Engineering built around the outcome

Generative AI

We build generative AI products around trusted data, clear boundaries, and repeatable evaluation—not a thin interface over a model API.

• Retrieval-Augmented Generation (RAG) systems: Ground language models in your documents, knowledge bases, tickets, or other approved sources. We design ingestion, chunking, search, reranking, citations, document updates, retrieval evaluation, and safe fallback behavior as one system.
• LLM fine-tuning and deployment: When prompting alone is not enough, we evaluate fine-tuning or self-hosted inference against quality, privacy, latency, and cost requirements. The deployment design includes model serving, capacity planning, versioning, and rollback.
• Semantic response caching: For suitable workloads, meaning-aware caching can reuse approved answers for similar requests, reducing latency and model spend while preserving freshness and access controls.

Agentic AI

We design tool-using AI workflows for tasks that genuinely require several steps: collecting evidence, applying business rules, calling approved systems, and preparing an action or response. Explicit orchestration, permission boundaries, audit trails, retries, and human approval points keep the workflow understandable and controllable.

Traditional Machine Learning

Many high-value prediction and decision problems are better served by established machine-learning methods than by a language model. We match the method to the data and the decision it needs to support.

• Supervised learning: Classification, regression, and forecasting for outcomes such as churn risk, fraud signals, demand, or operational performance when reliable historical labels exist.
• Unsupervised learning: Clustering, anomaly detection, and pattern discovery when the goal is to uncover useful structure without predefined labels.

When a simpler model is more accurate, explainable, and economical, that is the recommendation we make.

Prompt Engineering

Reliable model behavior requires more than a clever instruction. We design prompts, examples, context, tool contracts, and structured outputs as versioned system components, then evaluate them against representative and adversarial cases before release.

Natural Language Processing beyond chatbots

We build systems for classification, entity recognition, summarization, language detection, and structured extraction from unstructured text. The implementation may use a compact language model, an encoder model, or classical NLP—whichever provides the best quality, speed, and operating cost.

Speech recognition (ASR)

We develop speech-to-text workflows for specific languages, accents, acoustic conditions, and specialist vocabulary. Work can include data preparation, model adaptation, real-time transcription, confidence handling, and evaluation against domain-representative audio.

Computer vision and vision-language models

We apply image classification, object detection, visual similarity, and multimodal models to workflows such as quality inspection, content verification, visual search, and image-to-text extraction—supported by task-specific datasets and evaluation.

MLOps and model deployment infrastructure

We operationalize models with reproducible serving, experiment and model versioning, evaluation gates, drift and quality monitoring, rollback, and cost-aware scaling. Model behavior is tested alongside the surrounding software so changes can be released with evidence rather than intuition.