Generic LLMs give generic answers. We fine-tune, extend, and deploy language models that speak your domain, follow your rules, and serve your customers.
80+
LLMs Deployed
99.2%
Uptime SLA
60%
Avg Cost Reduction
LLM Token Pipeline
Capabilities
From model selection to production deployment - we handle every layer of your LLM product.
Adapt GPT-4, Llama, Mistral, or Gemini to your domain vocabulary, tone, and task - using your proprietary data for maximum accuracy.
Retrieval-Augmented Generation systems that ground your LLM in live business knowledge - eliminating hallucinations and keeping answers current.
Connect OpenAI, Anthropic, Cohere, or open-source models to your product stack via robust, cost-optimised API layers.
For data-sensitive sectors: run fully private LLMs on your own infrastructure with no data leaving your environment.
Systematic prompt design, chain-of-thought patterns, and few-shot examples that reliably produce high-quality, structured outputs.
Automated evaluation pipelines measuring accuracy, safety, latency, and cost - with regression testing for every model update.
0+
LLMs Deployed
0%
Avg Cost Reduction
0×
Faster than Internal Build
0%
Uptime SLA
Methodology
01
We define exact input/output specs, quality thresholds, latency budgets, and data availability - before a single token is processed.
Deliverables
02
Curate, clean, and format training data. For fine-tuning: instruction-response pairs. For RAG: chunking strategy, embedding selection, vector store setup.
Deliverables
03
Run LoRA/QLoRA fine-tuning or full fine-tuning depending on your requirements. Iterative evaluation against your benchmarks at each checkpoint.
Deliverables
04
Production-grade API deployment with autoscaling, cost tracking, and a live dashboard monitoring quality, latency, and token spend.
Deliverables
Technology Stack
Foundation Models
Training
RAG & Search
Infra & Ops
Common Questions
It depends on your goal. Fine-tuning adapts the model's style, format, and domain knowledge permanently. RAG retrieves up-to-date information at inference time. For most enterprise use cases, a RAG pipeline with light prompt tuning is faster and cheaper - fine-tuning makes sense when you need deep style or domain specialisation.
We recommend based on your budget, latency requirements, and task complexity. GPT-4 class models for high-stakes reasoning; Llama/Mistral for on-premise or cost-sensitive deployments; Gemini where multimodality is important. We work with all major providers.
More than you might think - but less than you fear. For instruction fine-tuning, 500-5,000 high-quality examples typically produce strong results. For domain adaptation, larger datasets (50k+) are needed. We help you assess and source the right volume.
Yes. We specialise in private, on-premise deployments using open-source models (Llama 3, Mistral, Mixtral) on your infrastructure. No data leaves your environment.
Through a combination of RAG (grounding responses in verified sources), output validation layers, structured generation constraints, and continuous evaluation pipelines that flag regression in factual accuracy.
Tell us your use case and we'll design the right LLM architecture - no commitment required.