# Applied AI Research: ML, LLM & voice models > Applied ML, LLM and speech research that ends in production: LLM distillation up to 1000× cheaper per prediction, small language models, speech recognition for low-resource languages, custom voices and inference optimisation. Source: https://aibyos.com/research Applied AI Research # Custom models that beat general LLMs _on your task_. A general-purpose LLM is rarely the cheapest, fastest or most private way to solve one specific problem. We research, train and ship task-specific ML, language and speech models, benchmarked against the best off-the-shelf alternative. **up to 1000×**lower cost per prediction with distilled models **100×+**faster responses: milliseconds, not seconds **30–50%**fewer speech-recognition errors after fine-tuning **2–5×**more throughput per GPU after optimisation 01 · Method ## From question to production model. Every research project follows the same reproducible pipeline, so results are measurable and the model you get is production-ready. 1. 01**Baseline** Measure the best off-the-shelf model on your data. This is the bar to beat. 2. 02**Data** Curate, label with LLMs and experts, generate synthetic data. 3. 03**Train** Distil, fine-tune or train from scratch on sovereign GPUs. 4. 04**Evaluate** Golden sets, error analysis, robustness and bias checks. 5. 05**Optimise** Quantise, compile and tune for your latency and cost targets. 6. 06**Ship** Serve, monitor and retrain with a documented model card. 02 · Research work ## What we research and build. Filter by area and open any card for the pipeline and tooling. [LLM up to 1000× lower cost per prediction Distilling an LLM into a task model 1000× cheaper Replace a large-LLM API call on a high-volume task (classification, routing, tagging) with a small model trained on the LLM’s own judgements. _100×+ faster: milliseconds, not seconds__CPU runs without GPUs_ Read the approach →](https://aibyos.com/research/distill) [LLM 10–50× lower inference cost Small language model that matches a large one on one job A fine-tuned 1–8B open model for structured extraction or drafting that reaches large-model quality on the target task, self-hosted, no data egress. _3–10× lower latency__0 data leaving your environment_ Read the approach →](https://aibyos.com/research/slm) [Voice & speech 30–50% relative drop in word error rate Speech recognition for a low-resource language Fine-tune a Whisper-class model for a language, accent or domain that general models handle poorly, e.g. Azerbaijani, Polish dialects or medical dictation. _real-time streaming on one GPU__in-region audio never leaves your jurisdiction_ Read the approach →](https://aibyos.com/research/asr) [Voice & speech <1 s target voice-to-voice latency Sub-second voice agent pipeline Research and engineering to bring voice-to-voice response time low enough for natural phone conversations. _streaming ASR → LLM → TTS__barge-in natural interruptions_ Read the approach →](https://aibyos.com/research/voice-latency) [Voice & speech 100% of calls analysed vs. manual sampling Emotion & sentiment from customer calls Multimodal models that combine what is said with how it is said, to flag frustrated customers and coach agents, on every call, not a sample. _audio + text multimodal signals__minutes from call end to insight_ Read the approach →](https://aibyos.com/research/emotion) [Voice & speech hours not weeks, of studio audio needed Custom brand voice (text-to-speech) A natural, consistent brand or product voice trained from consented recordings, running on your own infrastructure. _consent documented voice-rights process__self-hosted no third-party voice API_ Read the approach →](https://aibyos.com/research/tts) [Inference 2–5× more throughput per GPU Inference optimisation for self-hosted LLMs Serve more users on the same GPUs through quantisation, batching and speculative decoding, without measurable quality loss on your evals. _50–75% lower serving cost__same quality on your eval set_ Read the approach →](https://aibyos.com/research/inference) [Machine learning +10–30% recall@10 over generic embeddings Domain-tuned embeddings for better search Fine-tune an embedding model on your terminology so retrieval finds the right document first: the biggest lever on RAG quality. _smaller index and faster search__own model, no embedding API costs_ Read the approach →](https://aibyos.com/research/embed) [Machine learning weeks instead of months to first model Synthetic data for rare events Train fraud, defect or anomaly models when real positive examples are rare, sensitive or slow to label. _privacy no real personal data in training__balanced classes for rare events_ Read the approach →](https://aibyos.com/research/synthetic) [Vision <20 ms per frame on edge devices From vision-language model to edge detector Use a large vision-language model to label images, then train a compact detector that runs in real time on edge hardware. _no cloud inference on site__VLM auto-labelling cuts manual work_ Read the approach →](https://aibyos.com/research/edge-vision) Have a different research question or dataset? [Tell us about it →](https://aibyos.com/contact) Figures show typical orders of magnitude for each approach compared with calling a large general-purpose LLM, based on published benchmarks and common practice. Actual results depend on the task and data; we measure them on your data during the baseline phase.