The Engineering That Keeps AI Reliable After Launch
Build the operational infrastructure that makes AI systems observable, measurable, and improvable, cost controls, evaluation pipelines, guardrails, and the monitoring that surfaces problems before your users do.
System 04 - what we ship
What Is LLMOps?
Shipping an AI system is not the same as operating one. Models drift. Prompts that performed well at launch degrade over time. Costs compound faster than usage projections. Users route around outputs they do not trust. LLMOps is the practice of keeping AI systems reliable, observable, and continuously improvable in production, not just at the point of deployment. It is the difference between a system that runs your business and one that occasionally helps someone on a good day.
- Best for
- Scaling pilots
- Best for
- Compliance environments
- Best for
- Multi-agent portfolios
Teams that have shipped an AI system and are dealing with reliability or quality issues
Where something that worked at launch is now underperforming and the team does not have the visibility into the system to understand why.
Engineering leaders designing an AI system and wanting production infrastructure from the start
Observability and evaluation built in from the first sprint are cheaper, more complete, and more useful than observability retrofitted after a reliability incident.
Organisations scaling from one AI use case to a portfolio
Where cost control, cross-system observability, and governance become critical at scale and cannot be managed per-system any longer.
AI projects in regulated environments
Where explainability, audit trails, content controls, and documented human review requirements are not optional and the system needs to satisfy a compliance review, not just a technical one.
- 01
Stack Audit
We review your current AI system's observability coverage, evaluation framework, cost profile, and known failure modes. Most LLMOps problems are visible in the data before they surface to users if the right instrumentation is in place.
- 02
Observability Setup
LangSmith integration, distributed trace logging, latency and cost dashboards, and anomaly alerting. You should be able to see exactly what your agents are doing, what each call costs, and where failures concentrate.
- 03
Evaluation Pipeline
A test set built from real production queries. Automated evaluation on every deployment. Human review workflows for the edge cases that automated metrics miss. Regression detection before new versions reach users.
- 04
Guardrails and Controls
Input and output validation, content and scope filters, token budget controls, fallback paths for cases where the model should not decide alone, and explicit escalation paths where human review is required.
- 05
Ongoing Operation
Monthly performance reviews against the agreed metrics. Quarterly model and framework reviews as the LLM landscape evolves. Prompt engineering iterations driven by what the evaluation data shows, not by what feels like it should work.
- Production observability for deployed LLM and agent systems
- Evaluation frameworks for RAG, agent, and assistant outputs
- Cost and token management at scale
- Guardrails for compliance-sensitive AI outputs
- Fine-tuning and model customisation where prompting is insufficient
- Scaling single-workflow pilots to multi-team production environments
We operate what we build
Every AI system we deploy stays under our observability and evaluation coverage. The operational data informs every improvement. We are not a team that hands off at go-live and responds to support tickets.
LangSmith from the first sprint
Observability is an architectural decision, not a configuration step. We instrument systems for tracing and evaluation before the first user session because data collected from the start is data you can act on.
Framework-agnostic evaluation
Whether the system runs on LangGraph, CrewAI, a custom pipeline, or all three, we build evaluation coverage that measures what matters to the business. The metrics are not constrained by what the framework exposes by default.
Compliance-grade controls
Clients in pharma, fintech, and healthcare need AI guardrails that are documentable and auditable. We design controls that survive a regulatory review with the documentation and justification that accompany them.
Next step · 05
AI Chatbots & Assistants
Customer-facing and internal assistants grounded in your own data and connected to your live systems, with guardrails, human handoff, and conversation analytics built in from the start.