Organizations sit on mountains of unstructured text — interviews, clinical notes, survey responses, customer logs. I bridge cutting-edge academic NLP with practical LLM engineering to transform complex language into reliable, structured insights.
What We Can Build
Most data science tools are built for numbers; I build tools for human language. I help teams across various sectors automate their text-analysis bottlenecks.
Automating or accelerating analysis of interviews, open-ended survey responses, focus groups, and qualitative corpora — extracting themes, arguments, needs, emotions, or domain-specific codes while preserving validation against human judgment.
Extracting structured psychological or clinical indicators from patient narratives, intake forms, therapy transcripts, or health questionnaires — with strict emphasis on privacy, expert validation, and careful interpretation.
Turning user interviews, usability notes, feature requests, and market research responses into structured insights: taxonomies of user needs, pain-point clusters, decision-relevant themes, and representative evidence from the raw text.
Processing high volumes of public inquiries, user feedback, or customer support logs to automatically classify intent, cluster recurring complaints, and route issues — at scale.
Distilling actionable insights, efficiency metrics, and structured metadata from messy meeting minutes, vendor reports, and dense corporate documents.
How We Build It
Whether you know your architecture or simply know you have a textual data problem, I structure projects into two tracks based on data scale, privacy requirements, and task complexity.
The Methodology: Advanced Prompt Engineering and In-Context Learning (ICL). We use powerful existing models and design sophisticated instructions, few-shot examples, and logical constraints to reliably pull exactly what you need.
The Methodology: Efficient Fine-Tuning (PEFT/LoRA). We train a compact, open-weight model (Llama, Qwen, or Mistral) on your private datasets so it masters your specific textual domain.
The Foundation
Regardless of track, the validity of the data is everything. Every NLP pipeline is built and validated to a rigorous academic standard so that stakeholders, clients, and peer-reviewers can fully trust the results.
Systematically testing and tuning the model against your gold-standard data (e.g. human-labeled annotations) to ensure maximum accuracy on your specific task.
Measuring performance using accepted statistical agreement metrics to prove the automated pipeline is as consistent and reliable as human experts.
Optionally supplementing human validation with "LLM-as-a-Judge" workflows, using secondary models to score text or monitor compliance against your specific rubrics.
How It Works
You do not need to have the architecture mapped out before we speak. Start with your data problem.
We start with a brief scoping call and a sample of your raw data. I provide a preliminary assessment of what can be extracted and an estimation of the expected baseline accuracy range.
I operate on a project-based, fixed-price model. You know exactly what the pipeline will cost and what deliverables you will receive before work begins.
You receive the fully annotated datasets, the trained model weights (if applicable), and the methodological documentation required for your internal stakeholders or academic publications.
About the Architect
I am a faculty member and the Director of the NLP for Human Sciences Lab at Ariel University. My academic research focuses on the intersection of language, artificial intelligence, clinical psychology, and human behavior.
Read My Academic Profile & Publications →Let's Discuss Your Data
I accept a limited number of consulting projects per semester to ensure the highest level of rigorous, hands-on architectural design. Reach out with a brief overview of your dataset and objectives to schedule a preliminary call.