AI & Machine Learning Model Engineering
We design, train, evaluate, and deploy custom artificial intelligence and machine learning models. That includes text-to-speech (TTS) and speech-to-text (STT) systems, natural language processing pipelines, predictive models for forecasting and decision support, and fully bespoke architectures tailored to your data and domain — alongside deep learning for computer vision and fine-tuned generative models.
Core Technical Capabilities
Custom Model Engineering
Bespoke neural networks and ML architectures designed, trained, and evaluated for your exact problem and data.
Speech Models (TTS & STT)
Text-to-speech and speech-to-text models for voice interfaces, transcription, and audio generation.
Natural Language Processing
Classification, NER, summarization, sentiment, and transformer-based text reasoning pipelines.
Predictive Modeling
Forecasting, risk scoring, and tabular predictive models for operational and business decision support.
LLM Adaptation & RAG
LoRA, QLoRA, and DPO alignment, plus hybrid dense vector and BM25 retrieval with cross-encoders.
Computer Vision
Object detection, segmentation, OCR, and production vision inference pipelines.
Representative Deliverables
- Custom & Predictive Model Checkpoints
- TTS / STT Inference Pipelines
- NLP & Domain LLM Adapters
- Computer Vision & OCR Inference Models
- Production RAG Inference Endpoints
- Model Evaluation & Regression Test Harness
Why Businesses Engage InnvoSols
Zero commercial real-estate overhead. Engagements are executed entirely through agile digital sprints across all client time zones.
Clients speak directly with technical engineers. No non-technical layers or distorted communication channels.
Full intellectual property transfer upon delivery. Clean, documented, standard code repositories and automated test suites.
Every optimization, throughput benchmark, and latency metric is documented and independently reproducible.
AI & Machine Learning: FAQ
What kinds of AI and machine learning models does InnvoSols build?
Custom neural networks and ML architectures, text-to-speech (TTS) and speech-to-text (STT) models, NLP pipelines for classification, NER, summarization and sentiment, predictive models for forecasting and risk scoring, fine-tuned LLMs, and computer vision models for detection, segmentation and OCR.
Can InnvoSols fine-tune a large language model on our own data?
Yes. InnvoSols adapts LLMs with LoRA, QLoRA and DPO alignment, and builds production retrieval-augmented generation (RAG) that combines dense vector search, BM25 and cross-encoder re-ranking over your documents.
Does InnvoSols deploy models to production or only train them?
Both. Models are designed, trained, evaluated and deployed end to end, delivered as model checkpoints, inference pipelines and endpoints, plus an evaluation and regression test harness to catch quality drops.
Can you make training or inference cheaper on limited GPUs?
Yes. InnvoSols wrote TrainStream and AdamBridge, open-source PyTorch libraries that fine-tune models larger than GPU VRAM and cut peak VRAM, and applies the same techniques, including FP8, INT8 and NVFP4 quantization, to client models.
Explore Our Other Solutions
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All inquiries are reviewed directly by technical leadership. Tell us about your workload, stack and goals.