AI Integration for Existing Applications
Add AI-powered features to your product with integrations that handle real traffic, not just demos.
👋 Talk to an AI integration expert.
Trusted and top rated tech team
AI features that work beyond the prototype
Adding AI to your product sounds simple until the chatbot hallucinates, the API costs spike, and latency makes the feature unusable. We integrate LLMs, embeddings, and predictive models into existing applications with architectures that handle production load, manage costs, and deliver results users actually trust.
Our capabilities include:
- LLM API integration and prompt engineering
- RAG pipelines with vector databases
- Chatbots and conversational interfaces
- Predictive features using existing data
- Computer vision and image analysis
- AI cost optimization and caching strategies
Who we support
Getting AI to work in a demo is the easy part. Keeping it fast, affordable, and reliable in production is where most teams get stuck. We help you get past the prototype and ship AI features that actually hold up at scale.
Product Teams Adding AI Features
Leadership wants AI in the product but nobody on your team has integrated LLMs before. You're evaluating APIs, reading about embeddings and RAG, and trying to figure out what actually fits your use case without overbuilding.
Teams With AI That Failed at Scale
Your AI feature worked well in demos, but moving to production has been challenging. Real-world use revealed edge cases, latency issues, and rising costs. What once impressed stakeholders is now creating friction for customers.
Teams With AI That's Too Slow or Expensive
Your AI features are functional, but each API call incurs costs and increases latency. To optimize for scalability and efficiency, consider implementing caching, refining prompt design, or exploring alternative architectures.
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 AI integration?
Our engineers have integrated LLMs, embeddings, and predictive models into production applications. We understand prompt engineering, vector database selection, and the cost/latency tradeoffs that make AI features sustainable. You get AI that works at scale, not a prototype that impresses once.
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.
AI integration capabilities for production applications
LLM API Integration
RAG Pipeline Development
Chatbot & Conversational AI
Prompt Engineering & Optimization
Predictive Feature Integration
Cost & Latency Optimization
Tools and technologies for AI integration
LLM APIs & Providers
Our engineers integrate AI providers with proper error handling, rate limiting, and fallback strategies for production reliability.
- OpenAI API — GPT-4 and GPT-3.5 models for text generation, embeddings, and function calling with structured output support
- Anthropic Claude API — Claude models for long-context conversations, analysis, and tasks requiring nuanced reasoning
- Azure OpenAI Service — Enterprise deployment of OpenAI models with Azure security, compliance, and regional availability
- AWS Bedrock — Managed access to foundation models from Anthropic, Meta, Cohere, and others with AWS infrastructure integration
- Google Vertex AI — Gemini models and PaLM APIs with Google Cloud integration for enterprise AI deployments
- Cohere — Enterprise-focused models for embeddings, reranking, and retrieval-augmented generation with multilingual support
Vector Databases & Embeddings
Curotec builds RAG pipelines using vector databases that store, index, and retrieve embeddings for grounded AI responses.
- Pinecone — Managed vector database for similarity search with fast indexing, filtering, and real-time updates at scale
- Weaviate — Open-source vector database with hybrid search, GraphQL API, and built-in vectorization modules
- Chroma — Lightweight embedding database for prototyping and production RAG applications with simple Python integration
- Qdrant — Vector search engine with filtering, payload storage, and on-premise or cloud deployment options
- pgvector — PostgreSQL extension for vector similarity search that adds embeddings to existing relational databases
- Milvus — Open-source vector database built for billion-scale similarity search with GPU acceleration support
Orchestration & Framework Tools
We use orchestration frameworks that chain prompts, manage context, and coordinate multi-step AI workflows efficiently.
- LangChain — Framework for chaining prompts, managing memory, and building multi-step AI workflows with Python and JavaScript support
- LlamaIndex — Data framework for connecting LLMs to external data sources with indexing, retrieval, and query engines
- Semantic Kernel — Microsoft’s SDK for integrating LLMs into applications with prompt templating and plugin architecture
- Haystack — Open-source framework for building RAG pipelines, semantic search, and question-answering systems
- Flowise — Low-code tool for building LLM workflows with drag-and-drop interface and LangChain integration
- Instructor — Library for structured output extraction from LLMs with Pydantic validation and type-safe responses
Caching & Cost Management
Our developers implement caching layers and optimization strategies that reduce API costs without sacrificing response quality.
- Redis — In-memory cache for storing prompt responses, embeddings, and session state with fast retrieval and TTL support
- GPTCache — Semantic caching layer that matches similar prompts to cached responses, reducing redundant API calls
- Prompt Compression — Techniques like LLMLingua that reduce token counts while preserving semantic meaning for cost reduction
- Portkey — Gateway for managing multiple LLM providers with automatic fallbacks, caching, and spend monitoring
- LiteLLM — Unified interface for calling 100+ LLM providers with cost tracking, retries, and budget management
- Cloudflare AI Gateway — Edge caching and rate limiting for AI API calls with analytics and request logging
Conversational AI Platforms
Curotec builds chatbots using platforms that handle conversation state, intent recognition, and multi-turn interactions.
- Botpress — Open-source platform for building chatbots with visual flow editor, NLU, and multi-channel deployment
- Rasa — Framework for building contextual AI assistants with custom NLU, dialogue management, and on-premise deployment
- Dialogflow — Google’s conversational AI platform with intent recognition, entity extraction, and integration with Google services
- Amazon Lex — AWS service for building chatbots with speech recognition, natural language understanding, and Lambda integration
- Microsoft Bot Framework — SDK for building bots with Azure integration, adaptive dialogs, and multi-channel connectors
- Voiceflow — Collaborative platform for designing conversational experiences with visual canvas and team workflows
Monitoring & Observability
We configure monitoring that tracks prompt performance, token usage, latency, and response quality across AI features.
- LangSmith — LangChain’s platform for tracing, debugging, and evaluating LLM applications with prompt versioning and test datasets
- Weights & Biases — ML experiment tracking with prompt logging, evaluation metrics, and collaboration features for AI development
- Helicone — Observability platform for LLM usage with request logging, latency tracking, and cost analytics dashboards
- Arize AI — ML observability for monitoring model performance, detecting drift, and troubleshooting production AI issues
- Datadog LLM Monitoring — APM integration for tracking LLM latency, token usage, and error rates alongside application metrics
- OpenLLMetry — Open-source observability framework using OpenTelemetry standards for tracing LLM calls across providers
FAQs about our AI integration services
Which LLM provider should we use?
Depends on your use case. OpenAI for general-purpose tasks, Claude for long-context and nuanced work, Cohere for enterprise embeddings. We evaluate latency, cost, and output quality against your specific requirements before recommending.
How do you handle AI hallucinations?
RAG pipelines that ground responses in your data, prompt engineering that constrains outputs, and validation layers that catch obvious errors. Hallucinations can’t be eliminated entirely, but they can be reduced to acceptable levels.
Can you integrate AI into our existing application?
Yes. We add AI features to existing codebases without requiring a rewrite. This includes API integration, database changes for embeddings, and frontend components for chat or AI-powered interfaces.
How do you manage LLM costs?
Caching frequent queries, choosing appropriately sized models for each task, optimizing prompts to reduce tokens, and batching requests where possible. We design architectures where costs scale predictably with usage.
What's a RAG pipeline?
Retrieval-augmented generation. Your documents get converted to embeddings and stored in a vector database. When users ask questions, we retrieve relevant chunks and include them in the prompt so the AI responds using your data, not just its training.
How long does AI integration take?
Simple integrations like adding a chatbot take weeks. Complex RAG systems with custom pipelines take a few months. We scope based on your existing architecture and what the AI features need to do.
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.
