LLM applications & RAG
Retrieval-augmented assistants over your own documents, with chunking and retrieval tuned for your corpus, guardrails, citation of sources, and an eval set so a prompt change cannot silently make things worse.
Service
AI, ML & Data Science
Models that make it past the notebook.
The gap in most AI projects is not the model, it is everything around it: where the data comes from, how predictions reach a user, what happens when quality drifts, and how anyone knows it is still working. We build the whole path, and we are candid when a rules engine would beat a model.
What you get out of it
- A baseline you can beat, measured before any modelling starts
- Evaluation harness so quality is a number, not an opinion
- Serving infrastructure with latency and cost budgets
- Monitoring for drift, failure modes and spend
Capabilities
What this covers.
Predictive & forecasting models
Churn, demand, credit risk, maintenance windows. Gradient boosting and time-series methods, validated against a business baseline rather than a leaderboard metric.
Computer vision
Detection, classification, OCR and quality inspection — trained on your data, deployed to a server or to the edge device it has to run on.
Data engineering
Ingestion, cleaning, warehousing and the transformation layer underneath. Most analytics problems are pipeline problems wearing a model costume.
Analytics & decision dashboards
Metric definitions agreed with the people who use them, then dashboards that answer a specific question instead of showing every chart at once.
MLOps
Experiment tracking, model registry, reproducible training, automated retraining and rollback. The unglamorous work that decides whether year two goes well.
Technology
What we build it with.
Proven tools for the load-bearing parts, newer ones where the upside is real. Nobody should be debugging our curiosity at 2am.
- Python
- PyTorch
- scikit-learn
- LangChain
- Hugging Face
- OpenAI / Claude APIs
- pgvector
- Airflow
- dbt
- MLflow
- FastAPI
Deliverables
What lands in your hands.
- Data audit and feasibility note before commitment
- Reproducible training pipeline
- Evaluation report with honest error analysis
- Deployed inference API or batch job
- Monitoring dashboards and alerting
- Model card and handover documentation
Questions
About ai, ml & data.
We do not have much data. Can you still help?
Often yes. Many useful systems start with pretrained models, retrieval over existing documents, or a small labelled set built during the project. We run a short feasibility check first and will tell you plainly if the honest answer is that data collection needs to come before modelling.
Will our data be used to train public models?
No. We work inside your cloud account or an isolated environment, and where a third-party API is used we configure zero-retention settings and document exactly what leaves your perimeter. That document is part of the deliverable.
How do you measure whether the AI is actually working?
We agree an evaluation set and a target metric before building, and we compare against a simple baseline — often a rule or a heuristic. If the model cannot beat the baseline by a margin worth the complexity, we say so and recommend the simpler system.
Other things we do
Web Development
Sites and web apps that load fast, rank well and convert.
Mobile App Development
Apps people keep on the first screen.
IoT & Embedded Systems
From the sensor to the dashboard, one team.
Cloud & DevOps
Infrastructure you can reason about at 3am.
UI/UX Design
Design that survives engineering.
Next step
Need ai, ml & data?
Start with a call.
A thirty-minute call, then a written scope within 48 hours covering what we would build, what it costs and where the risk sits. That document is yours whether or not you hire us.
No obligation · Reply within one working day · ceo@hackedinsolutions.co.in