Inventh AI development services take AI from use case to production: identifying value, preparing data, building or adapting models, integrating them with business systems and monitoring them after launch. Work includes generative AI assistants, AI agents, retrieval augmented generation and predictive machine learning.
Our approach
AI that reaches production, not just a pilot.
Most AI initiatives stall between a promising demo and a dependable system. We close that gap by starting with the business case, building on your real data and engineering every model to run reliably inside your operations.
Business case first
Every engagement starts with a measurable outcome: hours saved, revenue gained or errors reduced. If AI is not the right answer for a problem, we will tell you.
Built on your data
We assess data quality and access early, then design the pipelines, retrieval and governance that let models work with the information your business actually has.
Engineered for production
Monitoring, security, cost control and human oversight are part of the build from day one, so what we ship keeps performing long after launch.
What we build
Our AI and machine learning services
Six capabilities, delivered individually or as one program from strategy to support.
AI Strategy and Consulting
Identify where AI will pay back, assess data readiness and build a prioritized roadmap with clear costs, risks and success measures.
Generative AI Development
Custom applications built on large language models: knowledge assistants, document processing, content generation and retrieval augmented generation (RAG) over your own data.
AI Agents and Workflow Automation
Agents that complete multistep tasks across your systems, from triaging requests to updating records, with approval steps wherever people need to stay in control.
Machine Learning Model Development
Custom models for forecasting, classification, recommendation and anomaly detection, trained, validated and tuned on your data.
Predictive Analytics and Business Intelligence
Dashboards and predictive models that turn operational data into decisions, connected to Power BI and your data warehouse.
AI Integration and MLOps
Connect AI to your ERP, CRM and custom applications, with deployment pipelines, monitoring and retraining that keep models accurate over time.
Where AI delivers
AI use cases by industry
Healthcare and medical supply
Demand forecasting, automated order processing, product data enrichment and compliance document review.
Financial services and accounting
Invoice and receipt extraction, reconciliation support, anomaly detection and client reporting assistants.
Retail and ecommerce
Product recommendations, catalog enrichment, search relevance and customer service agents.
Logistics and operations
Inventory and route optimization, shipment exception handling and predictive maintenance.
How we work
From idea to production in four stages
Discover
Workshops with your team to define the problem, success measures and data sources. You receive a feasibility assessment and a costed roadmap.
Prove
A focused proof of concept on your real data that tests accuracy, cost and user fit before any larger investment.
Build
Production engineering: integrations, security, interface, testing and documentation, delivered in short, visible iterations.
Operate
Monitoring, retraining and support after launch, reported against the outcomes agreed in Discover.
Technology
Platforms and tools we work with
We choose models and infrastructure based on your requirements for accuracy, cost, privacy and hosting, not vendor preference.
Models
Frameworks
Data
Cloud
Security and governance
Responsible AI, built in
Your data stays yours
We deploy through enterprise model agreements that exclude your data from public model training, and we sign NDAs as standard.
People stay in control
Consequential decisions keep a human approval step, with a clear audit trail of what the system did and why.
Measured accuracy
Every model ships with evaluation benchmarks and ongoing monitoring for drift, errors and cost.
Privacy by design
Access controls, encryption and data minimization aligned with GDPR, and with sector rules such as HIPAA where they apply.
Engagement models
Ways to work with us
Fixed scope project
Defined deliverables, timeline and price. Best for proofs of concept and well scoped builds.
Dedicated AI team
Engineers, data scientists and a delivery lead working as an extension of your team, scaled to your roadmap.
Advisory retainer
Ongoing access to AI architects for strategy, design reviews and vendor evaluation.
FAQ
Frequently asked questions
What do AI and machine learning development services include?
They cover the full path from strategy to production: identifying use cases, preparing data, building or adapting models, integrating them with your systems and monitoring them after launch. Inventh can deliver every stage or support your internal team where needed.
How much does AI development cost?
Cost depends on scope, data readiness and integration needs. A proof of concept is usually a smaller, fixed price engagement, while production systems are priced after discovery. You receive a written estimate before committing to a build.
How long does it take to build an AI solution?
A focused proof of concept typically takes a few weeks. A production system takes longer, depending on integrations, data preparation and compliance requirements. Discovery gives you a timeline before the build starts.
Do we need a lot of data to use AI?
Not always. Generative AI and pretrained models can deliver value with modest amounts of your own data, while custom machine learning models need enough history to learn from. Our discovery stage assesses what you have and what is realistic.
Will our data be used to train public AI models?
No. We use enterprise agreements and deployment options, including private cloud hosting, that keep your data out of public model training.
Can you integrate AI into our existing software?
Yes. Most of our AI work connects to existing ERP, CRM, ecommerce and custom applications through APIs, so your team keeps working in the tools they already know.
Have an AI use case in mind?
Tell us what you want to improve. We will come back with an honest view of feasibility, approach and cost.