Agentic AI Systems and Custom LLMs
Assistants that plan, use tools and complete multi-step tasks, with memory and safety layers rather than a single prompt call.
SKAD IT Solutions has merged with Hexagon IT Solutions.
AI Development
AI systems that survive contact with production
Agentic systems, custom LLM applications and machine learning models, built by SKAD IT Solutions to run reliably inside your product rather than in a notebook.
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Clear ownership
Decision rights mapped early
Reviewable delivery
Visible work and release cadence
Maintainable handoff
Documentation built into delivery
Organizations SKAD Has Worked With
What We Do
Machine Learning Models
Models built on your data, from feature engineering through training to deployment, with pipelines that survive shifting inputs.
Predictive Analytics
Forecasting for demand, churn, risk and capacity, wired into the workflows where the decisions are actually made.
Natural Language Processing
Semantic search, classification, extraction and sentiment analysis across your documents and conversations.
AI for Process Automation
Ticket triage, claims handling, document processing and fraud detection, with fallback logic for the cases the model gets wrong.
Generative AI Features
Content generation, summarisation and assistants inside your product, with prompt management, output validation and brand controls.
Why Teams Choose Us
Built for production
We optimise for uptime, latency and cost, not benchmark scores.
Grounded in your data
Retrieval and integration work comes first, because a model with no context is a novelty.
Evaluated continuously
Every deployment ships with an evaluation suite, so quality regressions are caught rather than reported by users.
Tools and Technologies
01
Deep learning
PyTorch 
TensorFlow 
Keras 02
ML libraries
XGBoost 
LightGBM 
Scikit-Learn 03
Serving and inference
TorchServe 
vLLM 
ONNX Runtime 04
Operations and monitoring
MLflow 
LangSmith 
Weights & Biases Frequently Asked Questions
Both. Most business problems are solved faster by grounding or fine-tuning an existing model. We train from scratch only when the data and the case justify it.
Retrieval grounding, structured output validation, evaluation suites run on every change, and human review on anything high-stakes.
Yes, including fully self-hosted open models where data cannot leave your environment.
Less than most people expect for retrieval-based systems, and considerably more for training a custom model. A readiness review answers this properly.
Inference and infrastructure, plus retraining and re-evaluation cycles. We model this before the build, not after.