How can AI make research better, faster, and more enjoyable?

A persistent, attentive intellectual companion for your research: sustained, critical engagement available on demand, for as long as you need it.

AI learns how you write, your vocabulary, your sentence rhythms, your disciplinary register, and produces text that sounds like you, not like a machine. The result is a reusable voice profile: a permanent tool you keep and use across any writing task. Multiple profiles can be created for different registers and audiences. The way you write a journal article is not the way you write a grant summary or a blog post. This is a fundamentally different experience from the generic, standardized output that most people associate with AI-generated text.

Translating complex research into compelling presentations. This is a skill that directly affects career trajectory and that few academics are trained for.

Making your research accessible beyond the academy: blog posts, op-eds, newspaper articles, Substack and newsletter content, podcast preparation, public lectures, media appearances. AI helps find the right register for non-specialist audiences while preserving the substance of your work.

AI assists on both sides of the review process.

Translations that preserve domain terminology, scholarly nuance, and disciplinary conventions, far beyond what AI consumer translation tools deliver.

When multiple authors work on a single manuscript, AI helps manage what is usually a painful coordination problem: synthesizing sections written in different styles, harmonizing voice and terminology across contributions, checking that the argument remains consistent from introduction to conclusion, flagging contradictions between sections, and tracking how the manuscript evolves across revision rounds. The result is a more coherent paper, produced with less friction between co-authors.

Figures, graphs, and diagrams for papers, presentations, and grant proposals. AI generates, iterates, and refines visualizations from your data, finding the clearest way to present your results for the specific context where they will appear.

Structured, actionable feedback on doctoral chapters and dissertations: detailed, specific, and available on demand.

Turning your existing collection of files, notes, and documents (scattered across folders, drives, and forgotten corners) into a living, searchable system. AI makes your personal archive retrievable: old research becomes findable, connections between things you wrote years apart surface, and nothing you have produced is lost anymore.

Building a centralized, local, portable, and highly personalized research system: a second brain that integrates all the workflows listed above into one environment. It captures, connects, and retrieves everything you produce and encounter in your academic life: reading notes, seminar ideas, supervision notes, research thoughts in transit. It knows your field, your sources, and your arguments. A full prosthetic second brain that evolves with you for the rest of your career and beyond, providing support across both professional and private life. My own system is the working model. The coaching teaches you to build yours.

Finding, screening, extracting, comparing, and synthesizing sources, without missing critical papers or spending months on the process.

AI supports hypothesis development through multiple pathways, matched to your research stage and data availability.

AI simulates cognitive interviews and pre-tests your survey with synthetic respondents before you go to the field. Confusing questions, response-scale problems, and acquiescence bias surface in hours instead of weeks of piloting.

AI identifies bias in your questions, tests alternative wordings, and refines your interview protocol before you sit down with participants, without needing human pilot rounds.

Research-grade transcription of interviews and focus groups, with verification workflows that catch errors and fabricated content that automated tools silently introduce.

Removing identifying information from transcripts, field notes, and documents before analysis, preserving analytical value while protecting participants.

Getting readable, searchable text from handwritten manuscripts, degraded print, and garbled output from optical character recognition software.

Thousands of unsorted files with no metadata. A common archival reality.

AI assists across methodological traditions, accelerating the mechanical parts of coding (labeling and categorizing segments of your data) while you stay in control of interpretation.

Applying discourse analysis methods at scale: identifying rhetorical patterns, tracking discursive shifts, and processing large corpora that would be impractical to handle manually.

Extracting structured, queryable information from large bodies of text: historical archives, literary collections, legal records, contemporary documents.

When your verified dataset is too small for statistical power, AI-generated predictions (corrected against your ground truth) can bridge the gap without treating AI output as real data.

AI-assisted support through the entire grant application lifecycle.

The level of my involvement varies: from targeted advice on a specific section you handle yourself, to ready-made workflows and templates you learn to use and develop further, to intensive co-writing where I work alongside you on the content and research strategy itself. The same approach applies to individual scholarships and fellowship applications: personal statements, research plans, CV positioning.

Giving detailed, specific feedback to every student is one of the most time-intensive parts of teaching, and one where consistency is hardest to maintain across large numbers of submissions. AI applies your standards evenly, generates structured feedback more efficiently, and frees your time for the responses that require your personal judgment.

How do you know your students actually learned something, when AI can produce most of the essays, papers, and assignments they would traditionally submit? Two distinct design challenges, each with its own solutions:

Syllabi, reading lists, exercises, slides, presentation materials: the operational infrastructure of teaching. AI accelerates the production of course materials and helps ensure that what you teach, what students do, and what you assess are actually aligned.

Cover letters, CV optimization, teaching and research statements, writing sample selection, each tailored to the specific job ad, institution, and position.

From your notes to a polished, personalized recommendation draft in minutes.

CV, publications, and project records compiled into formatted annual reports.

A working academic website. You walk away with the site, the knowledge to maintain it, and no dependency on a web developer.

Triage, prioritization, and draft responses for high-volume academic email. Routine correspondence handled in minutes; you focus on the messages that require your judgment.

Getting informed consent forms and ethics applications through review on the first try, with proper readability, completeness, and regulatory compliance.