Data science built for your business.

This assessment delivers all three — a complete diagnostic of your data asset (what's there, what's missing, what's trustworthy), an opportunity map prioritized by impact and feasibility, and a roadmap aligned with your leadership. The right starting point before any analytics or AI project, without committing to a larger engagement. Technically: statistical profiling (distributions, missing-value patterns MCAR/MAR/MNAR, outliers, lineage), data quality and readiness scorecard, gap analysis between current systems and model requirements, build vs. buy comparison, and KPI and analytics-maturity definition.

We build forecasting systems that give finance, supply chain, and operations real forward visibility: demand by SKU and region, cash flow, collections revenue, inventory, and consumption — with confidence intervals that quantify uncertainty so you plan with rigor, not optimism. Technically: we benchmark ARIMA, ETS, Prophet, and gradient methods (LightGBM, N-BEATS) with rolling-window out-of-time validation; we deliver probabilistic forecasts with credibility intervals and scenario analysis.

We combine three capabilities into one intelligence system over your customers and operations — predictive scoring (who, and how likely), survival analysis (when and for how long, CLV), and behavioral segmentation (which actionable groups). Technically: supervised classification and regression (gradient boosting, random forests, neural networks) with out-of-time validation, calibration, and SHAP; Kaplan-Meier and Cox models for time-to-event and competing risks; clustering (K-Means, HDBSCAN) with stability analysis and UMAP visualization.

We build recommendation systems that combine historical behavior, item similarity, and contextual signals to suggest the right thing at the right moment: from catalog recommendations to next-best-action logic in sales or collections. Technically: collaborative filtering (matrix factorization, ALS, deep learning), content-based models, hybrid systems, bandits for exploration-exploitation, and offline evaluation (precision@K, recall@K, NDCG) with temporal validation to prevent data leakage.

It's the difference between data and evidence. Technically: experiment design with power analysis and sample sizing, A/B testing with multiple-comparison corrections, sequential testing, and causal inference via difference-in-differences (DiD), synthetic control, regression discontinuity, and propensity score matching.

What once required dozens of manual inspectors becomes a system that operates 24/7 with consistency and full traceability. Technically: object detection models (YOLO, DETR), classification (EfficientNet, ViT), semantic and instance segmentation, and inference pipelines for batch or real-time operation on cloud, edge, or private infrastructure; with per-class metrics report (precision, recall, F1, mAP).

We build conversational AI for both ends: agents that retrieve and reason over contracts, manuals, and case files (RAG, with tool use and multi-step orchestration), and conversational flows that resolve FAQs, guide requests, and escalate to a human when needed — all of which can run inside your infrastructure. Technically: RAG architectures (chunking strategies, embeddings, vector stores, reranking) evaluated with RAGAS metrics (hit rate, faithfulness, answer relevancy); agentic pipelines with multi-step orchestration; and conversational flows with intent design, channel integration, and escalation logic.

We continuously monitor your key metrics, detect anomalies and significant changes, and turn them into interpretable alerts and narratives that reach the right person — before the problem becomes visible. From sales drops to pipeline failures or operational deviations. Technically: statistical change detection (mean, variance, distribution) and anomaly detection on operational time series, contribution analysis and variance decomposition, LLM-generated narratives over structured context, and routing by channel and severity (email, Slack, webhook) with a status dashboard.

We design intelligent dashboards — clear visualization, correctly defined metrics, and automatic analysis layers (trends, alerts, benchmarks, embedded forecasts) — and automate the generation and delivery of recurring reports to the right audience, in the right format, at the right time. Your team arrives at meetings with the information already prepared. Technically: analytical data model with semantic definitions, development in Metabase / Looker / Superset / Power BI / Streamlit, parameterized templates (PDF, Excel, HTML), personalized delivery scheduling by audience (email, Slack), and delivery logging.

We turn those chains of manual steps into automatic flows — and connect the systems that don't talk to each other today, so data flows on its own between your sales, operations, and finance tools. No manual exports, no double entry, no desync, with full traceability. Technically: flow orchestration with error handling, idempotent retries, and audit logging; integrations with field mapping, bi/unidirectional sync, and conflict and duplicate handling.

We extract fields, classify, detect entities, and measure sentiment and topics, over both internal documents and your brand's external conversation. Unlike conversational assistants, these pipelines process in batch and produce data, not chat answers. Technically: extraction with field schemas and validation rules, NLP for classification and entity extraction, domain-fine-tuned sentiment models, topic analysis (BERTopic / LDA), and structured output (JSON, CSV, database, API) with human review for low-confidence cases.

We integrate AI capabilities (prediction, classification, extraction, generation) directly into your existing applications, APIs, and workflows — and establish the operational infrastructure that keeps them running, monitored, and evolving. Without disrupting what already works. Technically: ML microservices (REST/gRPC) with shadow mode and feature flags for gradual rollout; experiment tracking (MLflow, Weights & Biases), versioned model registry, retraining CI/CD, data and concept drift detection (PSI), champion/challenger strategies, and model card documentation.

We build data foundations that make your team significantly more productive: automated ingestion flows, reliable and clean data layers, and AI-ready pipelines. Technically: ETL/ELT with modern tooling (dbt, Airflow, Prefect), warehouse/lakehouse design, data catalog and lineage documentation, automated quality tests (Great Expectations or dbt tests), and streaming pipelines with Kafka or equivalent as needed.

We deploy complete AI systems within your controlled infrastructure: local LLMs, on-premise computer vision, and ML pipelines that operate entirely within your security perimeter, with no third-party dependency. Technically: deployment of open-weight models (LLaMA, Mistral, Qwen) with Ollama or vLLM, quantization for efficient inference on available hardware, internal inference APIs, security architecture documentation, and model update guide.