AI Engineering & Automation
Practical AI that actually ships
LLM-powered workflows, internal tools, and guardrailed automations — built to save real time without turning your product into a science project. Part of MGR Ventures services. No hype, no hallucination-prone black boxes handed off without guardrails.
What I Build
AI features and automations designed around your actual workflow — not demos that look impressive but fall apart under real conditions.
LLM Integration
Connect OpenAI, Anthropic, or open-source models to your existing application via clean API wrappers. Prompt engineering, context management, and response parsing handled properly so the output is actually usable downstream.
Workflow Automation
Repetitive internal processes replaced with automated pipelines: document processing, data extraction, classification, routing, and notification flows that run without babysitting.
Internal AI Tools
Custom-built tools for your team: summarizers, draft generators, search interfaces over your own data, and assistants scoped to your domain — not general-purpose chatbots with no guardrails.
RAG Pipelines
Retrieval-Augmented Generation setups that let a model answer questions grounded in your own documents, knowledge base, or database — with citations and controlled scope so it doesn't make things up.
API & Webhook Integrations
AI features wired into your existing stack via REST APIs and webhooks. Triggers from form submissions, CRM events, database changes, or scheduled jobs — no manual intervention required once it's running.
How I Approach It
AI work goes sideways when the scope is fuzzy and the outputs are unmeasured. A clear process keeps it from becoming an endless experiment.
Define the Problem First
Before any model gets involved, I nail down what success looks like: what's the input, what's the expected output, and how will we know it's working. Vague goals produce vague AI features.
Pick the Right Tool
Not every problem needs a large language model. Sometimes a regex, a classification model, or a simple rule engine is faster, cheaper, and more reliable. I recommend what actually fits — not whatever's trending.
Build With Guardrails
Output validation, fallback logic, rate limiting, and error handling built in from the start — not bolted on after something breaks in production. LLMs fail in unpredictable ways; the wrapper needs to handle that gracefully.
Measure & Hand Off
Before handoff, I establish a baseline: latency, cost per call, error rate, and output quality against a representative sample. You get documentation and a clear picture of what's running and why it's configured the way it is.
Built for Production
Demos work in a notebook. Production AI needs cost controls, failure modes, and observability baked in from the start.
Output Validation
LLM outputs are parsed and validated before they touch anything downstream. Schema enforcement, confidence thresholds, and human-review queues for edge cases — so a bad generation doesn't corrupt your data or confuse your users.
Cost Controls
Token budgets, model tiering, caching strategies, and per-user rate limits set before launch. API costs can spiral fast on AI features — usage is monitored and alerting is in place before it becomes a surprise bill.
Fallback & Retry Logic
Rate limit errors, timeouts, and unexpected model responses are handled with retry logic, exponential backoff, and graceful degradation — not unhandled exceptions that surface to the end user.
Logging & Observability
Inputs, outputs, latency, and cost per request logged in a structured format. Gives you visibility into what the model is actually doing in production and a paper trail when something needs investigating.
Ship AI that works in the real world
Whether you have a clear use case or just a problem worth solving — send a note with where things stand. You'll get a straight assessment, not a pitch deck full of AI buzzwords. Related: Web Development, Backend & API Development, DevOps & Deployment, and Technical Consulting.
Contact MGR Ventures Let's get to work