LLMOps for AI That's Already Live
Our engineers set up the monitoring, evaluation, and cost controls that keep your AI features accurate and affordable in production.
👋 Talk to an LLMOps engineer.
Trusted and top rated tech team
When your AI works until it doesn't
Your team shipped AI features and moved to the next sprint. Now prompts aren’t versioned, there’s no evaluation running, costs spike without warning, and the only way you find out about hallucinations is when a customer complains. LLMOps is the operational layer that keeps AI features reliable after launch. Curotec builds that layer into your existing infrastructure so your team isn’t stuck babysitting models.
Our capabilities include:
- Prompt versioning, testing, and regression detection
- Evaluation pipelines for accuracy and hallucination
- Cost monitoring, model routing, and token optimization
- Drift detection and output quality tracking
- Guardrails for prompt injection and data leakage
- Deployment infrastructure for model serving at scale
Who we support
We help teams that already have AI in production but don’t have the operational infrastructure to keep it reliable, affordable, and accurate over time.
Groups With AI and No Observability
Your AI features return 200s even when they hallucinate. You have no visibility into output quality, retrieval accuracy, or whether the model's behavior changed after the last provider update. You need monitoring that catches semantic failures, not just infrastructure uptime.
Engineering Leads Watching AI Costs Climb
Every AI feature adds token costs and you can't predict the monthly bill. Some queries use expensive models when cheaper ones would work fine. You need cost tracking per feature, model routing by complexity, and prompt optimization that reduces spend without degrading output.
Teams Running Prompts Without Versioning
Someone changed a system prompt last week and output quality dropped but nobody can trace what changed or roll it back. You need prompts treated as code with versioning, peer review, and regression testing before anything reaches production.
Ways to engage
We offer a wide range of engagement models to meet our clients’ needs. From hourly consultation to fully managed solutions, our engagement models are designed to be flexible and customizable.
Staff Augmentation
Get access to on-demand product and engineering team talent that gives your company the flexibility to scale up and down as business needs ebb and flow.
Retainer Services
Retainers are perfect for companies that have a fully built product in maintenance mode. We'll give you peace of mind by keeping your software running, secure, and up to date.
Project Engagement
Project-based contracts that can range from small-scale audit and strategy sessions to more intricate replatforming or build from scratch initiatives.
We'll spec out a custom engagement model for you
Invested in creating success and defining new standards
At Curotec, we do more than deliver cutting-edge solutions — we build lasting partnerships. It’s the trust and collaboration we foster with our clients that make CEOs, CTOs, and CMOs consistently choose Curotec as their go-to partner.
Why choose Curotec for LLMOps?
Most teams build AI features and move on. Nobody sets up prompt versioning, evaluation pipelines, or cost alerts until something breaks in front of a customer. Our engineers build the operational layer after launch because we build the AI features themselves. We already know how the prompts are structured, where the models are called, and how the pipeline connects to your infrastructure. That’s why the monitoring we set up actually catches the problems that matter.
1
Extraordinary people, exceptional outcomes
Our outstanding team represents our greatest asset. With business acumen, we translate objectives into solutions. Intellectual agility drives efficient software development problem-solving. Superior communication ensures seamless teamwork integration.
2
Deep technical expertise
We don’t claim to be experts in every framework and language. Instead, we focus on the tech ecosystems in which we excel, selecting engagements that align with our competencies for optimal results. Moreover, we offer pre-developed components and scaffolding to save you time and money.
3
Balancing innovation with practicality
We stay ahead of industry trends and innovations, avoiding the hype of every new technology fad. Focusing on innovations with real commercial potential, we guide you through the ever-changing tech landscape, helping you embrace proven technologies and cutting-edge advancements.
4
Flexibility in our approach
We offer a range of flexible working arrangements to meet your specific needs. Whether you prefer our end-to-end project delivery, embedding our experts within your teams, or consulting and retainer options, we have a solution designed to suit you.
How we operationalize your AI features
Prompt Versioning & Management
Evaluation & Hallucination Detection
Cost Monitoring & Model Routing
Drift Detection & Output Monitoring
Guardrails & Security
Serving Infrastructure & Scaling
What our LLMOps engineers work with daily
Observability & Tracing
Catching failures in AI features requires tracing that follows a request from prompt to retrieval to generation, not just endpoint health checks.
- LangSmith – Trace every step of your LLM chain with prompt versioning, test datasets, and regression detection across deployments
- Langfuse – Open-source tracing for LLM applications with cost tracking, latency analysis, and scoring workflows per completion
- Arize Phoenix – Retrieval and generation analysis with embedding drift detection, response quality scoring, and exportable trace data
- Datadog – APM integration that tracks inference latency, token usage, and error rates alongside your existing application metrics
- Helicone – Request logging and cost analytics across LLM providers with latency breakdowns and prompt-level spend attribution
- Prometheus – Time-series collection for inference throughput, GPU utilization, and endpoint health across your serving infrastructure
Evaluation & Testing Frameworks
Standard test suites fail with non-deterministic outputs. LLM evaluation needs specialized metrics for accuracy, safety, and groundedness.
- RAGAS – Faithfulness, answer relevance, and context precision scoring for RAG pipelines with automated evaluation runs
- DeepEval – Unit testing framework for LLM outputs with hallucination, bias, toxicity, and correctness metrics per completion
- TruLens – Feedback functions that score groundedness and relevance with full tracing across each step of your generation chain
- Braintrust – Evaluation platform with prompt playground, dataset management, and side-by-side comparison across model versions
- Promptfoo – CLI tool for running prompt regression tests across providers, catching output degradation before it reaches production
- LLM-as-Judge – Using a secondary model to evaluate primary outputs for tone, accuracy, and instruction adherence at scale
Prompt Management & Versioning
Treating prompts as code allows version control, peer review, and rollbacks, so your team can track changes when outputs shift.
- LangSmith Prompt Hub – Centralized prompt storage with versioning, tagging, and deployment tracking across environments
- Portkey Prompts – Managed prompt templates with variable injection, A/B testing, and performance comparison across versions
- Pezzo – Open-source prompt management with version history, observability, and instant rollback for production prompts
- GitHub Actions – CI workflows that run prompt regression tests on every change before new versions deploy to production
- Humanloop – Prompt engineering platform with evaluation, versioning, and deployment pipelines for teams managing prompts collaboratively
- Custom Registries – Internal prompt stores built into your codebase with git-tracked history, environment configs, and approval gates
Cost Control & Model Routing
CoManaging per-token spend across providers and features requires routing logic that matches query complexity to the right-sized model.
- Portkey – Gateway for multiple LLM providers with automatic fallbacks, spend monitoring, and per-feature budget controls
- LiteLLM – Unified interface for calling 100+ providers with cost tracking, retries, and routing rules based on latency or price
- Redis – Semantic caching that stores frequent responses to reduce redundant API calls and lower per-request costs
- GPTCache – Similarity-based caching layer that matches incoming queries to prior responses, skipping the provider entirely
- Cloudflare AI Gateway – Edge caching and rate limiting for AI API calls with analytics, logging, and request-level controls
- Custom Routing Logic – Query classifiers that route simple requests to smaller models and complex requests to capable ones automatically
Guardrails & Security
Defending against prompt injection, data leakage, and unsafe outputs requires enforcement at both the request and response layer.
- NVIDIA NeMo Guardrails – Programmable safety layer that intercepts and validates inputs and outputs against defined conversational boundaries
- Guardrails AI – Validation framework with pre-built checks for PII, hallucination, toxicity, and schema compliance on every response
- Rebuff – Prompt injection detection using heuristics, LLM analysis, and vector similarity to flag adversarial inputs before processing
- Presidio – Microsoft’s PII detection and anonymization engine for scrubbing sensitive data from prompts and outputs
- Lakera Guard – Real-time threat detection for prompt injection, data exfiltration, and content policy violations across providers
- Custom Policy Layers – Application-specific rules for content filtering, output formatting, and domain boundaries enforced in your middleware
Serving Infrastructure & Scaling
How you host and scale inference endpoints determines latency, cost per request, and operational burden on your team.
- vLLM – High-throughput serving engine for open-source models with PagedAttention, continuous batching, and low-latency inference
- BentoML – Model serving framework with containerized deployment, adaptive batching, and multi-model endpoint management
- AWS SageMaker – Managed endpoints with auto-scaling, built-in monitoring, and integration across AWS services for production hosting
- Docker – Containerized serving for consistent behavior across development, staging, and production with reproducible builds
- Kubernetes – Orchestrated scaling for inference endpoints with GPU scheduling, readiness probes, and independent resource allocation
- ONNX Runtime – Cross-platform inference optimization that converts PyTorch and TensorFlow models for faster serving at lower compute cost
FAQs about our LLMOps services
How is LLMOps different from MLOps?
MLOps manages traditional models with deterministic outputs and accuracy scores. LLMOps manages non-deterministic text generation where evaluation requires semantic scoring, prompts need versioning like code, and costs scale per token instead of per training run.
We already have Datadog. Isn't that enough?
Datadog tells you the endpoint responded in 200ms. It doesn’t tell you the response hallucinated, the retrieval pulled irrelevant context, or that a prompt change last week degraded answer quality by 15%. LLMOps adds the semantic layer your APM can’t see.
How do you reduce our LLM costs?
We track spend per feature, route simple queries to smaller models, cache frequent responses, and compress prompts to reduce token counts. Most teams find 30-50% savings once routing and caching are in place without any change to output quality.
What happens when the model provider changes?
Evaluation pipelines catch it. We run regression tests against your ground truth data on every provider update so output degradation surfaces in your CI, not in customer complaints. Rollback is immediate if quality drops below your threshold.
Do we need LLMOps if we only use one model?
Yes. Even a single OpenAI integration needs prompt versioning, cost monitoring, hallucination detection, and guardrails against prompt injection. The number of models doesn’t determine operational complexity — the number of users does.
Can you set this up for AI we didn't build?
Yes. We audit your existing AI features, instrument observability, add evaluation pipelines, and configure cost controls regardless of who built the original implementation. We work inside your repo and infrastructure as-is.
Ready to have a conversation?
We’re here to discuss how we can partner, sharing our knowledge and experience for your product development needs. Get started driving your business forward.
