AI & Machine Learning
Practical AI automation — document parsing, LLMs, and computer vision for B2B.
Practical, high-value AI solutions designed to automate manual workflows, classify complex documents, and extract actionable insights from business data without the hype or astronomical infrastructure costs.
What breaks without this
Expensive manual data entry, slow document review processes, and high error rates in classification and quality control workflows.
Staff spend hours per day manually entering data from invoices, reports, and PDFs
Document classification errors in quality control cause costly downstream rework
Buying off-the-shelf AI tools means your proprietary data trains their models
There's no clear way to measure or monitor model accuracy after deployment
How we deliver this
Feasibility & data assessment
We audit your data volume, quality, and labelling state. We define success metrics (accuracy, recall, latency) before writing code, and tell you upfront if AI won't outperform a good rule engine.
Model selection & prototyping
We evaluate whether fine-tuning, RAG, or training from scratch fits the problem. A working prototype with real data validates accuracy before committing to production build.
Production pipeline engineering
Training, evaluation, and inference pipelines are built with MLflow experiment tracking, automated retraining triggers, and data versioning.
Drift monitoring & maintenance
A production monitoring dashboard tracks accuracy, confidence distributions, and latency. Alerts fire when model performance degrades below agreed thresholds.
What you gain
Automated parsing of unstructured documents (invoices, specification sheets, lab results) with high accuracy.
Predictive analytics and anomaly detection models for operational and financial data.
Custom-tuned Large Language Models (LLMs) with RAG (Retrieval-Augmented Generation) for proprietary data.
Computer vision models for automated quality control and visual inspections.
Production-grade model monitoring pipelines to track and prevent model drift over time.
Concrete deliverables
Every engagement ends with tangible artefacts you own and can hand to any team.
AI feasibility study, data availability report, and ROI analysis.
Trained machine learning models and weight files.
Model training, evaluation, and feature engineering pipelines.
Secure inference REST/gRPC APIs and integration code.
Evaluation metrics and model drift monitoring dashboards.
Technologies we use
We select tools based on your requirements — not on what we happen to know best.
Frequently asked questions
No. By using transfer learning and fine-tuning pre-trained foundation models (such as GPT-4, Llama 3, or ResNet), we can build highly accurate custom models with just a few hundred labeled examples from your specific domain.
We run open-source models (like Llama or Mistral) on dedicated, private cloud infrastructure (AWS SageMaker or Azure ML) or use enterprise APIs with strict data protection terms, ensuring your proprietary data is never used for training external models.
We establish a rigorous validation framework during the discovery phase. We split your historical data into training, validation, and test sets, and define strict metrics (like precision, recall, and F1-score) that the model must pass before production deployment.
Let's Discuss Your AI/ML Project
Schedule a free 30-minute consultation with a senior engineer. No sales pitch — just an honest assessment of your situation.