AI systems for messy, high-value business problems.
Univ.AI combines machine-learning depth, software engineering, and business translation to build systems that survive contact with real workflows.
We build tools where models, documents, evaluation loops, and human review become working software. These projects often start as research spikes or operational prototypes and end as APIs, dashboards, traces, workflows, and documentation that client teams can keep using.
Evaluation infrastructure
Benchmark model, prompt, setup, and agent combinations across documents, with run tracking, prompt versions, judges, human review, composite scores, Elo comparisons, MLflow traces, and next actions.
Domain analytics
FastAPI, React, Semiotic, and SQLite turn ball-by-ball cricket data into deep-linked team, player, matchup, and scorecard views.
Developer platforms
Async SQLAlchemy with dataclass tables, foreign-key navigation, full-text search, migrations, and FastAPI CRUD routers.
Document intelligence
Document analysis and contract-review systems for XML/PDF uploads, retrieval, structured answers, citations, redlines, rationale, and reviewer signoff.
We pair modeling skill with domain constraints: audit trails, document citations, physical plausibility, deployment inside enterprise systems, and human review where it matters.
Fraud prevention for claims operations: suspicious claim patterns, review-ready analytics, and investigator workflows shaped around model outputs that fit the way claims teams actually work.
Risk scoring, anomaly detection, recommender systems, financial document extraction, model monitoring, and stakeholder-ready dashboards for high-stakes decisions.
Systems that search, reason over, and cite long contracts and loan documents, turning dense PDFs into structured, reviewable answers.
Convolutional neural-network approximations for reservoir physics, including pressure fields, water saturation, and oil saturation across rock strata from reservoir inputs.
Our engagements are scoped around work products that teams can inspect, run, and improve after the handoff.
Custom AI builds, research spikes, prototypes, production hardening, corporate courses, and team enablement.
Code, APIs, dashboards, experiment traces, model documentation, workflows, training material, and deployment handoffs.
Problem definition, model construction, business translation, deployment, monitoring, and training.
Email rahuldave@univ.ai with the problem, data context, and desired business outcome.