Model selection and evaluation
We shortlist open models against your actual task, then measure them on your data: accuracy, latency, memory and licence terms side by side, before anything is committed to an architecture.
Services
AI & Model Engineering
We work at the model layer — selecting, adapting, quantizing, benchmarking, serving and operating open models against your constraints: latency, language, hardware and data protection. Self-hosted on infrastructure you control.
Evidence
What this domain delivers
What we have shipped in this domain
- Live transcription capacity multiplied up to six-fold on the same GPU — a measured gain reached through model-variant selection, precision and quantization settings, batching and serving configuration.
- Self-hosted open translation models covering 15 languages, including Indian languages, running live inside video consultations.
- Self-hosted neural TTS, and an open vision-language model doing damage analysis on live insurance video calls.
What we offer as capability
- Fine-tuning and adapter training on client data — part of how we adapt an open model to a domain, its vocabulary and its speakers.
- Evaluation harnesses built around your own languages, accents and domain vocabulary, so quality is a measured property of your workload rather than a leaderboard position.
- LLM-integration architecture: tiered gateways that keep sensitive workloads on self-hosted models, with the serving infrastructure underneath built and operated by the same team.
Capabilities
What we do
Adaptation and fine-tuning
Prompt and decoding strategy, domain vocabulary and terminology handling, and fine-tuning or adapter training where the data supports it.
Quantization and precision engineering
Running a model at the precision that fits your hardware without losing the quality you are paying for, verified by measurement rather than assumption.
Serving and inference optimization
Batching, concurrency, warm-up, queueing and streaming behaviour. This is where the 4–6x transcription capacity gain came from.
Benchmarking that reflects production
We measure real concurrent-stream capacity at acceptable latency and quality, not synthetic throughput, and we publish the method alongside the number.
Multilingual speech recognition (ASR)
Live and batch transcription with Whisper, covering 15 languages including Indian languages.
Live translation, speech synthesis and vision
Self-hosted open translation models integrated into real-time products, modern open-source TTS engines, and current open vision-language models for image and video-stream understanding.
LLM integration architecture
LLM-backed features behind a tiered gateway: self-hosted models for sensitive data, external APIs only where your policy explicitly allows. The pattern is described in our decision guide for banks.
Sovereign, self-hosted deployment
The full stack runs on dedicated GPU infrastructure under your control. No data sent to third-party AI APIs. How access and custody work is documented on our security page.
Tech stack
- Whisper (ASR)
- open translation models
- self-hosted neural TTS
- open vision-language models
- quantization and precision tuning
- inference serving and batching
- dedicated GPU infrastructure
- containerization
- CI/CD
- monitoring
Spotlight
Model engineering is the part almost nobody sells
Most vendors who say "AI" mean an API call. The work that decides whether a model is usable in production happens one level down: which variant, at which precision, batched how, served on what, measured against what.
That is the work we do. On the transcription stack we operate, the difference between the default configuration and an engineered one was up to a six-fold live-stream capacity gain on the same GPU — a 4–6x capacity gain on hardware the client already owned. Nothing about the hardware changed. The model variant, the precision, the batching and the serving configuration did.
The same discipline applies to every constraint that makes an open model awkward: a language it handles poorly, a latency budget it misses, a GPU it does not fit on, a data-protection rule that forbids sending anything to a vendor. We work through those constraints in the open-model stack instead of routing around them with a third-party API.
Where the value lands: adapting, quantizing, benchmarking, serving and operating open models is what we have shipped, with numbers attached. Fine-tuning and adapter training extend the same discipline to your own data. Every claim we make about a model is one we can put a measurement behind.
Method
How we work
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Feasibility with your data.
We validate model quality on your real languages, accents and audio conditions before committing to an architecture.
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Measure, then choose.
Model variant, precision and hardware are decided by benchmarks on your workload, not by vendor datasheets or leaderboards.
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Sovereignty by default.
We design for self-hosting first; external APIs only when you explicitly choose them.
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Production, not demo.
Deployment comes with containerization, monitoring and operational runbooks — the same platform discipline we apply to everything we run.
FAQ
Frequently asked questions
What does "working at the model layer" actually mean?
It means we change how the model runs, not just what we send it. Model-variant selection, precision and quantization settings, batching, concurrency and serving configuration are all engineering decisions we make and measure. On our transcription stack that work multiplied capacity up to six-fold on the same hardware — room for over a thousand transcribed consultations a day per GPU.
Do you fine-tune models on our data?
Yes — fine-tuning and adapter training are part of how we adapt an open model to your domain, alongside prompt and decoding strategy and vocabulary handling. We decide it the same way we decide everything else: measure the base model on your data first, then train only where the data supports a gain we can show on a held-out set.
Can we run AI transcription without sending audio to a cloud API?
Yes — that is exactly what we build. Our stack (Whisper ASR, open translation models, neural TTS, vision-language models) runs fully self-hosted on dedicated GPU hardware, so audio, text and video never leave your infrastructure. This is how we meet GDPR requirements for our European clients.
Which languages do you support for live captioning and translation?
Our live captioning and translation setup covers 15 languages, including Indian languages. Whisper's multilingual models are the foundation, and we validate quality per language on real audio before go-live.
How many live transcription streams can one GPU handle?
It depends on model size and latency targets, but as a reference point: through systematic benchmarking and tuning we multiplied concurrent live-transcription capacity up to six-fold on the same GPU.
Is self-hosted AI actually GDPR-compliant?
Self-hosting removes the hardest GDPR problem — third-party data transfer — because processing happens on infrastructure you control. We design the deployment (data flows, retention, access) so your data-protection officer can sign off on it. Our security & data stewardship page describes the operating model, including how our support access is scoped.
Can you integrate LLMs, not just speech and vision models?
Yes, at the architecture and integration level: we design tiered gateways that route sensitive workloads to self-hosted models and route only approved, non-sensitive traffic to external APIs — and we build and operate the serving infrastructure underneath.
Can AI analyze video, not just audio?
Yes. Using a self-hosted open vision-language model we built damage analysis that assesses damage shown during live insurance video calls — vision models put directly into a real-time video workflow.