LLM Integration

Integrate large language models into your existing enterprise systems — not as a toy chatbot, but as a core part of your product and operations. We handle the hard parts: prompt engineering, rate limiting, fallback chains, cost optimization, and production reliability at scale.

Discuss Integration
75+
Enterprise Integrations
99.9%
Uptime SLA Maintained
50%
Avg Cost Optimization
<200ms
P95 Response Latency

LLM Integration Services

Production-grade LLM integration with enterprise reliability, security, and cost controls

API Integration & Abstraction

We build a unified API layer that abstracts away provider differences — seamlessly switch between OpenAI, Anthropic, Google, and open-source models without changing application code. Includes automatic failover, load balancing, and cost routing.

Prompt Engineering & Management

We design, test, and version-control production prompts. Our prompt management system includes A/B testing, regression suites, prompt versioning with rollback, and analytics showing which prompts perform best for each use case.

Security & Guardrails

Comprehensive security layer: PII detection and redaction before data reaches the LLM, output filtering for harmful/inappropriate content, injection attack prevention, rate limiting per user/team, and complete audit logging of all interactions.

Cost Optimization & Routing

Intelligent request routing that sends simple queries to cheaper/faster models and complex ones to capable models. Token usage monitoring, caching for repeated queries, and cost dashboards broken down by team, feature, and model.

Streaming & Real-time Integration

Build real-time AI experiences with streaming responses, WebSocket integration, and server-sent events. We handle the complexity of partial response rendering, error recovery mid-stream, and graceful degradation.

Monitoring & Observability

Full observability stack: request/response logging, latency tracking, error rate monitoring, model performance dashboards, cost analytics, and alerting. Know exactly how your LLM integration is performing at all times.

Why Integrate With Us

We've built LLM integrations that serve millions of requests per day

Multi-Provider Abstraction

Enterprise Security First

Optimized for Low Latency

Cost Visibility & Control

Automatic Failover Chains

Production-Tested at Scale

SaaS Product — AI Features Ship in Weeks, Not Months

A project management SaaS wanted to add AI features: smart task suggestions, meeting summary generation, automated status reports, and natural language project queries. Their team had experimented with ChatGPT's API but couldn't get consistent, reliable results in production.

We built an LLM integration layer that includes prompt templates for each feature, context assembly from their database, output parsing and validation, streaming for real-time features, and fallback chains (Claude → GPT-4 → GPT-3.5). The abstraction layer means their product team can now ship new AI features in 1-2 weeks instead of 2-3 months. Total LLM cost per user: $0.12/month.

SaaS Product — AI Features Ship in Weeks, Not Months

Enterprise Search — "Ask Your Data" for 50,000 Employees

A Fortune 500 company wanted employees to query internal knowledge bases, policies, and documentation in natural language — like having a company-wide expert available 24/7. Previous attempts with keyword search and basic chatbots had low adoption because answers were unreliable.

We integrated LLMs with their document management system using a RAG pipeline: documents are chunked, embedded, and stored in a vector database. User queries retrieve relevant chunks, which are assembled into a context window with source attribution. We added citation linking so every answer shows exactly which document and paragraph it came from. Accuracy hit 94% on their internal benchmark, and monthly active users grew to 35,000 within 3 months of launch.

Enterprise Search — "Ask Your Data" for 50,000 Employees

Healthcare Platform — HIPAA-Compliant AI at Scale

A telehealth platform needed LLM integration for clinical note generation, symptom triage, and patient communication — all under strict HIPAA compliance. No PHI could ever reach third-party model providers, and every AI interaction needed to be auditable.

We built a HIPAA-compliant LLM gateway: PII detection strips patient identifiers before any API call, responses are re-personalized on the return path, all interactions are logged to immutable audit storage, and the entire pipeline runs within their HIPAA-compliant AWS environment. For the highest-sensitivity use cases, we deployed self-hosted open-source models. The platform now processes 100K+ AI-assisted interactions monthly with zero compliance incidents.

Healthcare Platform — HIPAA-Compliant AI at Scale

LLM Integration FAQ

All major providers: OpenAI (GPT-4o, GPT-4o-mini), Anthropic (Claude 4.5), Google (Gemini), Meta (Llama 3.x), Mistral, Cohere, and any model on Hugging Face. We also deploy self-hosted models for clients with strict data residency requirements. Our abstraction layer means you can switch providers without changing application code.
We build multi-provider failover chains: if your primary model is down or slow, requests automatically route to a backup provider. We implement request queuing, retry logic with exponential backoff, circuit breakers for degraded services, and health checks. Most of our integrations achieve 99.9%+ uptime even when individual providers experience outages.
Multiple strategies: intelligent routing sends simple requests to cheaper models, semantic caching avoids redundant API calls, prompt optimization reduces token usage, batching combines multiple requests where possible, and we provide per-feature cost dashboards so you can see exactly where your money goes. Typical savings: 40-60% versus naive implementation.
Yes. We use RAG (Retrieval-Augmented Generation) to ground LLM responses in your specific data without fine-tuning. Your data stays in your database/vector store — only relevant snippets are sent to the LLM as context. For maximum security, we can deploy self-hosted models so no data ever leaves your infrastructure.
Our security layer includes: input sanitization to prevent prompt injection attacks, output filtering for harmful or inappropriate content, PII detection and automatic redaction, rate limiting per user and per team, token budget caps, and complete audit logging. We follow OWASP LLM security guidelines and regularly test against known attack vectors.
Single-feature integration (like AI-powered search or content generation): $25-50K. Multi-feature AI layer for an existing product: $60-120K. Enterprise-grade LLM platform with security, monitoring, and multi-provider routing: $100-200K. We always start with a 2-week technical assessment ($8-12K) to evaluate your architecture and recommend the optimal approach.

Ready to Add AI to Your Product?

Let's evaluate your use case and design an LLM integration architecture that's reliable, secure, and cost-effective. Start with a free technical consultation.

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