Proven depth
AI for Higher Education
Institutional analytics, research computing, and governed campus AI.
Business outcomes AI can move
- Increase enrollment yield and tuition revenue per cohort
- Reduce student attrition and protect recurring tuition
- Cut administrative cost per enrolled student
- Accelerate research grant cycles and publication throughput
AI capabilities
Each capability maps to a business KPI, revenue, cost, risk, or cycle time, not model accuracy on a slide.
Revenue
Enrollment yield optimization
Predict which admitted students will enroll and which need outreach, fill seats without over-discounting.
Student retention models
Flag at-risk students early, advising interventions that protect retention revenue.
Cycle time
Admissions & inquiry agents
Answer prospective student questions 24/7 with governed retrieval, more qualified inquiries, fewer counselor hours.
Research data pipelines
Automate ingestion, QC, and feature extraction for grant-funded work—faster time to results and renewals.
Production use cases
- Enrollment & retention forecasting
- Research data pipelines
- Responsible AI for campus operations
Frequently asked questions
How do you scope AI for our industry?
Week-one discovery locks business KPIs—revenue, cost, risk, or cycle time—not model vanity metrics. We map your data sources, compliance constraints, and existing stack, then propose a fixed-scope mandate with acceptance criteria or redirect you to a simpler path.
Do you have experience in our sector?
Proven depth sectors (financial services, federal, real estate, telecom, higher education) reflect production systems we've shipped. Growth sectors are selective mandates where agentic and ML patterns transfer with industry-specific tuning. Biotech, insurance, and regulated work often runs under NDA.
What do we get at the end of an engagement?
Deployable code in your repos, monitoring and runbooks, documentation your team can operate, and knowledge-transfer sessions. The Production Standard is contracted in writing—no handoff to a junior bench after the sale.
What's the fastest way to start?
Book a discovery call or start with a bounded diagnostic: $2,500 Postgres audit, insurance claims-fraud diagnostic from $7,500, or the free MLOps readiness assessment. Each path surfaces whether AI is the right tool before a larger commitment.
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Selective intake · 2026
Your next system shouldship to production.
Agentic development from architecture through deployment, with the Production Standard contracted in writing.