We need experience to get a job. We need a job to get experience. Let's break that loop.
So "years of experience" doesn't start at zero.
We build production AI systems for communities that need them most.
If you want hands-on engineering tied to partner needs, there is room to contribute here.
From student to engineer. For real.
Faculty & Advisors
“Research rigor means being able to show your work at every step — the methods, the assumptions, the limitations...”
“Most agentic AI research focuses on what these systems can do. We at AnacodicAI Labs focus on who they're for...”
Why we exist
Some communities have a clear need for technology and no path to it. Not because the problem is unsolvable, but because no one is paid to solve it for them.
We build in that gap: students gain real-world experience on real data, held to a peer-reviewed evidence bar.
44M teachers short by 2030. 66% of Malawi primary schools: 90+ students per teacher.
We build: TerrierGrader
Source · UNESCO · Global Report on Teachers (2024) · UNICEF Malawi · Budget briefs (incl. Education 2024–25) · OECD · Education at a Glance 2024
Rural clinics with no access to latest research. < 1 psychiatrist per 100K in low-income countries.
We build: ClinicalSearch
Source · PMC · AI & telemedicine in rural communities (2025)
Skill-rich artisan communities locked out of premium markets.
We research: WeaveForward
Source · Reimagining craft for community development (T&F) · IIT Kanpur · YUKTI craft kārkhānās as living labs
How we bridge it
You bring the skills; they hold the mandate. We run the collaboration like research and ship like production. Clear scope, honest review, systems held to a production bar, not a demo bar.
Students gain production commits, published research, and documented contributions. See:
Where we build
Rooted at Boston University.
Built for where infrastructure, capital, and specialist access run out first.
Open research threads · What we're researching right now
Faculty-advised research — team forming
→ Core contributors included as co-authors
Evidence Retrieval AI
Evidence retrieval for resource-limited clinical settings where standard access infrastructure is unavailable. The gap between published literature and frontline care decisions is the core constraint. Research conducted with faculty advisors at Boston University.
Apply by April 30, 2026
Faculty-advised research — team forming
→ Core contributors included as co-authors
AI Energy Research
Extension of the published CCI energy benchmarking framework to deployment environments with infrastructure constraints. Energy cost characterization and carbon-aware model selection studied under constrained deployment conditions.
Apply by April 30, 2026
Faculty-advised research — team forming
→ Core contributors included as co-authors
Academic AI
Automated assessment support for resource-constrained educational environments with high enrollment-to-instructor ratios. Feedback consistency and throughput under structural volume constraints are the organizing problem. Research conducted in coordination with active institutional deployments.
Apply by April 30, 2026
Faculty-advised research — team forming
→ Core contributors included as co-authors
Business Intelligence AI
Cost-constrained intelligence for operators in emerging markets where incumbent analytics tooling is priced out of reach. The analytical access gap is the organizing constraint. Research conducted with active deployment evaluation across geographic regions.
Apply by April 30, 2026
What we're building right now
Academic AI
Production evaluation pipeline with rubric-aligned generative assessment and self-consistency verification. Retrieval-augmented feedback synthesis across configurable assessment criteria. 500+ documents per evaluation cycle, 3 institutional deployments, 60% overhead reduction.
RAG Systems · Prompt Engineering · NLP
Apply by April 30, 2026
Optimization AI
Hierarchical multi-agent system with domain-constrained specialist delegation for cross-vertical optimization. Orchestrator-mediated coordination across regulatory compliance, demand forecasting, and inventory agents.
Agent Architecture · Ensemble ML · XGBoost
Apply by April 30, 2026
Business AI
multi-agent orchestration layer on AWS with Model Context Protocol integration and dynamic tool selection. Function-calling coordination across domain-specialized agents. Deployed across 4 geographic regions at 90% cost reduction.
AWS · MCP Protocol · Agent Orchestration
Apply by April 30, 2026
Founded at BU · Systems backed by research · Volunteer-run
Collaborating with: Boston University · Boston Children's Hospital · Harvard Medical School · Cleveland Clinic
