Evidence-Based Insight Engine
Find the insight.
Keep the evidence.
Your business already has reviews, feedback, conversations, and documents. I build systems that extract useful findings from that material and let your team inspect the evidence behind them.
One source collection. One question worth answering.
From records to findings
Understand what
the evidence supports.
A useful finding needs more than a convincing sentence. It needs a path back to its sources, enough context to interpret it, and room for evidence that disagrees.
Preserve the sources
Bring in the agreed records with their identity, context, and access boundaries. Keep the material needed to inspect a finding later.
Extract observations
Identify specific statements and experiences, tied to supporting passages. Validate references before building broader conclusions.
Develop and check insights
Group related observations, propose findings, and examine their support. Keep disagreement and missing evidence visible.
Put findings to work
Deliver a reviewable report, structured export, or agreed integration. Choose how people use the findings and how updates are reviewed.
Start with a real question
What do you need
to understand?
The question determines what to extract, how to group it, and what counts as useful evidence. These are possible engagement scopes; each needs validation on your own data.
| Existing material | Question | Useful output |
|---|---|---|
| Customer reviews | What do customers consistently value or struggle with? | Supported themes, disagreements, and source examples |
| Support conversations | Where do customers repeatedly get stuck? | Recurring friction with context for investigation |
| Surveys and interviews | Which needs recur, and where do experiences differ? | Findings tied to responses, with qualifications preserved |
| Project records | Which lessons recur across completed work? | Traceable lessons and questions for expert review |
The first engagement
A working path
through your data.
We choose a representative collection, a recurring question, and the people who will use the findings. Together we define examples of useful insights, unsupported conclusions, and important exceptions.
I implement the agreed ingestion, extraction, validation, and delivery path. The handover includes code, operating documentation, and an evaluation against the reviewed examples. Quality, review effort, and processing costs become visible before we expand the scope.
You bring source access, business context, and a reviewer who knows the domain. The proposal sets the output format, integrations, ownership, and acceptance criteria.
Built from Lemtika
Research first.
Applications follow.
In Lemtika, I built a research pipeline that turns customer reviews into observations and insights linked to their evidence. A separate generation layer uses eligible findings to shape a website.
That implementation informs this engagement. A new domain needs its own source handling, definitions, and checks. We establish those boundaries before expanding to more data or destinations.
Need to turn selected findings into content? The AI Content Engine covers that downstream workflow.
Practical questions
Before we build.
Your first workflow
What is your data
trying to tell you?
Tell me what material you already have, what you need to understand, and who will use the answer. We will scope a workflow your team can evaluate.
Scope your insight workflow ↗