AI & ML
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Custom AI Built on Your Data, Not a Vendor’s Dataset
Off-the-shelf AI tools return generic outputs. A model trained on your data, solving your specific problem, gives you something competitors can’t replicate from the same SaaS subscription. CimpleO builds LLM integrations, AI chatbots, and custom ML models — scoped to your problem, deployed on your infrastructure or in the cloud.
Predictive Analytics & Forecasting
Replace gut feel with models trained on your historical data. We build forecasting systems for demand planning, churn prediction, revenue modelling, and risk scoring — integrated into your existing dashboards, not a standalone tool your team has to learn separately.
NLP & Conversational AI
We build language models fine-tuned on your domain: document classification, information extraction, contract review, customer support automation, and RAG systems that let your teams query internal knowledge bases in plain language. Model choice — GPT, Claude, LLaMA, or other open-source — depends on your data privacy requirements.
Computer Vision
Automated visual inspection, object detection, document OCR, defect identification — we train and deploy vision models that handle tasks which currently require human eyes. Deployable on cloud infrastructure or on edge hardware for real-time processing in the field or on the production line.
AI Integration into Existing Systems
You don’t need to rebuild your stack to add AI capability. We integrate ML models into your existing applications via API — adding recommendation layers, anomaly detection, or intelligent routing without disrupting what already works.
What We Build
- LLM integrations — connect GPT-4, Claude, or Llama to existing products via API; includes prompt engineering and context management
- RAG systems — retrieval-augmented generation for knowledge bases, document Q&A, and internal search; the model answers from your data, not from training data
- AI chatbots — customer support bots, lead qualification assistants, internal tools that answer questions from your docs and CRM
- Custom ML models — fine-tuning, classification, recommendation systems, anomaly detection; trained on your data, not public datasets
- AI inference infrastructure — deploy models on GPU (RTX 4090) or CPU (Xeon), benchmark latency and cost before committing to cloud spend
See our LLM inference benchmark — 8 models tested on Xeon CPU vs RTX 4090.
Why Companies Build AI with CimpleO
- Custom models, not wrappers — we train on your data, not public datasets anyone can access
- End-to-end delivery — data preparation, model development, deployment, and monitoring in one engagement
- Explainable outputs — models your business stakeholders can understand and trust
- Privacy-first options — on-premises or private cloud deployment for sensitive data environments
Ready to scope what AI can do for your specific problem? Start with a discovery call — we’ll tell you what’s realistic and what it would take.
Frequently Asked Questions
How much does custom AI/ML development cost?
A focused ML model (single prediction task, clean data): $15,000–$40,000. A full AI feature with data pipeline, model training, deployment infrastructure, and monitoring: $40,000–$120,000. RAG-based LLM integrations: $20,000–$60,000 depending on knowledge base complexity. We scope the data situation first — bad data doubles the timeline.
Do you handle data preparation, or do we need clean data?
We handle data preparation as part of the engagement — collection, cleaning, labelling strategy, and feature engineering. If your data is genuinely too sparse or too noisy to produce a useful model, we'll tell you before taking the project, not after billing 3 months of work.
Should we use GPT-4/Claude API or train our own model?
For most business applications: use the API. Foundation models via API are faster to ship, cheaper to maintain, and surprisingly capable with good prompting and RAG architecture. Fine-tuning your own model makes sense when your domain is highly specialised, data privacy prevents sending data to an external API, or inference cost at scale makes API pricing unacceptable.
How do you prevent hallucinations in LLM-based features?
Grounding. RAG (Retrieval-Augmented Generation) connects the model to your verified knowledge base so it answers from your data, not from training data. Structured output schemas constrain what the model can say. Confidence thresholds and human-in-the-loop for high-stakes decisions. There's no magic — the answer is architecture and testing.
Can you integrate AI into our existing product without rebuilding it?
Yes. Most AI integrations are additive — a new API endpoint that the existing product calls, a background processing pipeline that enriches existing data, or a new UI component. We design integrations to minimise disruption to your current system.
Do you offer on-premises AI deployment for data-sensitive environments?
Yes. For environments where data can't leave your infrastructure, we deploy on open-source models (LLaMA 3, Mistral, Qwen) on your own hardware or private cloud. We benchmark model quality against your specific tasks before committing to an approach.