AI & Machine Learning
We build machine learning into products where it earns its place — forecasting, document understanding, search, recommendations — and we are candid when a simpler rule would do the same job.
What this looks like in practice
Most useful AI work is unglamorous: cleaning the data, defining the evaluation, and wiring the model into an existing workflow so people actually use it. We handle all three, and we instrument the result so you can tell whether it is still working six months later.
Typical stack
- Python
- PyTorch
- TensorFlow
- LangChain
- Hugging Face
- pgvector
- FastAPI
Retrieval-augmented assistants
LLM applications grounded in your own documents, with citations and guardrails, so answers can be traced back to a source.
Forecasting and prediction
Demand, churn and risk models built on your historical data, with honest accuracy reporting and clear confidence intervals.
Document intelligence
OCR and NLP pipelines that turn invoices, contracts and forms into structured, queryable records — including Bangla text.
Monitored in production
Drift detection and evaluation harnesses, because a model that was accurate at launch may not be next quarter.
AI & Machine Learning Questions
The things clients ask us most often, before the first call.
How much data do we need?
It depends on the task. Fine-tuning a classifier can work with a few thousand labelled examples; a retrieval-based assistant over your documents needs no training data at all. We assess what you have in a short feasibility review before proposing an approach.
Can the model run on our own infrastructure?
Yes. For sensitive data we deploy open-weight models inside your VPC or on-premise, so nothing leaves your network. We will walk through the accuracy and cost trade-offs against hosted APIs.
How do you prevent an AI assistant from making things up?
By grounding it in retrieved source material, requiring citations, constraining outputs to defined schemas, and adding refusal paths when confidence is low. We also build an evaluation set from real queries so quality is measured rather than assumed.
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