Which retrieval configuration earns deployment?
Compare two candidates against the evaluation set. Defend the threshold and publish the failure cases.
CONSTRAINTSp95 latency < 1.2scitation recall > 0.91cost < ₹0.18 / query
Take learning beyond the classroom.
BayesStack helps colleges bring industry-shaped programs, hands-on building and meaningful professional access to undergraduate students.
Students test a working prototype during an open lab session.
THE GAP WE CLOSE
A curriculum is only part of an education.
Students grow when knowledge meets difficult work, useful feedback and people already practising the craft.
01
Current curriculumCourse architecture shaped with practitioners and made ready for academic review.
02
Faculty ownershipMaterials, tools and enablement that help your faculty lead every delivery.
03
Work worth inspectingProjects, reviews and evidence that reveal what students can actually do.
01 / Programs
New fields become rigorous courses your faculty can own.
Practitioners help shape the capability. BayesStack turns it into a credit-ready course with teaching plans, studios, assessments, rubrics and faculty enablement.
- Board of Studies package
- Purpose-built learning studio
- Delivery support that transfers ownership
STUDIO / MILESTONE 02
● SavingBuild a grounded answer pipeline
TaskDatasetNotebookEvidence
evaluation.pyRUN 08
01candidate = load()02result = evaluate(candidate)03assert recall > 0.9104publish(result)✓ 12 checks passedEvidence updated
LAB OPS
WEEK 08 / LIVEACTIVE BUILDS12+3 this month
REVIEW READY05Thursday gate
MENTOR HOURS38Booked / 44
Campus compute control plane REVIEW
Vision quality inspection BUILD
Payment failure router TEST
02 / Labs
Give ambitious student work a serious home on campus.
A BayesStack Center of Excellence is an operating system for projects, not a room full of equipment. It brings together selected cohorts, mentors, technical infrastructure, review cadence and public showcases.
- A year-round project pipeline
- Practitioner and faculty mentorship
- Evidence from every milestone
03 / Partnerships
Make industry more than a guest lecture.
Bring practitioners into the learning journey at the moments where their context matters most: when a brief is framed, an architecture is challenged, a project is reviewed or a cohort enters the field.
- Small-format technical masterclasses
- Architecture and milestone reviews
- Prepared industry and cultural immersion
ACCESS PORTFOLIO
2026 / 27FOR COMPUTER SCIENCE / YEAR 3
Proximity at the moments that matter.
01FRAMEA live challenge enters campus
02ENTERStudents meet the system in context
03REVIEWPractitioners question the work
04RETURNInsight reaches the wider cohort
DOMAIN PORTFOLIOS
Courses students want to talk about.
Current enough to be exciting. Structured enough to belong inside a serious undergraduate education.
01
AI EngineeringBuild, evaluate and operate intelligent systems.
Foundation models, agentic systems, computer vision, production ML and responsible AI.02Cloud & PlatformDesign the systems everything else depends on.
Distributed systems, internal developer platforms, infrastructure as code, SRE and incident practice.03CybersecurityLearn to break assumptions and defend real systems.
Application security, cloud identity, detection engineering, threat modeling and incident response.04FinTech & QuantEngineer trustworthy systems for money and markets.
Digital payments, trading systems, financial data, model risk, fraud and quantitative research.SEE THE SYSTEM
Serious learning leaves evidence behind.
Move through four things an institution, faculty member and student can inspect.
01 / 04
A studio students can build in
Browser workspaces, tests, runtime feedback and project evidence stay connected to the course.
STUDIO / MILESTONE 02
● SavingBuild a grounded answer pipeline
TaskDatasetNotebookEvidence
Which retrieval configuration earns deployment?
Compare two candidates against the evaluation set. Defend the threshold and publish the failure cases.
CONSTRAINTSp95 latency < 1.2scitation recall > 0.91cost < ₹0.18 / query
evaluation.pyRUN 08
01candidate = load()02result = evaluate(candidate)03assert recall > 0.9104publish(result)✓ 12 checks passedEvidence updated
REAL CAMPUS PRACTICE
Designed for the colleges students actually attend.
BayesStack works with the institution you have, then helps raise what students and faculty can do inside it.
START WITH A REAL NEED
What should your students become capable of next?
Bring us the gap you can see. We will help shape the program, lab or industry partnership around it.