Services · AI & Machine Learning
Models that work in production, not just notebooks.
Custom ML models, LLM integrations, and RAG pipelines built for production data at production volume. No science projects.
What we build
Every layer of the ML stack.
Custom ML Models
Classification, regression, clustering. Trained on your data, tuned to your KPIs, served behind a FastAPI endpoint.
LLM Integration & Fine-Tuning
Connect GPT, Claude, or open-source models to your workflows. Fine-tune for domain-specific accuracy when general models fall short.
RAG Pipelines
Retrieval-augmented generation that grounds LLM responses in your actual data. Vector search, chunking strategy, and hallucination reduction.
NLP & Text Analysis
Sentiment analysis, entity extraction, document classification, and summarization. One document or 50k, same interface.
Anomaly Detection
Statistical and ML-based detection of outliers in pricing, transactions, user behavior, or equipment telemetry. Real-time or batch.
Predictive Analytics
Forecast demand, churn, pricing trends, or inventory needs. Models retrained automatically as new data flows in.
Stack
How the code looks.
async def process_reviews(reviews: list[str]) -> AnalysisResult:
embeddings = model.encode(reviews, batch_size=64)
clusters = HDBSCAN(min_cluster_size=10).fit(embeddings)
sentiments = await asyncio.gather(*[
analyze_sentiment(review) for review in reviews
])
topics = extract_topics(embeddings, clusters.labels_)
anomalies = detect_outliers(sentiments, threshold=2.5)
return AnalysisResult(
topics=topics,
sentiment_distribution=Counter(sentiments),
anomalies=anomalies,
cluster_count=clusters.labels_.max() + 1
)model.encode(reviews, batch_size=64)
Sentence-transformer embeddings. Batching avoids OOM on large review sets. 64 is tuned for a 16GB GPU.
HDBSCAN(min_cluster_size=10)
Density-based clustering that discovers the number of clusters automatically. No need to pre-specify K.
detect_outliers(sentiments, threshold=2.5)
Z-score based anomaly detection on the sentiment distribution. 2.5σ catches ~1.2% of reviews for manual review.
After we process your data.
Insights without action are just reports. Our automation layer turns ML outputs into triggered workflows: alerts, updates, API calls, and business actions that happen without human intervention.