be compared with the current process by time, quality, and cost.
Scientific rigorAI engineeringBusiness outcomes
Choose an entry point
Four routes replace a single long sales narrative. Choose the state you are in now and move directly to the relevant chapter.
Selected route
Map the processes, available data, and constraints, then choose one or two scenarios worth testing.
Open the formats chapterSelected route
Frame the question, collect relevant evidence, and produce a concise research brief for a go, change, or stop decision.
Open the method chapterSelected route
Fix the process and constraints, assemble a testable AI scenario, and observe it with the people who will use it.
Open the formats chapterSelected route
Measure time, quality, errors, interventions, and cost so the next decision is based on observed work.
Open the evidence chapterThe working story
Move through the argument one screen at a time. Every chapter has its own responsibility and a stable URL anchor.
The gap
The difficult part is rarely choosing a model. It is choosing the right problem, defining the usable data, and proving that the process became better.
Many AI opportunities exist, but not every one is tied to a real pain point, available data, and a management outcome.
Reviews, interviews, and analysis create value only when they produce requirements, scenarios, and verification criteria.
A prototype can look convincing. Without time, quality, and cost comparisons, the case for continuing remains unclear.
Decisions and arguments stay in chats and files, so the next team has to reconstruct what was already learned.
Find where the data, management value, and a fast route to evidence intersect.
Check whether sources, data, interviews, or market practice support the idea.
Compare an agent, prototype, or manual candidate process using explicit criteria.
Method
We do not start with development. The problem, data, and success criteria come first; research and prototyping follow only when the previous step is clear.
Map the process, participants, constraints, and intended outcome.
Test the hypothesis with sources, data, interviews, and market practice.
Create a prototype, AI agent, or working scenario for the selected problem.
Run the pilot in real work and gather structured feedback from the team.
Compare the process before and after by time, quality, errors, and cost.
First prove that the problem is worth solving. Then build the tool.
Working formats
Sometimes the right start is an audit. Sometimes one process is defined well enough to move directly into research, a pilot, or measurement.
Starting format
For teams with many ideas but no defensible first AI problem. We examine processes, data, and constraints to select one or two scenarios for testing.
Research
Validate a management or product hypothesis through sources, data, and interviews before development.
Outcome: research brief and a decision-ready conclusion.Pilot
Build an AI agent or prototype when the process and result criteria are already explicit.
Outcome: testable pilot and constraints for expansion.Memory
Structure project documents, decisions, and arguments so the team can preserve what it learned.
Outcome: knowledge base and decision history.Measurement
Compare the process before and after by time, quality, errors, interventions, and cost.
Outcome: continue, change, or stop recommendation.Evidence
A project should not end as a conversation. It should leave documents, working artifacts, and a decision that another team can inspect and continue.
A map of priority problems, hypotheses, constraints, and next experiments.
A concise record of what was tested, which evidence was used, and what follows.
An AI agent, prototype, or scenario tested inside the selected process.
Observed time, quality, cost, errors, and the decision on expansion.
Structured project memory: documents, decisions, findings, and instructions.
Evidence Lab combines their strongest contributions in one cycle: question, validation, pilot, and a measured next decision.
Research may stop before a working process exists.
A plan may be delivered without testing it in practice.
Technology can precede validation of the business problem.
The product may not fit the process, data, or constraints.
Connects the question, evidence, implementation, and decision in context.
Delivery
The exact team changes with the project. The stable principle is to connect scientific rigor, business context, and engineering execution.
Frames questions, selects methods, interprets results, and protects evidence quality.
Connects the work to economics, operations, commercialization, and management decisions.
Builds agents, prototypes, integrations, and the technical basis for verification.
Data and constraints
Before work begins, we agree what data can be used, where it lives, who has access, and what is removed afterwards.
Do not request sensitive data when the hypothesis can be tested without it.
Treat personal, client, and confidential information as explicit project boundaries.
Document regions, access, logs, retention, and deletion when the project crosses jurisdictions.
Legal and infrastructure conditions are agreed for each pilot and require separate approval before public claims are made.