We build and ship AI features — document processing, LLM-powered chat, predictive analytics, and computer vision — with the evaluation and monitoring that keeps them reliable once real users depend on them.
We build AI features that solve a specific, measurable problem in your product or operations — document processing, conversational support, prediction, or vision — and we own the evaluation and monitoring that keeps them reliable after launch.
Extraction and classification pipelines that turn invoices, forms, and contracts into structured, usable data.
Conversational assistants built on LLM APIs, grounded in your own data via retrieval, with guardrails against hallucinated answers.
Models trained on your historical data to forecast demand, churn, or risk, delivered as an API or dashboard your team can act on.
Image and video pipelines for detection, classification, or quality inspection, deployed where the data actually lives.
AI projects fail most often from unclear success criteria and skipped evaluation. We build both into the process from day one.
We define exactly what 'working' means in numbers — accuracy, latency, cost per request — before any model work starts.
We audit what data actually exists and its quality, because most AI project risk lives here, not in model selection.
We choose between a hosted LLM API, a fine-tuned model, or a custom-trained model based on cost, accuracy, and data sensitivity.
Outputs are tested against a labeled evaluation set, with fallback and human-review paths for low-confidence cases.
The feature ships behind monitoring for accuracy drift and cost, so degradation is caught before users notice it.
Working software with visibility into how well the model is actually performing — not a one-off notebook.
Retrieval-grounded chat and generation features wired into your product.
Trained and evaluated models for prediction and classification tasks.
Image/video inference deployed close to where the data is captured.
Drift monitoring and a retraining path as real-world data evolves.
Answers to what prospective clients ask us most before starting a project.
Not always. Some use cases, like LLM-based chat or document extraction, work well against hosted models with light fine-tuning or retrieval over your existing documents. Custom prediction models do need historical data, and we'll tell you upfront if what you have is enough.
We ground responses in your own data through retrieval rather than relying on the model's general knowledge, constrain the output format, and add confidence thresholds that route uncertain answers to a human instead of guessing.
Yes — this is most of our AI work. We integrate document processing, chat, search, or prediction into an existing web or mobile app via API, rather than building a separate standalone AI product.
We define accuracy, latency, and cost targets before launch and monitor against them in production, with alerts if performance drifts — the same rigor we'd apply to any other backend service.
Tell us the problem you want AI to solve and what data you have — we'll tell you honestly whether it's ready to build and what it would take.
Features shipped with monitoring, not left as a one-off demo.