AI that does real work, not demos.
Custom machine learning, LLM apps and autonomous agents that plug into your data and systems, and keep delivering in production.
What do AI and machine learning services include?
AI and machine learning services cover custom models, large language model applications, retrieval-augmented generation (RAG) assistants, AI agents and intelligent process automation. GloberixAI takes AI from a proof of value on your own data to a secure, monitored production system.
- Best for
- Teams with repetitive work or rich data to learn from
- Common builds
- RAG chatbots, document AI, forecasting, agents
- First phase
- Proof of value on your real data in a few weeks
- Data policy
- Your data is not used to train public models
Last updated
AI that fits your business, not the other way round.
From predictive models to autonomous agents, we build the pieces that remove manual work and sharpen decisions.
Custom machine learning
Predictive analytics, clustering and deep learning models tuned to your own business data.
- Forecasting and demand planning
- Churn, risk and lead scoring
- Computer vision and language models
AI agents and RAG chatbots
Domain-trained assistants that understand context, call your tools and resolve multi-step tasks.
- Answers grounded in your documents
- Tool and API calling
- Hand-off to people when unsure
Intelligent process automation
Document parsing, computer vision and API triggers that remove manual work from start to finish.
- Invoice and contract extraction
- Approval and routing workflows
- ERP and CRM integration
Production AI integration
Bridge the gap between a prototype and a high-throughput product with robust, secure services.
- Model APIs, caching and versioning
- Monitoring, drift and cost control
- Security and access control
From raw data to a model that keeps earning its place.
What we do, and what you get, at each stage.
Data
We audit your sources, fix quality gaps and build the pipelines your models will learn from. Good data decides the ceiling of every AI project.
Model
We choose the simplest approach that works: prompting, retrieval over your documents, fine-tuning or classic machine learning, and benchmark each against a baseline.
Evaluate
Models are tested on real examples with automatic checks and human review before anything goes live, including edge cases and adversarial prompts.
Deploy
We serve models behind secure APIs with caching, versioning, rate limits and rollback, and connect them to your CRM, ERP or product.
Monitor
We track answer quality, drift, latency and cost, and retrain or re-tune when your data changes, so results stay good after launch.
Where AI pays for itself first.
Five places we see the clearest return, and the work behind each.
Customer support
AI support agents answer tier-1 questions around the clock, draft replies for your team and route complex tickets to the right person. Sentiment analysis shows where customers struggle.
- Resolve common questions instantly
- Route and prioritize tickets
- Spot sentiment shifts early
Finance and operations
Document AI reads invoices and contracts, cross-checks them against your ERP and routes approvals, so your team handles exceptions instead of data entry.
- Invoice and contract extraction
- ERP cross-checks
- Automatic approval routing
Sales and marketing
Predictive lead scoring, audience segmentation and personalized journeys help your team focus on the people most likely to buy.
- Lead scoring
- Audience segmentation
- Personalized content
Security and risk
Behavioral models learn what normal looks like and flag unusual activity early, so your team can respond before damage is done.
- Anomaly detection
- Automated alerts
- Isolation playbooks
Forecasting and analytics
Self-updating dashboards combine live data with forecasts and anomaly alerts, so planning stops relying on last month’s spreadsheet.
- Demand forecasting
- Anomaly alerts
- Executive dashboards
The toolkit behind reliable AI.
- Models
- OpenAI, Anthropic, Llama, Mistral, Hugging Face
- Frameworks
- PyTorch, scikit-learn, LangChain, LlamaIndex
- Data
- PostgreSQL, pgvector, Pinecone, Airflow, dbt
- Serving
- FastAPI, Docker, Kubernetes, AWS, Google Cloud
- Quality
- MLflow, Weights & Biases, OpenTelemetry, Eval suites
- Interfaces
- Next.js, React, Slack and Teams bots, WhatsApp
A four-step path from idea to live AI.
Short cycles, working software and decisions backed by evidence.
Discover
We score use cases by payoff and feasibility, then agree how success will be measured before any build starts.
Prototype
A working proof on your real data in a few weeks, so you can judge the result with evidence rather than slides.
Productionize
We harden the prototype with APIs, security, evaluations, monitoring and the integrations your team needs.
Improve
We retrain, tune and extend as usage grows and data changes, on a monthly plan that keeps results improving.
An AI partner that tells you the truth.
Whatever we build, the way we work stays the same.
Business outcome first
We start with the result you want and choose the simplest AI that delivers it, which is often less than you expect.
Honest about limits
We tell you where AI will help, where plain rules will do, and where a person should stay in the loop.
Evaluation built in
Every model is tested on real examples before launch and monitored after it, so quality is measured, not assumed.
Secure by design
Access controls, encryption and clear data boundaries are part of the architecture, and your data stays yours.
Works with your stack
We integrate with your CRM, ERP, data warehouse and internal tools instead of asking you to replace them.
Knowledge transfer
Documentation and training mean your team understands what was built and can extend it.
AI development services for businesses that want results, not experiments.
GloberixAI is an AI development company that builds custom machine learning models, large language model applications and intelligent automation. We cover the full journey: data engineering, model development, evaluation, deployment and long-term monitoring, so you work with one accountable team from the first workshop to a system that is live and measured.
Our AI agents and retrieval-augmented generation (RAG) assistants answer questions from your own documents, call your tools and APIs, and hand off to people when they are unsure. Our process automation reads invoices, contracts and forms with document AI, checks them against your systems and routes work for approval. Our predictive models forecast demand, score leads, flag risk and detect anomalies in data you already collect.
Because production is where most AI projects stall, we engineer for it from day one: secure APIs, caching, versioning, cost controls, drift monitoring and clear rollback plans. Tell us the process you would most like to improve and we will reply with a practical plan, a realistic timeline and a quote.
AI development, answered.
Do you build custom models or only use APIs from large providers?
Both. We choose the simplest approach that meets your quality bar: prompting a hosted model, retrieval over your documents, fine-tuning an open model, or training a classic machine learning model. We explain the trade-offs in cost, privacy and accuracy before you commit.
How long does an AI project take?
A focused proof of value on your real data typically takes a few weeks. Production rollout, integrations and monitoring add time depending on scope. We agree a dated plan after discovery.
Will my data be used to train public models?
No. We configure providers so your data is not used to train public models, and we can deploy models inside your own cloud account when needed. Access is limited by role and logged.
How do you reduce hallucinations?
We ground answers in your documents with retrieval, require citations, add validation rules and guardrails, and test against a set of real questions. When confidence is low, the system says so or hands off to a person.
Can AI work with our existing software?
Yes. We build connectors and APIs for CRMs, ERPs, help desks, data warehouses and internal tools, so AI works inside the systems your team already uses.
How do we measure return on investment?
We agree success metrics up front, such as hours saved, tickets resolved, conversion lift or forecast accuracy, and report them against a baseline measured before launch.
What does AI development cost?
Cost depends on data readiness, scope and integrations. After a short discovery call we provide a fixed quote for the first phase, so you can judge the value with evidence before you scale.
Related services and guides
Have a process worth automating?
Tell us where your team loses the most time. We will reply with a practical AI plan and a quote.