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LLMs are easy to prototype and hard to operate. The infrastructure between a demo and a production system is the engineering problem most teams underestimate. We build that infrastructure.
Streamline deployment, optimise performance, and maximise ROI for your large language models.
BNS builds the LLMOps infrastructure — evaluation frameworks, prompt management, cost governance, guardrails, and observability — that makes large language model products reliable and economical in production.
BNS LLMOps programs build the production infrastructure for large language model applications — evaluation frameworks, prompt versioning, cost governance, guardrail systems, observability stacks, and the MLOps foundations that keep LLM products reliable and improving. The gap between a convincing GPT-4 demo and a production LLM product is the same gap as any other ML system: evaluation, monitoring, cost discipline, and governance. We build these foundations on the data and cloud platforms your engineering organisation already operates, connected to the AI engineering practice that governs the models and data underneath.
Our LLMOps engineers have shipped production RAG systems, agentic workflows, and fine-tuned models inside regulated enterprises — and have been through the cost surprises, quality regressions, and compliance reviews that happen when LLMOps infrastructure is an afterthought. Evaluation is a standing product, not a pre-launch checklist. Every LLM product ships with a red-teaming framework, a cost budget, and a quality baseline that the team owns and runs — so the business impact is measured, and the cost is predictable.
Current LLM application architecture, cost baseline, quality baseline, and compliance requirements.
Evaluation framework, red-teaming suite, and quality baseline established before any optimisation.
Prompt versioning, cost governance, guardrails, and observability stack deployed to production.
Caching, model routing, RAG tuning, and continuous quality improvement with measured outcomes.
Automated evaluation suites for accuracy, hallucination rate, toxicity, and task-specific quality metrics.
Version-controlled prompt libraries, A/B testing, and systematic prompt optimisation.
Governed retrieval pipelines with versioned corpora, access control, lineage, and document-level audit.
Token budgets, semantic caching, model routing, and inference cost telemetry.
Input/output filtering, PII redaction, jailbreak detection, and content policy enforcement.
Trace logging, latency monitoring, cost dashboards, and quality regression alerting.
BNS is technology-neutral by policy — our engineers work across the open-source and commercial stacks that enterprise platforms actually run on.
Semantic caching, model routing, prompt optimisation, and token budget governance cut LLM inference costs by 61% on average — without degrading product quality. The cost model for a production LLM application is predictable and improving from the first deployment, not a surprise at month-end.
Automated evaluation suites covering hallucination rate, factual accuracy, and task-specific quality metrics run on every prompt change and every model upgrade. Regressions are caught before they reach users — not discovered from a complaint. 100% of production LLM applications are under continuous evaluation.
Retrieval corpora are versioned, access-controlled, and lineage-tracked — with document-level audit, PII handling, and data residency controls. The LLM has access to the right data, with the right controls, and every retrieval decision is logged. Regulated-industry RAG that meets the standard auditors apply.
Input filtering, PII redaction, content policy enforcement, and jailbreak detection are production-grade components — not prompt instructions. Every output is checked against the content policy before it reaches the user. Red-teaming results inform the guardrail design, and the guardrails are updated as the threat model evolves.
BNS LLMOps programs move from PoC to production-grade infrastructure inside four weeks — evaluation framework, observability stack, cost governance, and guardrails deployed and running. The first production deployment has the infrastructure the team needs to operate and improve it.
Our LLMOps leads have shipped production RAG systems, agentic workflows, and fine-tuned models inside regulated enterprises. They have been through the cost spirals, quality regressions, and compliance reviews that happen when the infrastructure is not there — and they build it in from day one.
OpenAI, Anthropic, Google Gemini, Meta Llama, Mistral — we work across the full LLM provider landscape and recommend based on cost, quality, latency, residency, and compliance requirements. Model routing between providers is a governance decision, not a loyalty decision.
LLM applications are one part of an enterprise AI portfolio. BNS LLMOps programs connect to the data governance, model risk, and AI governance frameworks the organisation runs — so LLM products are governed alongside, not separately from, the rest of the AI estate.
Transparency obligations, automated-decision documentation, PII handling in prompts and responses, and data residency requirements are designed into the LLMOps infrastructure — not retrofitted when the regulation lands. Clients deploying LLM products in the EU are compliant from the first production sprint.
Measurable outcomes from the platforms our engineers shipped this year.
Most enterprise AI projects stall because the platform was never built to run models in production. A pragmatic blueprint for the foundation layer.
The hardest part of shipping AI in healthcare or financial services is not the model. It is proving that you did the uninteresting things right.
Boards over-index on burn-rate and under-index on delivery signal. The five charts that change the room.
Yes — Llama, Mistral, and other open-source models on your cloud or on-premises infrastructure are supported.
Yes — we run an LLMOps audit to baseline quality and cost, then implement the infrastructure to improve both.
Trusted data platforms and production AI that moves the P&L — not the lab.
Seamless integration and maximum ROI for your machine learning models in production.
Tailored data-driven solutions that optimise performance and drive measurable business outcomes.
Share your brief and we’ll assemble two senior llmops practitioners for a 90-minute working session — no deck, no sales.