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. 01Baseline

    Measure the best off-the-shelf model on your data. This is the bar to beat.

  2. 02Data

    Curate, label with LLMs and experts, generate synthetic data.

  3. 03Train

    Distil, fine-tune or train from scratch on sovereign GPUs.

  4. 04Evaluate

    Golden sets, error analysis, robustness and bias checks.

  5. 05Optimise

    Quantise, compile and tune for your latency and cost targets.

  6. 06Ship

    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 secondsCPU runs without GPUs Read the approach → 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 latency0 data leaving your environment Read the approach → 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 GPUin-region audio never leaves your jurisdiction Read the approach → 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 → TTSbarge-in natural interruptions Read the approach → 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 signalsminutes from call end to insight Read the approach → 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 processself-hosted no third-party voice API Read the approach → 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 costsame quality on your eval set Read the approach → 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 searchown model, no embedding API costs Read the approach → 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 trainingbalanced classes for rare events Read the approach → 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 siteVLM auto-labelling cuts manual work Read the approach →

Have a different research question or dataset? Tell us about it →

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.

Next step

Let's talk about your AI system.

A free 30-minute call with an Engagement Lead or AI Architect. You'll leave with a clearer view of options, risks and cost, whether or not we work together. Your case doesn't need to fit any box on this site; just tell us what you're facing.

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There’s more to see.

Up next · Use cases What this looks like in your industry 24 use cases across finance, healthcare, retail, manufacturing, legal and SaaS. How we work Start small. Prove value. Scale. Three engagement models, a measure-first approach and a 3-click starting-point finder. About Hands-on engineering, not slideware Who we are and the principles we work by. Blog Notes from the field In-depth articles on architecture, production lessons and research. Services Everything between the idea and the SLA Agents, MCP servers, RAG, fine-tuning, voice, vision and governance, 19 hands-on capabilities.