AI engineering, evaluation, and practical software development.
We build tools and reference implementations for teams working with language models, retrieval, model adaptation, and application delivery. Start with the project that matches your use case, then follow its setup guide and tests.
Website · Acadify AI Lab · Contact
| Project | What you can explore |
|---|---|
| AI Evals Framework | Dataset-based evaluation, metric thresholds, and ordered evaluation reports |
| LLM Security Proxy | API-key access, pattern-based prompt screening, PII redaction, and audit events |
| Fine-Tuning Pipeline | Dataset preparation, LoRA/QLoRA training code, and training configuration examples |
| Enterprise RAG Boilerplate | Document ingestion, metadata-filtered retrieval, vector-store adapters, and response checks |
| Autonomous Agent Swarm | Task queues, workflow dependencies, persisted state, recovery, and human approval gates |
| Aegis MVP Launchpad | A deterministic planning demo with a React dashboard and FastAPI backend |
| ASR Feedback | Feedback schemas, example records, SDK source, and evaluation playbooks |
| Sales Automation | A local sales dashboard and configurable lead, CRM, and follow-up workflows |
| Engineering Standards | Git branching, review practices, coding guidance, and release checklists |
These repositories are reference implementations and development tools. Read each README and security policy for implemented behavior, mock modes, configuration, and limitations. Passing unit tests does not establish production readiness, regulatory compliance, or complete protection from prompt injection.
Check the repository's Actions tab for current validation results and its Pull requests tab for proposed changes. Infrastructure examples require a separate review of access controls, costs, networking, and operational needs before deployment. Project-specific license terms are in each repository.
For implementation and architecture inquiries, contact contact@acadifysolution.com.
For code contributions, follow the target repository's CONTRIBUTING.md, open a
focused branch and pull request, and include the checks you ran. For suspected
security issues, use that repository's SECURITY.md reporting instructions and
keep credentials and sensitive records out of public issues.