LLMs Development

Fine-tune, distill, and operate LLMs with measurable quality, controlled cost, and enforceable policy.

Plan an LLM Build & Ops Track
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iconTechnology Stack
  • OpenAI
  • Anthropic
  • Llama
  • Mistral
  • LoRA
  • QLoRA
  • PEFT
  • vLLM
  • TensorRT-LLM
  • Triton
  • ONNX
  • MLflow
  • W&B
  • Ray
  • Kubernetes

From Base Model to Production

Data Curation & Labeling

Tooling rail: weak-supervision • dedup • PII scrub • golden sets

Edge: Leakage prevention and policy-aligned datasets.

Fine-Tuning & Distillation

Tooling rail: LoRA/QLoRA • PEFT • KD pipelines

Edge: Balanced quality vs. cost/latency targets.

RAG + LLM Hybrid Patterns

Tooling rail: rerank • hybrid search • routing

Edge: Σ-Graphion™ graph-aware context for precision.

Evaluation & Safety

Tooling rail: BLEU/BERTScore • task rubrics • red team

Edge: EthicΞense™ enforces policy-as-code with attestations.

Serving & Scaling

Tooling rail: vLLM • TensorRT-LLM • A/B • canary

Edge: Qμβrix™ throughput/latency SLOs with rollback.

Cost Management & Telemetry

Tooling rail: token budgets • caching • quantization

Edge: Neuro-Quantis™ inference FinOps with budgets and caps.

Our Clients

Clients that trusted Us

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Methodology

Prepare & Protect Data Policy

Curation + leakage controls

Tune & Test Eval

Sandboxed; auditable prompts/outputs.

Serve & Monitor LLM Ops

SLOs + rollback + budgets.

LLMs you can govern

Quality, safety, and cost—engineered together.

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  • Policy-aligned data curation
  • Efficient tuning and distillation
  • Graph-enhanced retrieval
  • Production-grade serving and SLOs
  • FinOps telemetry and controls

Assurance Strip

Foundational safeguards ensuring compliance, accountability, and operational integrity.

Evaluation Assessing AI models, data, and system outputs to ensure accuracy, reliability, and alignment with objectives
Safety Implementing measures to prevent harm, mitigate risks, and ensure ethical AI operations
SLOs Service-level objectives defining performance, reliability, and availability standards for AI systems
Budgets Tracking and controlling costs for AI initiatives, including development, deployment, and operational expenses

Boardroom Kit

Executive tools to govern, track, and fund AI initiatives responsibly.

LLM Eval Checklist Step-by-step guide to evaluate large language models for accuracy, bias, and compliance before deployment
Safety Test Pack Collection of tests and procedures to validate AI system safety and risk mitigation measures
Serving SLOs Operational objectives defining performance, availability, and reliability standards for AI model serving
FinOps Budget Sheet Financial tool to track, plan, and control budgets for AI development, deployment, and operational costs

Get Started – AI Efficiency with
Proven Performance

Accelerate AI workloads with rigorously tested Quantum-Inspired Neuromorphic AI.

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