The Engineering Team Behind Your Production AI

We build LLM features, AI agents, and RAG systems that hold up under real traffic, with evals, guardrails, and observability engineered in from day one.

👋 Talk to an AI engineer.

Trusted and top rated tech team

Production AI Engineering, Not Just Prototypes

Most AI pilots stall between demo and production. The models work in isolation but fall apart under real traffic, cost spirals out of control, and there’s no way to measure whether the output is actually good. We build AI features engineered to survive that transition, with evals, cost controls, and observability wired in from the first sprint.

Our capabilities include:

Python engineering for AI workloads
Anthropic, OpenAI, and Google model integration
LangGraph for production agent orchestration
MCP (Model Context Protocol) for agent and tool connectivity
RAG pipelines on pgvector, Qdrant, and Turbopuffer
Evals and observability with Braintrust, LangSmith, and Arize
Fast inference with Groq, Cerebras, and Fireworks
Voice agents with Vapi, Retell, and ElevenLabs

Who we support

We build AI features for teams that need production reliability, not another proof-of-concept. Every engagement starts with your existing application and infrastructure.

Product Teams Adding AI for the First Time

Leadership wants AI in the product but nobody on your team has integrated LLMs, built a RAG pipeline, or deployed an agent to production. You're evaluating providers and architectures without a clear path from prototype to feature release.

Teams Whose AI Failed in Production

Your AI feature worked in demos but real traffic exposed hallucinations, latency problems, and costs that don't scale. You need engineers who can diagnose what went wrong and rebuild the parts that can't handle production load.

Engineering Leads Managing AI at Scale

Your AI features work but every API call costs money and adds latency. You need caching strategies, model selection per task, prompt optimization, and architecture decisions that keep costs predictable as usage grows.

Enterprise Teams Needing AI Governance

Your industry requires audit trails, access controls, and explainability around AI decisions. You need models deployed with proper data governance, permission scoping, and monitoring that satisfies your compliance and security teams.

Teams Replacing Manual Processes With AI

Your team handles document processing, data classification, or support triage manually and the volume keeps growing. You need automation that integrates into your current systems without requiring a separate platform or workflow.

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

Why choose Curotec for AI & ML development?

Most AI agencies can build a demo. Few can integrate it into a production application without breaking what already works. Our engineers build AI inside the same codebases they’ve maintained for over a decade. Your LLM integration works with your auth system, your RAG pipeline connects to your actual document stores, and your models deploy through the same pipeline as the rest of your application.

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.

What AI looks like inside the applications we build

Intelligent Search & Knowledge Retrieval

Give users and employees answers pulled from your actual documents, databases, and support history instead of generic AI responses.

Predictive Analytics & Recommendations

Surface recommendations, churn risk, and demand forecasts trained on your data so predictions reflect your business, not benchmarks.

Conversational AI & Chatbots

Deploy chat interfaces that maintain context and respond from your knowledge base without hallucinating answers or losing conversation state.

Document Processing & Extraction

Extract structured data from invoices, contracts, and forms using models that handle your specific document formats and edge cases.

Workflow Automation & AI Agents

Automate multi-step processes where AI agents call APIs, make decisions, and execute tasks inside your existing business systems.

Anomaly Detection & Monitoring

Flag fraud, infrastructure issues, and data quality problems in real time using models trained on your own operational patterns.

What our AI engineers work with daily

LLM Providers & APIs

Which model you choose affects answer quality, latency, token cost, and how well it integrates with your existing application stack.

OpenAI API – GPT-5.4, GPT-5.4 mini, and GPT-5.4 nano with function calling, structured output, tool use, and long-context support for production AI features
Anthropic Claude API – Claude Opus 4.6 and Sonnet 4.6 with 1M token context, extended thinking, tool use, and computer use for analysis, coding, and agentic workflows
AWS Bedrock – Managed access to foundation models from Anthropic, Meta, and Cohere with AWS VPC integration and enterprise security
Azure OpenAI Service – Enterprise deployment of OpenAI models with Azure compliance, regional availability, and private networking
Google Vertex AI – Gemini models with grounding APIs and integration across Google Cloud services for enterprise AI workloads
Open-Source Models – Llama, Mistral, and self-hosted options for teams requiring on-premise deployment or full control over inference costs

Orchestration & Agent Frameworks

Chaining prompts, managing context, and coordinating multi-step AI workflows requires frameworks built for production reliability.

LangChain – Python and JavaScript framework for chaining prompts, managing memory, and building retrieval-augmented workflows
LlamaIndex – Indexing and query framework that connects LLMs to external sources with chunking strategies and response synthesizers
CrewAI – Multi-agent orchestration framework where specialized AI agents collaborate on complex tasks with role-based delegation
AutoGen – Microsoft’s framework for building multi-agent conversations where AI agents plan, execute, and validate work collaboratively
Semantic Kernel – Microsoft SDK for integrating LLMs into .NET and Python applications with prompt templating and plugin architecture
Haystack – Open-source framework for building search, question-answering, and RAG pipelines with modular, swappable components

Vector Databases & Embeddings

Storing and retrieving embedded content accurately is what makes RAG, semantic search, and AI-powered recommendations actually work.

Pinecone – Managed vector database with real-time indexing, metadata filtering, and namespace isolation for multi-tenant applications
Weaviate – Open-source vector database with hybrid search combining dense vectors and BM25 keyword matching in a single query
pgvector – PostgreSQL extension that adds vector similarity search to your existing relational database without separate infrastructure
Qdrant – Vector search engine with payload filtering, on-premise deployment, and snapshot-based backups for enterprise requirements
Elasticsearch – Hybrid search engine combining BM25 keyword matching with vector similarity for pipelines needing lexical and semantic precision
Chroma – Lightweight embedding database for prototyping and production RAG applications with straightforward Python integration

ML Frameworks & Model Training

Training and deploying models for classification, prediction, and analysis requires frameworks your team can retrain and maintain.

PyTorch – Deep learning framework for building and training neural networks across NLP, computer vision, and custom model architectures
TensorFlow – End-to-end ML platform with production serving, distributed training, and mobile deployment through TensorFlow Lite
Hugging Face Transformers – Pre-trained models for text classification, summarization, entity recognition, and embedding generation with fine-tuning support
Scikit-Learn – Classical ML library for regression, classification, clustering, and feature engineering on structured datasets
XGBoost – Gradient boosting framework for tabular data problems including fraud detection, churn prediction, and demand forecasting
MLflow – Experiment tracking, model versioning, and deployment management that brings reproducibility to your ML training pipeline

AI Infrastructure & Deployment

Where and how you serve models affects latency, cost, scaling behavior, and how much operational overhead your team absorbs.

AWS SageMaker – Managed model hosting with auto-scaling endpoints, built-in monitoring, and integration across AWS services
Docker – Containerized model serving for consistent behavior across local development, staging, and production environments
Kubernetes – Orchestrated scaling for inference endpoints, training jobs, and pipeline workers with independent resource allocation
Terraform – Infrastructure-as-code provisioning for GPU instances, model endpoints, vector databases, and networking configuration
ONNX Runtime – Cross-platform inference optimization that converts models from PyTorch or TensorFlow for faster serving with lower compute costs
TensorFlow Lite – On-device ML deployment for mobile and edge applications where latency and offline capability are requirements

Monitoring & Observability

Tracking model performance, prompt quality, token spend, and inference latency surfaces issues before they reach your users.

LangSmith – Tracing and evaluation platform with prompt versioning, test datasets, and regression detection for LLM applications
Datadog – APM integration for tracing inference latency, token usage, error rates, and model performance alongside application metrics
Weights & Biases – Experiment tracking with prompt logging, evaluation metrics, and collaboration features for ML development teams
Helicone – LLM observability with request logging, cost tracking, and latency analytics across providers and prompt versions
Arize AI – ML observability for monitoring model drift, retrieval quality, and response accuracy in production deployments
Prometheus – Time-series metrics collection for tracking inference throughput, GPU utilization, and endpoint health across your serving infrastructure

Specialized AI & ML services we deliver

Our AI practice covers the full spectrum from strategy through production deployment. Each service area has dedicated engineers with deep implementation experience.

AI Agent Development

Build autonomous agents that orchestrate multi-step tasks, call APIs, and make routing decisions inside your existing business workflows.

Generative Development

Add content generation, summarization, and creative AI features to your product using LLMs configured for your specific use cases.

Machine Learning Dev

Train, fine-tune, and deploy predictive models for classification, recommendations, and forecasting on your own data.

Data Science Services

Extract insights from your operational data with statistical analysis, feature engineering, and visualization that informs business decisions.

AI Integration

Connect LLMs, embeddings, and predictive models to your existing application with architectures that handle production traffic and costs.

AI Model Governance

Implement monitoring, bias detection, access controls, and compliance frameworks that keep your AI systems accountable and auditable.

RAG Development

Build retrieval pipelines that ground AI responses in your documents, databases, and knowledge bases instead of the model’s training data.

Computer Vision

Add image analysis, object detection, document processing, and visual inspection to your application with models tuned to your domain.

Deep Learning

Design and train neural networks for complex pattern recognition across text, images, audio, and structured data.

Industry-Specific AI Models

Deploy AI models trained on domain-specific data for healthcare, finance, legal, and other regulated industries with compliance built in.

LLMOps

Set up prompt versioning, evaluation pipelines, cost monitoring, and guardrails that keep your AI features reliable and affordable after launch.

FAQs about our AI & ML development services

Will adding AI break our existing application?

That’s the most common concern we hear. We integrate AI features into your existing codebase, auth system, and deployment pipeline rather than building a separate system you have to maintain alongside your product. Your CI pipeline, your database, your infrastructure — the AI feature ships through the same channels as everything else.

Why did our AI feature fail in production?

Demos don’t have real users, edge cases, or cost constraints. Production exposes latency under load, hallucinations on unexpected inputs, and API costs that scale faster than revenue. We audit what went wrong and rebuild the parts that can’t handle real traffic.

How do you keep AI costs from spiraling?

Caching frequent responses, selecting the right-sized model per task, compressing prompts to reduce tokens, and batching where possible. We architect so costs scale predictably with usage instead of surprising you on the monthly invoice.

Can our team maintain this after you leave?

That’s the goal. We build inside your repo, follow your conventions, document architectural decisions, and pair with your engineers on the patterns they haven’t used before. The codebase is yours and your team should be able to extend it without us.

Do we need to pick an LLM provider before engaging?

No. We evaluate providers against your use case, latency requirements, compliance needs, and budget. Some features need GPT-5.4, others work fine with a smaller open-source model at a fraction of the cost. We recommend after we understand the problem.

How do you handle AI hallucinations?

RAG pipelines that ground responses in your data, prompt engineering that constrains outputs, and evaluation frameworks that measure accuracy continuously. Hallucinations can’t be eliminated entirely, but we reduce them to levels your use case can tolerate and monitor for drift over time.

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.