Full-Stack AI Applications
AI products with real interfaces, authentication, APIs, Postgres or Supabase data layers, deployment paths, and production-ready user workflows.
AI Systems | LLM Apps | Data Infrastructure
Production AI systems, built to hold up.
Aquila builds the software layer around AI: agent workflows, RAG pipelines, APIs, Postgres data models, analytics dashboards, and evaluation systems that make AI usable in production.
What We Deliver
AI software systems with the data layer built in.
Most AI projects do not fail because the model cannot reason. They fail because the application around it is brittle. Aquila builds the product surface, workflow controls, data spine, and review loop that let teams ship AI without guessing whether it worked.
Agentic Workflow Systems
Tool calling, workflow orchestration, state management, retries, fallback logic, review queues, and confidence thresholds for safe automation.
RAG & Knowledge Systems
Document ingestion, chunking, embeddings, vector search, metadata filters, citations, source review, and retrieval evaluation.
AI Data Infrastructure
ETL and ELT pipelines, AWS storage, Postgres models, reconciliation logic, lineage checks, and monitoring for AI-ready data products.
AI Analytics Dashboards
Operational dashboards that combine metrics, model summaries, trend narratives, client reporting, and drill-down evidence.
AI App Hardening & Evals
Prompt-injection testing, model regression checks, tenant isolation review, CI/CD guardrails, and launch-readiness audits.
Case Studies
Proof that looks like the work clients actually buy.
Each case study is framed as a product surface: architecture, interface, code patterns, data contracts, and operational controls required to move from AI demo to deployed system.
Agents
AI Agent Operations Console
A control surface for prompts, tool calls, run logs, confidence scores, review states, and database-backed task history.
Next.js | Supabase | Claude
Retrieval
RAG Knowledge Base With Evidence Review
Document ingestion, embedding, metadata filtering, source-grounded answers, and retrieval evaluation for decision workflows.
Pinecone | Postgres | OpenAI
Documents
AI Document Intelligence Workbench
PDF and scan extraction with schema validation, confidence gates, field-level evidence, and a human review queue.
Python | AWS | LLM APIs
Data Platform
Data Platform Control Tower
Pipeline health, bronze/silver/gold flow, data quality checks, lineage, backfills, and stakeholder-ready metrics.
SQL | dbt | Airflow
Analytics
AI Analytics Dashboard for Agencies
Client dashboards that combine performance data, charts, monthly AI summaries, and recommendation workflows.
React | APIs | Claude
Engineering Stack
Built across the app layer, the AI layer, and the data layer.
The positioning is intentionally software-forward: clients buying AI agents, RAG, and LLM apps are buying production systems, not notebooks or prompt experiments.
Application
- TypeScript
- Next.js / React
- Node.js APIs
- Supabase / Auth patterns
AI Systems
- Claude / Anthropic API
- OpenAI API
- LangChain / orchestration
- Prompt and retrieval evals
Data
- Python / SQL
- PostgreSQL / pgvector
- Pinecone vector search
- ETL and reconciliation
Cloud & Delivery
- AWS S3 / Lambda / RDS
- Docker / Git
- CI/CD guardrails
- Monitoring and review artifacts
Credentials
Data science training, production engineering delivery.
The work is grounded in quantitative training and business context: Johns Hopkins data science, JPMorgan risk analysis, research experience, and hands-on production AI/data systems work.
M.S. Data Science
Johns Hopkins University.
Risk Analysis
Former hedge fund credit analyst at JPMorgan Chase.
Production Systems
LLM apps, RAG pipelines, agent workflows, and evaluation loops.
Data Engineering
Postgres, ETL, dashboards, reconciliation, lineage, and cloud delivery.
Start Here
Need an AI system that can survive production?
Schedule a confidential conversation about your AI application, agent workflow, RAG system, data infrastructure, or launch-readiness risk.
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