True LLM development is not prompt engineering. It is pre-training, large-scale fine-tuning, quantization, inference optimization and the GPU clusters to do it. We build foundation models for banks, governments, telecoms and large fintechs who can't rent their AI.
Walk into ten AI agencies and nine will sell you "LLM development" — but the work is prompt engineering, light fine-tuning or RAG over GPT-4. That's fine for chatbots. It is not LLM development.
From-scratch or continued pre-training on trillions of tokens. Custom tokenizers, curated corpora, distributed training over multi-node GPU clusters.
Supervised fine-tuning, instruction tuning, DPO, ORPO and GRPO alignment on your domain data with proper evaluation harnesses.
INT8, INT4, AWQ, GPTQ and SmoothQuant. Shrink 70B models to fit a single GPU without losing the accuracy that matters.
vLLM, TensorRT-LLM, SGLang and llama.cpp tuning. Continuous batching, paged attention, speculative decoding — production throughput and latency.
On-prem or sovereign-cloud GPU clusters, InfiniBand fabric, Kubernetes, observability and air-gapped deployment for regulated environments.
Web-scale crawling, deduplication, quality filtering, PII removal, multilingual balancing and synthetic data generation pipelines.
Sovereign credit models, internal copilots over confidential filings, fraud and compliance LLMs that never leave the bank's perimeter.
Multilingual, sovereign-AI models trained on national corpora — defence, public-services, judiciary, fully air-gapped deployment.
Network-ops copilots, carrier-grade customer-care LLMs and CDR-aware analytical models trained on subscriber-scale data.
Proprietary LLMs for risk, KYC, trading research and partner copilots — fine-tuned on years of in-house transactional and document data.
H100 / H200 / B200 multi-node clusters connected over InfiniBand 400G. On-prem or sovereign-cloud (CoreWeave, Lambda, Crusoe, OCI).
Distributed pre-training with ZeRO-3, FSDP, tensor and pipeline parallelism. Megatron-LM for trillion-token runs.
Industrial-strength training stacks for 7B–70B+ models. Activation checkpointing, sequence parallelism, communication overlap.
Production inference — continuous batching, paged attention, FP8 and INT4 kernels for sub-50ms latency at scale.
Datatrove, dolma, fineweb-style pipelines: crawl, dedupe, filter, tokenize. PII scrubbing and multilingual balancing built in.
MMLU, GSM8K, HumanEval, BBH plus custom domain harnesses. Red-teaming, jailbreak resistance and policy alignment.
Model size, languages, target tasks, compliance constraints and infrastructure plan. Cluster sizing and budgeting before a single GPU spins up.
Curate trillions of tokens, build the tokenizer, run distributed pre-training across multi-node H100 / H200 clusters with full observability.
Instruction tuning, DPO/ORPO, red-teaming and full evaluation against MMLU, domain benchmarks and your acceptance criteria.
Quantize, optimize inference with vLLM / TensorRT-LLM, deploy self-hosted, set up monitoring, retraining and incident response.
We've trained and operated multi-node H100 clusters end-to-end — not Colab notebooks. Distributed bugs are our day job, not a surprise.
Self-hosted by default. Models, weights, datasets and training pipelines never leave your jurisdiction or your firewall.
We work the way banks and governments require — change control, audit logs, secure SDLC, dual control over model releases.
We tell you when fine-tuning is the right answer instead of pre-training. We don't bill GPU hours that don't move the metric.
Local language and script support is a first-class concern from tokenizer through eval — not a translation layer bolted on at the end.
We don't disappear after handover. Continued pre-training, alignment refreshes and incident response under SLA.
Take an open-weight base model and continue pre-training on your domain corpus, then align and deploy.
Build a foundation model from scratch — your tokenizer, your data, your weights, your moat.
Multi-model, multi-year programme for governments, central banks and tier-1 institutions.
LLM development means creating or heavily modifying foundation models — pre-training new models from scratch, performing large-scale fine-tuning, quantizing and optimizing them for inference, and operating the GPU clusters required to do it. It is not the same as prompt engineering or building RAG on top of GPT-4.
Banks, governments, telecom operators and large fintech companies that need sovereign, regulated or domain-specialized models — not generic SaaS APIs. Anyone with compliance, data-residency or scale requirements that rule out public AI providers.
Most agencies claim 'LLM development' but only do prompt engineering, light fine-tuning or RAG on top of someone else's model. True LLM development requires large GPU clusters, ML engineers, massive datasets and full ownership of the training pipeline — that is what we deliver.
Multi-node H100 or H200 GPU clusters connected over InfiniBand, distributed training with DeepSpeed, FSDP or Megatron-LM, and self-hosted inference using vLLM or TensorRT-LLM behind your firewall.
Specialized models in the 1B–13B parameter range typically take 3–9 months end-to-end including data curation, pre-training, instruction tuning, alignment and evaluation. Continued pre-training of existing open-weight models is faster — usually 6–14 weeks.
Yes. Weights, tokenizer, training code and dataset pipelines are all delivered to you. Sovereign AI means sovereign — no vendor lock-in, no remote kill switch.
Tell us where you want to incorporate and which package fits. We'll come back with a checklist and a timeline — usually within the same business day.