the practice
We build generative AI for institutions that cannot afford to get it wrong.
Twenty years of enterprise software sit behind this practice — web platforms, data systems, master data management, and SaaS products built from nothing. The last years have gone into production GenAI for multinational banks: regulated, audited, and used every day by people who need the answer to be right.
We work end to end. One team carries a system from the first conversation to the day it runs in production — the interface people use, the services behind it, and the models underneath.
what we build
AI platforms
On-premise and cloud. GPU server setup, model hosting, and a single place for teams to use many models safely.
Retrieval and knowledge
RAG pipelines, vector databases and knowledge graphs, built over requirements, specifications, policies and regulation.
Prompts and orchestration
Prompt engineering, multi-step workflows, and validation that checks the output before anyone relies on it.
Guardrails and moderation
Controls on what goes into a model and what comes out. Built for the scrutiny of a regulated industry.
Cost and speed
Token cost optimisation and inference tuning, so a system that works in a pilot can afford to run at scale.
Fine-tuning
LoRA adaptation, and feedback loops (RLHF) that let real users improve the model as they work.
The application itself
Full-stack web products — ReactJS, Node.js, FastAPI and Flask — so the AI reaches people as a tool they actually want to use.
Data foundations
Data lineage, master data management, and entity resolution. A model is only as good as the data it stands on.
selected work
All of it delivered for multinational banks. Names are kept private, as our clients expect.
An AI data-lineage platform
Business users ask how a regulatory number was made, and see the answer as an interactive graph. An intelligent parser reads PL/SQL and Hive scripts. A RAG pipeline, grounded in requirements and regulatory text, turns the code into plain business language. No one needs to read SQL.
An enterprise GenAI portal
One internal platform for the whole organisation. Self-serve prompt engineering and model management across several LLMs, with guardrails, moderation and token cost controls built in.
A knowledge assistant over requirements and regulation
Staff ask questions in plain language across requirement documents, functional specifications and regulatory guidelines. User feedback flows back into the system, so it improves with use.
Legacy code conversion for credit risk
A GenAI tool that migrates legacy SAS code to Python. Prompt strategies and validation checks prove the new code behaves exactly like the old — which matters when the code computes risk.
A regulatory policy assistant
A conversational app over complex regulatory policy. Many document sources are joined into one knowledge graph, so an answer can be traced back to where it came from.
Customer master data on a lakehouse
A single source of truth for customer data. AI-assisted match-and-merge across bronze, silver and gold layers, with survivorship rules, quality monitoring and governance.
Before GenAI — master data for banks in Europe and beyond
Over a decade leading customer data platforms for multinational banks and global enterprises: matching and duplicate resolution, golden records, data stewardship, and the integrations around them.
how we work
We say how we will know it works before we build it — and we show you the proof, not a promise.
A model's answer is checked, not trusted. Where a person must decide, a person decides.
We build for the people who will use it, not for the demo. A tool nobody opens has failed, however clever it is.
We stay honest about limits — what the model cannot do, what it costs to run, and when AI is the wrong answer.
why netineti does this
We know what these machines are. So we build them carefully.
A machine receives, finds and outputs. It does not understand, and it does not care. Holding that clearly is what lets us build AI that is useful and safe — and it is the same clarity behind everything else netineti makes.
If you are bringing AI into a place
where being wrong is expensive, write to us.