academAI builds proven software systems for research and teaching — configured to your protocol, running on your institution's own systems, and fully owned by your team.
What we build
We build three categories of academic software. Each is designed around a specific class of workflow — and configured to your exact requirements at deployment.
Pipelines that apply your coding protocol to video, audio, or sensor data — outputting timestamped event logs with confidence scores, formatted for R, SPSS, or custom analysis environments. Applicable to any structured observation study with a defined behavioral coding scheme.
Event-based · Time-samplingSystems that transform raw observational data into structured, analysis-ready datasets — with consistent schema across annotation cycles, audit logs for compliance, and version control built in. Designed so a different lab member, years later, can reproduce the same output.
Reproducible · Audit-loggedAI systems that operate within instructor-defined boundaries — scoped to specific course content, with professor-side visibility into every student interaction. Model-agnostic, deployable on institutional infrastructure, priced for grant budgets.
Model-agnostic · Institution-deployableWhat we've built
Each of these systems was built for a real client to solve a specific problem. If you're facing something similar, we can build a version configured to your context — owned entirely by your team.
We built an automated observational coding system for a research lab studying early childhood behavior. It ingests classroom video and outputs timestamped, analysis-ready datasets — replacing weeks of manual frame-by-frame review.
Built for
A behavioral science lab running event-based observation studies where manual coding was delaying analysis by months. The system was configured to their specific coding protocol, validated against their annotators, and runs on their own hardware under IRB compliance.
Have a similar problem? Let's talk →What this system produces
Timestamped event logs — every coded behavior with start/end times to ±0.1s and a per-code confidence score, validated against human-annotated ground truth using inter-rater reliability methods
Multi-label behavior annotations — gaze direction, body orientation, gesture type, verbalization, and object interaction coded simultaneously per individual across 100–200× the volume of manual coding
Structured dataset exports — CSV and JSON formatted for direct import into R, SPSS, or custom analysis pipelines, with consistent schema across annotation cycles
Human review interface — browser-based tool for inter-rater reliability checks; verified corrections feed back into model retraining automatically, improving accuracy with each reviewed dataset
Full processing logs — model version, parameters, and pipeline configuration documented for methods section reproducibility; re-run the same pipeline on new data and get the same structure
We built a course-scoped AI tutoring system for a faculty member teaching quantitative methods. Students get an AI that only knows the course material. The professor gets a dashboard showing every conversation, with engagement signals and review summaries.
Built for
An instructor who wanted students using AI as a thinking partner, not a homework machine — with full visibility into every interaction and control over what the tool will and won't do. The system runs on the institution's existing infrastructure with no IT procurement required.
Interested in something like this? →What this system delivers
Professor-controlled boundaries — topic limits, assignment-specific behavior, and refusal rules set by the instructor and invisible to students
Multi-agent screening pipeline — every student input is validated before reaching the AI, blocking off-topic requests and prompt injection attempts automatically
Automatic review summaries — surfaces which students are engaging critically and which are copying outputs, without the professor reading every transcript
Model-agnostic — runs on Claude, Gemini, DeepSeek, or an institution's existing AI license; switch providers in config without touching the codebase
Pluggable deployment — free-tier cloud for a pilot, institutional servers for production, Docker on a laptop for a demo; auth supports Google Workspace, Microsoft/Entra, SAML, or invite codes
How these systems are built
Every deployment ships with full version control, processing logs, and configuration records. Re-run the same pipeline on new data years later — on different hardware, by a different lab member — and get the same structure. Outputs are documented for methods sections without requesting anything from us.
Built in independent, testable components. Add a new behavior code, swap a model provider, or extend to a new data type without rewriting the pipeline. Each system is designed to grow with a study — not require a new build every time the protocol evolves.
Deploys on your hardware, your HPC cluster, or your institution's cloud environment. No dependency on our servers, no third-party data transfer, no new procurement. Your data stays exactly where your IRB requires it — and stays there permanently.
Processing logs, model metadata, and parameter records are structured for methods section inclusion. Reviewers can audit exactly what ran, in what version, on what data. No black box — every output is traceable back to a specific pipeline configuration.
What working with us looks like
Every engagement follows the same structure — designed so you know exactly what to expect at each stage.
You describe your workflow and bottleneck. We assess whether our systems fit — and tell you directly if they don't.
Together we review your protocol, IRB constraints, and infrastructure to define the exact configuration.
You receive a fixed-scope proposal — specific deliverables, fixed price, clear timeline. No hourly billing.
We configure the system to your protocol, validate against your data, and deploy on your institution's hardware.
Your team operates the system independently, with full documentation for methods sections. We remain available for support and iteration.
Who we work with
Every engagement is scoped as a fixed deliverable with clear ownership. No ongoing license fees, no vendor lock-in.
Labs running observation studies, coding video data, or managing annotation at scale. If your team is spending weeks on manual coding before analysis can start, we build the pipeline that removes that bottleneck — configured to your protocol, running on your hardware.
Professors running studies without a large lab team — who need research-grade tooling without an enterprise budget or a lengthy IT process. We scope projects to fit grant deliverables and hand off systems your team can run independently from day one.
Instructors who want AI integrated into their course in a way that supports student thinking rather than replacing it — with visibility into how students are using it and full control over what the tool will and won't do. Deployable mid-semester without IT involvement.
Centers coordinating studies across multiple faculty and grant cycles who need data infrastructure that's consistent, reproducible, and doesn't depend on any one lab member staying around. Systems are documented and transferable — built to outlast the project that funded them.
Start a conversation
Initial conversations are exploratory — no commitment required. Describe your workflow and current bottleneck and we'll respond within one business day with an honest assessment of whether our systems are a fit and what configuration would look like.
Most deployments are scoped as fixed-price deliverables that fit within a single grant budget line. You'll receive a clear scope and price estimate after an initial conversation — no obligation, no hourly billing.