AI and machine learning development that reaches production.
DibHub builds AI and machine learning solutions for real business workflows: LLM and RAG applications, natural language processing, computer vision, predictive analytics and recommendation systems. Every model is trained, evaluated, deployed and monitored in production not handed over as a notebook.
Machine learning solutions built for real business decisions.
Every AI system we build starts from a decision your business already makes by hand and ends with a measurable baseline it has to beat in production.
Natural Language Processing
LLM and RAG applications for document classification, data extraction, semantic search and support automation over the language your business already runs on: contracts, tickets, clinical notes and claims.
Computer Vision
Computer vision models for object detection, image classification and quality inspection on images and video running at the edge or in batch depending on where your data sits.
Predictive Analytics
Demand forecasting, churn prediction, risk scoring and fraud detection models wired into the dashboards and alerts your teams already watch, with confidence shown alongside every number.
Recommendation Engines
Recommendation systems for product catalogues, content feeds and next best action, evaluated against real conversion and retention rather than offline accuracy alone.
We start with the decision, not the model.
As an AI development company, we open every engagement on the data you already hold and the decision you want changed. We audit the pipeline first (where the data lives, how clean it is, how often it moves) because that determines what a machine learning model can honestly deliver.
From there our engineers own the full ML lifecycle: feature pipelines, training, evaluation against a documented baseline, deployment for real time inference or large-scale batch, and drift monitoring once it is live. Every model ships explainable, versioned and retrainable as your data grows.
Accelerate Growth
AI driven personalisation and demand forecasting that lift conversion and retention on the traffic you already have.
Boost Efficiency
Manual review, ticket triage and data entry automated where the model is confident and escalated to a person where it is not.
Reduce Costs
Right sized inference and training budgets, so cloud compute spend tracks the value each model returns.
Enhance Security
Training data governed, access controlled and inference deployed inside your own cloud boundary, so sensitive data never leaves your environment.
Collaboration
Your analysts and our ML engineers work in the same repository, notebooks and review cycle from day one.
Networking
Models integrated with the databases, APIs and event streams already carrying data between your teams.
Global Translations
Multilingual language models tuned for the markets you sell in, not only English first datasets.
In house Techs
A permanent in house ML team, not subcontractors, so the engineers who trained the model stay on through handover.
Book a free software consultation.
Thirty minutes with a senior engineer, not a sales pitch. Bring the problem you are trying to solve and leave with a clear view of scope, technical approach, timeline and cost for your custom software project.