PRACTICAL ENGINEERING OFFERINGS
Services & System Capabilities
I design and build software for businesses that need clear, dependable digital systems. Six specialized engineering services backed by verified code implementations and clear operational boundaries.
SERVICE 01
AI Agents & RAG Systems
Tool-using autonomous agents, multi-agent LangGraph graphs, and grounded RAG knowledge assistants bounded by strict schema guardrails.
FOR WHO: Engineering and operations teams needing context-aware AI tools that interact securely with internal databases and external APIs.
PROBLEM SOLVED: Replaces generic, ungrounded LLM prompts with deterministic state-machine agent graphs and verifiable vector retrieval.
Deliverables:
Multi-Agent Supervisor Pipelines (LangGraph)
Permission-Aware RAG Knowledge Systems
Autonomous Sales & Customer Service Agents
Human-in-the-Loop Approval Checkpoints
Long-Term Memory & Stateful Context Caching
TECHNICAL STACK
Python, LangGraph, Qdrant/pgvector, FastAPI, Pydantic, OpenAI / Anthropic APIs.
DETERMINISTIC GUARDRAILSAGENT_GRAPH_OBSERVATORY // LANGGRAPH_TOPOLOGYSTATEFUL SUPERVISOR ROUTER
01. SUPERVISOR NODE
Intent Router
Parses user payload and selects domain specialist agent.
STATE: EVALUATING
02. WORKER AGENT A
Entity Extractor
Extracts budget, timeline, and decision-maker roles.
PYDANTIC SCHEMAS
03. TOOL GATE
CRM API Node
Executes webhook API call with automated retry queue.
EXPONENTIAL BACKOFF
04. HUMAN GATE
Approval Checkpoint
Holds low-confidence outputs for human verification.
VERIFIED AUDIT LOG
SERVICE 02
Business Automation & Integrations
n8n workflow pipelines, vision document OCR extraction, and resilient webhook integrations connecting fragmented enterprise systems.
FOR WHO: Operations, finance, and non-profit teams spending hours manually copying data between disconnected SaaS tools.
PROBLEM SOLVED: Eliminates repetitive data entry, prevents silent webhook drops, and enforces schema validation across legacy tools.
Deliverables:
Enterprise n8n Workflow Pipelines
Multimodal Document & Invoice OCR Extraction
Automated CRM Lead & Ticket Routing
Custom Webhook Middleware with Exponential Retries
Real-Time Slack/Email Notification Systems
TECHNICAL STACK
Python, n8n, Playwright, REST/GraphQL APIs, Docker, Redis.
EXPONENTIAL RETRIESAUTOMATION_CONTROL_PLANE // N8N_PIPELINERETRY & QUEUE BACKEND
01
Trigger
Webhook Event
02
Auth Check
Token Audit
03
Validation
Pydantic Rule
04
Branching
Intent Router
05
Transform
JSON Mapping
06
Retry Queue
Redis Queue
07
Dispatch
CRM / Slack
SERVICE 03
Full-Stack AI Products
Production-ready SaaS MVPs, client portals, and administrative analytics dashboards built with Next.js App Router and TypeScript.
FOR WHO: Startups and mid-market companies needing robust, accessible, high-performance web applications without agency bloat.
PROBLEM SOLVED: Translates complex business rules into clean, maintainable software architectures with sub-second page loads.
Deliverables:
SaaS Minimum Viable Products (MVPs)
Secure Client & Audit Portals
Administrative Analytics Dashboards
API Integration Layers & PostgreSQL Schemas
WCAG AA Accessible Design System Implementation
TECHNICAL STACK
Next.js 16, TypeScript, React 19, Prisma, PostgreSQL, Tailwind CSS.
LIGHTHOUSE 90+ ACCESSIBLESYSTEMS_ARCHITECTURE_EXPLODER // LAYERED_STACKFULL-STACK SYSTEM DECOUPLING
Frontend Layer
Next.js 16 App Router, React 19, Tailwind CSS, TypeScriptAPI & Middleware
FastAPI, Pydantic Schema Validation, Rate LimitersOrchestration
LangGraph Multi-Agent Supervisor & State MachinesModel Providers
OpenAI GPT-4o, Anthropic Claude 3.5, Vision AIData Stores
PostgreSQL (Prisma), Qdrant Vector Store, Redis CachingObservability
LangSmith traces, Pydantic audit logs, Error retriesSERVICE 04
Data Science & Machine Learning
Exploratory data analysis, predictive classification models, feature extraction pipelines, and interactive data visualization dashboards.
FOR WHO: Data teams and business leaders requiring structured statistical insights, trend forecasting, or custom NLP classifiers.
PROBLEM SOLVED: Transforms raw, noisy tabular data and text logs into actionable statistical models and verifiable analytical reports.
Deliverables:
Custom NLP Intent & Entity Classifiers
Predictive Tabular & Time-Series Models
Automated Data Cleaning & ETL Pipelines
Interactive Analytical Dashboards (Recharts / D3)
Reproducible Jupyter & Python Data Workbooks
TECHNICAL STACK
Python, pandas, scikit-learn, PyTorch, FastAPI, Recharts.
FEATURE ENGINEERINGLATENT_SPACE_FIELD // VECTOR_CLUSTERSCOSINE DISTANCE MAP
Cluster A: High Intent Sales
Cluster B: Support Routine
Cluster C: OCR Invoice Data
SERVICE 05
AI Evaluation, Safety & Observability
Systematic testing harnesses for measuring groundedness, hallucination rates, tool success, API cost, and latency across AI workflows.
FOR WHO: Organizations deploying LLMs to production who need quantitative proof of accuracy, safety boundaries, and cost controls.
PROBLEM SOLVED: Replaces vibe-checking prompts with quantitative benchmark evaluations, cost tracking, and failure alert escalations.
Deliverables:
RAG & Agent Evaluation Test Harnesses
Groundedness & Hallucination Scoring Frameworks
Token Cost & Latency Optimization Profiles
Prompt Injection & Safety Boundary Audits
Detailed Failure Trace Logging Systems
TECHNICAL STACK
Python, Ragas, DeepEval, LangSmith, PostgreSQL, Custom Metrics.
QUANTITATIVE BENCHMARKSMODEL_EVALUATION_RADAR // SYSTEM_BENCHMARKQUALITY METRICS
Accuracy94%
Groundedness92%
Latency88%
Cost Control95%
Safety Guard98%
Robustness90%
Tool Success96%
Human Fallback100%
*Illustrative benchmark evaluation protocol — demonstration data, not a client result.
SERVICE 06
Research-to-Prototype Engineering
Converting academic AI papers and experimental architectures into clean, working technical proof-of-concepts.
FOR WHO: Product teams wanting to explore cutting-edge research methods before committing full development cycles.
PROBLEM SOLVED: Bridges the gap between theoretical research papers and production software feasibility.
Deliverables:
Technical Paper Analysis & Feasibility Reports
Working Proof-of-Concept Codebases
Architecture Comparison Benchmarks
Experimental Visualizations & Notebooks
System Trade-off Analysis
TECHNICAL STACK
Python, PyTorch, Next.js, FastAPI, Docker, Jupyter.
PAPER REPRODUCTIONRETRIEVAL_INTELLIGENCE // HYBRID_RAGVECTOR RETRIEVAL & RERANKING
STEP 01
Chunking
512 token splits
STEP 02
Embedding
Dense vectors
STEP 03
Qdrant Search
Cosine sim (k=25)
STEP 04
Cross-Encoder
Rerank top-5
STEP 05
Grounded Gen
With citations