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Quick answer
UnlockLive IT builds production AI and machine learning systems for North American businesses — custom-trained predictive models, computer vision, NLP classifiers, recommendation systems, foundation-model fine-tuning (LoRA / QLoRA), and full MLOps. Our Toronto-managed team works in PyTorch + Hugging Face on AWS SageMaker, Vertex AI, Modal, or your preferred cloud — with versioned datasets, reproducible training pipelines, automated evaluation suites, and drift monitoring in production. If you need LLM-based agents specifically, see our AI Agent Development page; for retrieval-augmented systems, see Custom RAG Development.
What we build
Predictive analytics & forecasting:Demand forecasting, churn prediction, lead scoring, fraud detection, dynamic pricing, propensity models. Gradient boosting (XGBoost, LightGBM, CatBoost) by default; deep learning when the problem demands it. Deployed as production APIs with monitoring.
Computer vision:Object detection (YOLOv8/v10, RT-DETR), image classification (ConvNeXt, ViT), segmentation (SAM 2, Mask2Former), OCR (PaddleOCR, TrOCR), pose estimation, defect detection, face recognition, video analytics. Edge deployment with Core ML, TFLite, ONNX Runtime.
NLP classifiers & extraction:Domain-specific text classification, named-entity recognition, structured information extraction, sentiment, topic modeling. Often built on smaller fine-tuned models (DistilBERT, ModernBERT, Qwen) for speed and cost — not always foundation-LLM territory.
Recommendation systems:Collaborative filtering, matrix factorization, two-tower models, sequential recommenders (BERT4Rec, SASRec), hybrid retrieval-and-rerank pipelines for content, e-commerce, and learning platforms.
Foundation model fine-tuning:LoRA, QLoRA, and full fine-tuning of Llama 3.3, Qwen, Mistral, Gemma, and DeepSeek for domain-specific tasks where in-context learning isn't enough. Reinforcement learning from human or AI feedback (RLHF, DPO, KTO) when warranted.
Speech, audio & multi-modal:Speech-to-text (Whisper, AssemblyAI, Deepgram), TTS (ElevenLabs, OpenAI), speaker diarization, audio classification (PANNs, AudioSet), and multi-modal models combining vision, audio, and text.
Our AI & ML stack
Our ML development process
- Problem framing & feasibility (1-2 weeks): Pin down the success metric in business terms. Audit available data and labels. Run a quick baseline (often a non-ML heuristic) to set a floor. Decide ML vs not-ML honestly — many problems are better solved without ML.
- Data engineering (1-4 weeks): Source ingestion, cleaning, deduplication, label collection or label cleanup, train/val/test splits, and a versioned dataset under DVC or Hugging Face Datasets. Most ML failures are actually dataset failures.
- Modeling experiments (2-6 weeks): Run 3-5 candidate model architectures and training recipes. Track every run in MLflow / W&B. Pick a winner against the eval set, not vibes. Document what didn't work and why.
- Productionization (2-4 weeks): Wrap the model in an inference API, add request batching, semantic caching where applicable, fallback paths, and full observability. Load-test against expected QPS. Deploy via SageMaker, Vertex AI, BentoML, or Triton.
- Drift monitoring & retraining (ongoing): Production monitoring for data drift, prediction drift, and performance degradation. Scheduled retraining pipelines. Shadow deployment of new model versions before promotion.
- Responsible-AI documentation (parallel): Model card, datasheet for the dataset, fairness evaluation broken down by relevant segments, documented failure modes, and an incident-response plan. Required for regulated industries; recommended for everyone.
Frequently asked questions
How is AI/ML different from your AI Agent Development service?
AI/ML covers the underlying models — predictive analytics, NLP classifiers, computer vision, recommendation engines, fine-tuning of foundation models, and the full MLOps pipelines around them. AI Agent Development is one application of AI focused specifically on building autonomous LLM agents that use tools to make decisions. If you need a custom-trained model or fine-tuning, you want this page. If you need an LLM-powered agent or chatbot, see our AI Agent Development page. RAG over enterprise data is on the dedicated RAG page.
How much data do we need to start an ML project?
Depends on the problem. Many modern problems are best solved with zero training data using foundation models and prompt engineering. For supervised classification or regression, a few hundred labeled examples is often enough to validate feasibility. For deep-learning vision or speech models trained from scratch, 10,000+ labeled examples is the minimum. For fine-tuning a foundation LLM, 100-2,000 high-quality examples is usually enough with LoRA. We assess feasibility honestly during the discovery phase.
Should we fine-tune a model or use a foundation model with prompts?
Default to prompts + few-shot examples first — it's cheaper, faster to iterate, and good enough for most use cases. Add RAG when the problem is 'find and cite relevant facts.' Fine-tune when you need consistent format, persona, or domain terminology that prompts can't reliably enforce, or when latency/cost requirements rule out a large model. We pick the right tool in the discovery phase, not by default.
Can you deploy on our existing infrastructure?
Yes. We deploy to AWS SageMaker, Vertex AI, Azure ML, AWS Lambda, Modal, Triton, BentoML, your own Kubernetes cluster, or directly into your application backend. Edge deployment to mobile (Core ML, TFLite, ONNX) and embedded devices is also supported. For air-gapped environments we deploy fully on-prem with vLLM or TGI for LLMs.
How much does an ML project cost?
A focused single-model project (one use case, one inference API) typically ranges from $25,000 to $80,000. A full ML system with custom data pipeline, multiple models, MLOps, and monitoring ranges from $80,000 to $250,000. Computer-vision projects with custom dataset collection and labeling start higher. Fine-tuning of foundation models typically ranges from $15,000 to $60,000 depending on dataset size and evaluation rigor.
What does MLOps look like in practice?
Versioned datasets and models (DVC, MLflow, W&B), reproducible training pipelines, automated evaluation against a held-out set on every retrain, drift monitoring in production, scheduled retraining, and shadow deployment of new versions before promotion. Every model we ship in production includes the full MLOps pipeline — not just the model artifact.
How do you handle model bias and fairness?
Bias evaluation is part of every release: we evaluate model performance broken down by relevant demographic or business segments, document any disparities, and either retrain or apply mitigation (re-sampling, fairness constraints, post-processing) until disparities are within acceptable bounds. We document the entire fairness assessment in the model card.
Ready to ship a model that earns its keep?
Tell us about the problem and the data you have. Book a free strategy call with our Toronto team — we'll give you an honest feasibility take, not a sales pitch.
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