Custom AI, Machine Learning & Predictive Modeling Solutions
AI designed around what your business needs to know and what it needs to do next. Predictive modeling and advanced analytics turn historical data into forecasts, while autonomous agents act on those insights without waiting for a human in the loop when that is the right call, and with human oversight when it is not.
What We Propose: From predictive models to autonomous agents
Predictive modeling: demand forecasting, churn prediction, anomaly detection, and time-series forecasting to improve inventory and resource planning
RAG (Retrieval-Augmented Generation) pipelines: document ingestion, chunking strategy, embedding model selection, vector store design, and retrieval quality evaluation to ground responses in your own data
Agentic AI systems: multi-agent orchestration for workflows that span departments or systems, with explicit human-approval gates at key decision points to improve efficiency while maintaining control
Fine-tuning vs. prompt engineering vs. RAG: selected by use case based on data volatility, latency requirements, and how often the underlying facts change to balance accuracy, speed, and maintainability
MLOps: model versioning, drift detection, automated retraining triggers, and A/B evaluation against the production baseline before rollout to keep models reliable in production
Deterministic guardrails around LLM output: sensitive-data decision logic handled by rule-based systems, with the LLM used only where language understanding adds the most value to reduce risk and improve consistency
How We Decide What and How to Build
Defining Architecture
Not every AI feature needs an LLM. A demand forecast is a regression problem: gradient-boosted trees (XGBoost/LightGBM) or a classical time-series model (Prophet, ARIMA-class) can handle it. Meanwhile, LLMs work well when the input is unstructured language or the task genuinely requires reasoning over retrieved context.
RAG architecture is tuned to the actual problem
The chunking strategy, embedding model, and retrieval method are chosen based on the document structure and query patterns. For example, a legal contract and a product-support knowledge base need different retrieval designs, not the same off-the-shelf RAG template. We evaluate retrieval quality against a held-out query set before the pipeline goes anywhere near production.
Agentic workflows
Multi-agent orchestration (LangGraph, CrewAI-class frameworks) is used for workflows that span systems or require multi-step tool use. But every workflow has an explicit map of which decisions the agent can make autonomously and which require human approval before anything executes against a production system.
Deterministic logic
Where a wrong number has real consequences, such as a price, a medical dosage reference, or a compliance threshold, ensure that the calculation is deterministic code, not LLM output. The model handles language understanding and drafting, while a rule engine or calculator performs exact calculations or makes decisions.
Defining Architecture
Not every AI feature needs an LLM. A demand forecast is a regression problem: gradient-boosted trees (XGBoost/LightGBM) or a classical time-series model (Prophet, ARIMA-class) can handle it. Meanwhile, LLMs work well when the input is unstructured language or the task genuinely requires reasoning over retrieved context.
RAG architecture is tuned to the actual problem
The chunking strategy, embedding model, and retrieval method are chosen based on the document structure and query patterns. For example, a legal contract and a product-support knowledge base need different retrieval designs, not the same off-the-shelf RAG template. We evaluate retrieval quality against a held-out query set before the pipeline goes anywhere near production.
Agentic workflows
Multi-agent orchestration (LangGraph, CrewAI-class frameworks) is used for workflows that span systems or require multi-step tool use. But every workflow has an explicit map of which decisions the agent can make autonomously and which require human approval before anything executes against a production system.
Deterministic logic
Where a wrong number has real consequences, such as a price, a medical dosage reference, or a compliance threshold, ensure that the calculation is deterministic code, not LLM output. The model handles language understanding and drafting, while a rule engine or calculator performs exact calculations or makes decisions.
MLOPS
MLflow for experiment tracking and model registry, drift monitoring on production input distributions
Data Infrastructure
Airflow / Dagster for pipeline orchestration, dbt for transformation, PostgreSQL / ClickHouse for storage
Serving
FastAPI inference endpoints, vLLM / TGI for self-hosted model serving, response caching for repeated queries
Models
Claude (Sonnet/Opus), GPT-4-class, open-weight models (Llama, Mistral) self-hosted where data residency requires it
Orchestration
LangGraph, CrewAI, LlamaIndex for advanced RAG pipelines
Vector & Retrieval
PGVector, Pinecone, Weaviate, selected on cost/scale/hosting constraints, not a single default
Classical ML
XGBoost / LightGBM for tabular prediction, Prophet / statsmodels for time-series, scikit-learn for the rest
Evaluation
Ragas / DeepEval for RAG and retrieval quality, LangSmith / Braintrust for LLM tracing and evaluation, promptfoo for prompt regression testing
