AI Integrations (Frontier Models)
Seamlessly integrate cutting-edge AI models into your existing applications and workflows. From GPT-5 to Claude to Gemini - we connect you to frontier intelligence.
Services
Integration Services
LLM API Integration
Integrate OpenAI GPT-5, Anthropic Claude, Google Gemini, and other frontier models into your applications with proper error handling and fallback strategies.
Vision & Multimodal AI
Implement image analysis, document understanding, OCR, and multimodal processing using GPT-5 vision, Claude Sonnet 5 vision, Gemini 3, and specialized vision models.
Speech & Audio AI
Voice-to-text, text-to-speech, real-time transcription, and audio analysis using Whisper, ElevenLabs, and cloud speech services.
RAG & Knowledge Systems
Build retrieval-augmented generation systems - vector databases, embedding pipelines, semantic search, and knowledge base integration.
Workflow Automation
AI-powered workflow automation - document processing, email triage, content generation, data extraction, and intelligent routing.
Custom AI Pipelines
End-to-end AI pipelines combining multiple models - chains, routers, evaluators, and orchestration for complex multi-step AI workflows.
What We Do
What Production AI Integration Actually Requires
Integrating a frontier model into a product is straightforward in a prototype. The API call works, the output looks right, and the demo is convincing. The engineering challenges that separate a prototype from a production system are reliability, observability, cost control, and security. Model providers have rate limits, occasional outages, and latency that varies under load. Responses that look correct in testing fail in edge cases that only surface at scale. Token costs that are acceptable in development become significant at production volumes. These are engineering problems that require deliberate architecture, not configuration.
RAG system architecture decisions made during integration have long-term consequences that are expensive to reverse. The vector database selection determines query performance and cost at scale. The chunking strategy for source documents determines retrieval quality: chunks too large retrieve irrelevant content, chunks too small lose context. The embedding model determines semantic search quality and has implications for cost and latency. The access control model at the retrieval layer determines whether users can access only the documents they are authorized to see. These decisions interact with each other and with your existing data architecture in ways that require understanding both the AI systems and the infrastructure they connect to.
Security considerations are specific to how each integration is implemented. LLM API keys with broad permissions stored insecurely are a common credential exposure vector. Model outputs passed to downstream systems without sanitization create injection risks. User input passed directly to model APIs without filtering creates prompt injection exposure. Sensitive data sent to third-party model providers without review of data handling terms creates compliance risk, particularly for organizations handling PHI or regulated financial data. We design integrations with these controls addressed in the architecture rather than identified after the integration is already in production.
Models
Frontier Models We Work With
OpenAI
Anthropic
Open Source
Production and Compliance
AI Integration for Production Reliability, Observability, and Compliance
Reliability and Fallback Architecture
Production LLM integrations require reliability architecture that accounts for provider rate limits, latency variability, and occasional outages. Retry logic with exponential backoff handles transient failures without cascading into user-facing errors. Fallback routing to secondary providers or cached responses maintains availability when primary providers are degraded. Circuit breakers prevent a slow or failing model provider from blocking unrelated application functionality. Request queuing manages burst traffic that would otherwise exhaust rate limits. We design reliability architecture that fits your availability requirements and traffic patterns rather than applying defaults that work in development but fail under production load.
Observability and Cost Control
AI integrations without observability become black boxes that are difficult to debug, optimize, and cost-manage. We instrument integrations with logging that captures inputs, outputs, token consumption, latency, and error conditions at each model call. Cost tracking at the feature and user level makes AI spend visible and controllable before token costs become significant. Prompt versioning and A/B testing infrastructure enables systematic quality improvement without manual comparison. Evaluation frameworks that run against your actual production scenarios rather than curated benchmarks give you an accurate picture of how model quality changes with provider updates and prompt modifications.
Compliance in Regulated Environments
Organizations in healthcare, financial services, and government integrating frontier models face data handling obligations that apply to what is sent to the model, not just what the model returns. PHI sent to a third-party model provider requires a Business Associate Agreement and compliance with HIPAA minimum necessary principles. Financial data sent to model providers may trigger regulatory obligations depending on the data classification. We review data flows in AI integrations against your compliance obligations before integration design is finalized, identify which data categories can and cannot be sent to specific providers, and design input filtering and data minimization controls that satisfy your regulatory requirements while preserving the functionality the integration is designed to deliver.
FAQ
Common Questions About AI Integration
How do we choose between GPT-5, Claude, and Gemini for our use case?
Model selection depends on your specific use case requirements rather than general rankings. For long-context tasks requiring deep reasoning over large documents, context window size and reasoning quality are the primary selection criteria. For tasks requiring consistent structured output, instruction following fidelity and function calling reliability matter most. For high-volume, latency-sensitive applications, cost per token and response time at scale determine viability. For regulated environments, data handling terms and provider compliance documentation affect which providers are available to you. We evaluate candidate models against your specific tasks using production-representative inputs rather than public benchmarks, because benchmark performance frequently does not predict performance on domain-specific workloads.
What is a RAG system and when does an application need one?
Retrieval-augmented generation is an architecture pattern that retrieves relevant documents from a knowledge base and provides them as context to a language model before it generates a response. Applications need RAG when they require the model to answer questions about content that was not part of its training data: your internal documentation, your product catalog, your customer records, or any domain-specific content that changes frequently enough that fine-tuning is not practical. RAG is also appropriate when you need the model to cite specific sources rather than synthesizing from general knowledge, and when you need to control what information the model can access for a given user or query through retrieval-layer access controls.
Can we send customer data or PHI to a third-party model provider?
Whether you can send specific data categories to a model provider depends on the provider's data handling terms, your regulatory obligations, and the sensitivity classification of the data involved. OpenAI, Anthropic, and Google each offer API terms that address data retention, training use, and security controls, and each offers enterprise agreements with stronger data handling commitments. For PHI, a Business Associate Agreement with the provider is required before any PHI can be transmitted. For PII under GDPR or CCPA, data processing agreements and data residency requirements apply. We review your specific data categories and regulatory obligations against provider terms before integration architecture is finalized, rather than discovering compliance gaps after the integration is already processing production data.
How do we manage model API costs at scale?
Token costs that are manageable during development frequently become significant at production scale. Cost management starts with instrumentation: every model call should log token consumption so spend is visible by feature, user segment, and request type before it becomes a surprise on a provider invoice. Prompt optimization reduces token consumption without degrading output quality by trimming context that does not contribute to response accuracy. Caching semantically similar queries avoids redundant model calls for requests that could be served from prior results. Model routing sends simpler requests to smaller, cheaper models and reserves frontier models for tasks that genuinely require their capability. Spending limits and alerting at the provider and application layer prevent cost surprises during traffic spikes or runaway usage patterns.
What is the difference between fine-tuning and RAG, and when should we use each?
Fine-tuning adapts a base model's weights by training it on domain-specific examples, which can improve performance on tasks that require a specific output style, format, or domain vocabulary that is difficult to specify through prompting alone. It does not give the model access to new factual knowledge: a fine-tuned model still only knows what was in its training data and fine-tuning dataset. RAG gives the model access to specific documents at query time without modifying the model's weights, which is appropriate when you need the model to answer questions about content that changes frequently or that must remain current. Fine-tuning and RAG are not mutually exclusive: fine-tuning can improve how a model uses retrieved context, and RAG provides factual grounding that fine-tuning alone cannot supply.
Insights
Related Articles
Book a Call
Integrate AI Into Your Stack
Book a consultation to explore which AI models and integrations will drive the most value for your business.
Schedule a consultation
Choose a convenient time for a free 30-minute consultation.