Put an LLM Feature Into Your Product Without a Research Project
Most teams do not need a new model. They need a model wired into the product they already have: answering questions over their own data, reading documents, drafting text, with the same reliability as the rest of the backend. I build that layer in Python, with LangChain, HuggingFace and FastAPI, and deploy it on your cloud. At SecurityScorecard I took an LLM chatbot from zero to AWS production in 6 weeks.
What Problems I Solve
Chat Over Your Own Data
Let users ask questions in plain language and get answers from your database, documents or APIs, instead of learning a query screen or opening a support ticket.
Document Processing
Extract fields, classify, summarize and route documents that people read by hand today, with checks that flag low-confidence results for a human.
Prototype to Production
A demo works on a laptop. I add what production needs: an API, authentication, rate limits, logging, evaluation, cost tracking and a deploy pipeline.
Private Models
When data cannot leave your network, I run open-source models on your own infrastructure with Ollama or HuggingFace, behind the same API your product already calls.
ML Models in Real Systems
Wrap an existing model in a service, feed it clean data and keep it running. I have shipped credit scoring, fire detection and license plate recognition models to production.
AI Features That Misbehave
Audit an LLM feature that gives wrong answers, costs too much or times out, then fix the retrieval, the prompts or the serving layer and add the evaluation that was missing.
Value I Deliver
Weeks, Not Quarters
A narrow first feature in production fast, then iterate on real usage. The SecurityScorecard chatbot went from zero to production in 6 weeks.
Built Like a Backend
The AI layer gets the same tests, monitoring and deploy pipeline as the rest of your system, so it does not become the fragile part.
Evaluated, Not Guessed
A set of real questions with expected answers runs on every change, so you know whether a new prompt or model is better before users do.
Cost You Can See
Token usage and model cost are logged per feature, so the bill is a number on a dashboard, not a surprise at the end of the month.
Data Stays Where It Should
Hosted API or private model, chosen by what your data and your compliance rules allow, with access controls on what the model can read.
No Lock-In
Model calls go through one interface, so moving from one provider or model to another is a configuration change, not a rewrite.
Real-World Implementations
LLM Chatbot Over Security Data
Challenge
SecurityScorecard publishes continuous security ratings for 12M+ companies. The goal was a chatbot that lets people ask questions about that security score data in plain language, built from zero and shipped to production.
Solution
I led the full backend of the chatbot, from the first design to production:
- LangChain orchestration that turns a question into lookups over the security score data
- Open-source HuggingFace models behind the orchestration layer
- A FastAPI serving layer the product could call like any other internal API
- Deployment to AWS on Kubernetes, alongside the rest of the platform
Results
The chatbot went from zero to production on AWS in 6 weeks. Read the full case study
Technologies Used
Credit Decisioning Engine
Challenge
Brazil's largest credit bureau needed a new credit scoring engine, "New Score", that businesses across the country would use to make lending decisions.
Solution
- Led a team of 6 engineers and owned the technical direction, sprint planning and code quality
- Built the data processing in PySpark to feed a neural network model
Results
New Score is used by thousands of Brazilian companies. Read the full case study
Technologies Used
Computer Vision in Public Safety
Challenge
Two systems where a model had to work on live camera feeds: spotting wildfires early, and reading vehicle license plates across a city.
Solution
- Sintecsys: fire detection APIs built on TensorFlow, Keras and OpenCV
- Multiway: neural network pipelines for real-time license plate recognition across hundreds of city cameras
Results
Wildfire detection latency dropped from 5 minutes to 30 seconds. See all case studies
Technologies Used
I also wrote a hands-on guide to running Ollama securely on a remote GPU.
Technologies & Tools I Work With
LLM Frameworks
Machine Learning
Serving & APIs
Data
Infrastructure
How I Work
Pick One Use Case
Which question or task, for which users, and what a good answer looks like. A narrow feature that works beats a broad one that sometimes does.
Build the Evaluation First
Collect real examples with expected results before choosing a model. That set decides between models and prompts, not opinions.
Ship a Thin Version
An API, the retrieval, one model, logging and cost tracking, deployed to your cloud behind a flag. Real users, real data, small blast radius.
Improve on Real Usage
Read the logs, grow the evaluation set with the questions that failed, and change one thing at a time.
Hand Over
Documentation, dashboards and a walkthrough, so your team can change prompts, swap models and read the metrics without me.
How We Can Work Together
AI Feasibility Review
A short, fixed-scope look at the feature you have in mind and the data behind it. You get a written answer: whether it will work, the risks, the likely cost and a build plan.
Fixed-Scope Project
One AI feature taken from idea to production with a written scope, timeline and deliverables.
Monthly Retainer
Ongoing ownership of the AI layer: new features, model and prompt updates, evaluation and cost control, with priority response.
Not sure which fits? See how consulting engagements work or book a call.
Frequently Asked Questions
Should we use a hosted API or run our own model?
Start with whatever your data rules allow. A hosted API is the fastest way to learn whether the feature is useful. A private model makes sense when data cannot leave your network or when volume makes per-token pricing expensive. The code is built so you can switch later.
How do you stop the model from making things up?
You reduce it, you do not eliminate it. Answers are grounded in data retrieved from your systems, the model is told to say when it does not know, and an evaluation set catches regressions. For high-stakes outputs, a human reviews before anything is acted on.
How long does an AI integration take?
A focused first feature is typically a few weeks. The SecurityScorecard chatbot went from zero to production in 6 weeks. I give a written estimate after the first call, once I have seen the data and the use case.
Can you work inside our existing codebase and team?
Yes. The AI layer usually lives inside an existing backend. I follow your repo's conventions, open pull requests your team reviews and document decisions where your team will find them.
Have an AI Feature in Mind?
Tell me what it should do and what data it needs, and I will tell you whether it will work and how to ship it.