Neucent Technologies

AI solutions

Practical AI, built into the software you run

We turn language models, computer vision and machine learning into features your team and customers use every day.

What we build

Six ways AI earns its place in a product

Chatbots and voicebots

Support and internal assistants, by text or voice, that answer from your own policies and data and pass the conversation to a person when they are unsure.

Knowledge search

Ask a question in plain language and get an answer drawn from your documents, wikis and tickets, with the sources shown.

Document and vision AI

Read invoices, forms and IDs, pull out the fields you need, and flag images that need a human look.

Workflow automation

Agents that carry out multi-step tasks across your tools, with approval checkpoints where the stakes are high.

Predictive models

Forecasting, scoring and recommendation models trained on your history and tested before they influence a decision.

AI inside your product

Add summaries, drafting, classification or smart search to a web or mobile app you already run.

Our approach

From idea to a feature people trust

AI projects go wrong when they skip evaluation. Ours are organised around it.

  1. 01

    Pick the right problem

    We start with the task that costs you the most time or money and check whether AI is the right tool for it at all.

  2. 02

    Test on your data

    Before building anything large, we assemble real examples and measure how well the approach works on them.

  3. 03

    Build a working pilot

    A small, usable version goes to a real group of users, so feedback comes from practice and not from slides.

  4. 04

    Ship with guardrails

    Monitoring, fallbacks, human review and spend limits go live together with the feature.

  5. 05

    Measure and improve

    We track quality and cost after launch and tune prompts, data and models as usage grows.

Responsible by design

Safeguards are part of the build

Reliable AI is mostly careful engineering around the model. These practices apply to every project.

Your data, your rules

We choose model providers and hosting, including your own cloud account, to match your data and compliance requirements.

Measured, not guessed

Every AI feature has test cases and a quality target, so "it seems good" is never the acceptance criterion.

People stay in the loop

Sensitive actions go through review. The system knows when to say "I am not sure" and hand off.

Costs you can predict

We track latency and per-request cost from the first prototype and design around a budget.

AI toolkit

What we work with

  • Chatbots
  • Voicebots
  • LLM APIs
  • Retrieval (RAG)
  • PyTorch
  • OCR
  • Python
  • FastAPI
  • PostgreSQL
  • Docker

Not sure where AI fits?

Describe the task that slows your team down. We will tell you honestly whether AI helps and what a first pilot would look like.

Start a project