We integrate OpenAI GPT-4, Anthropic Claude, and Google Gemini into your business applications via secure RAG architecture — so your AI works on your private data, inside your secure environment, without any data leaving your network.
Six specific pain points where AI delivers the fastest, most measurable return.
GDPR, HIPAA, or internal data policy prevents sending proprietary data to public AI APIs. Private deployment via Azure OpenAI or on-premise models gives you the same capability — inside your firewall.
Public LLMs don't know your products, your policies, or your customers. RAG architecture retrieves relevant context from your private data at query time — answers are specific to your business.
Naive LLM integrations send entire documents as context — burning tokens unnecessarily. Proper RAG retrieval reduces token usage dramatically while improving answer quality.
Response latency matters in production. Proper caching, streaming, and retrieval optimization reduces perceived latency to under 1 second for most queries.
A response from an LLM is only valuable if it connects to action. We build the integration layer that routes LLM outputs into your CRM, ERP, support desk, or custom application.
Which queries are being answered well? Where is it failing? What's the cost? Production LLM integrations need observability dashboards — not just a live endpoint.
Not vague AI promises. Specific systems, integrated with your existing tools, with ROI scoped before any development begins.
We deploy OpenAI models inside your Azure tenant or on-premise infrastructure. Zero data leaves your network. Same GPT-4 capability — completely private. Suitable for GDPR, HIPAA, and enterprise security requirements.
We build the full RAG pipeline: document ingestion, chunking strategy, embedding generation, vector database, retrieval logic, and context injection. Your private data becomes the LLM's knowledge base.
REST API, SDK integration, streaming responses, function calling, tool use — we integrate the LLM into your existing application architecture. Your product, with AI inside it.
Beyond Q&A — LLM agents that use tools: search your CRM, raise a ticket, query a database, trigger a workflow. AI that takes actions, not just generates text.
Production dashboards showing query volume, latency, token cost, retrieval quality, and answer accuracy. Alerts when cost exceeds budget or accuracy falls below threshold.
GPT-4 for complex reasoning. Claude for long documents. Haiku / GPT-4o Mini for high-volume classification. We design the right model mix for your use case — balancing capability, speed, and cost.
These are live systems we've built for clients. Specific scenarios, specific results.
Full RAG pipeline built on your private data — product docs, support history, policies, CRM data — with OpenAI GPT-4 deployed in your Azure tenant. Zero data egress. Answers specific to your business.
LLM reads incoming documents — invoices, contracts, reports, forms — extracts structured data, classifies document type, and routes to the right business system. AI that reads like a human, at machine speed.
Replace keyword search with natural language understanding. Users ask questions in plain English and get precise answers from your database, documentation, or knowledge base — with source citations.
An LLM agent equipped with tools — search CRM, query ERP, raise ticket, send email, update record — that takes multi-step actions in response to natural language instructions.
Not every task needs GPT-4. We design architectures where each stage uses the most cost-effective model: fast cheap models for classification, powerful models for generation, embedding models for retrieval.
AI that reads your data and writes the narrative — weekly business summaries, board pack commentary, customer health reports, compliance filings — in your house style, from your actual systems.
We deploy OpenAI models inside your Azure tenant, AWS account, or on-premise infrastructure. No data egress to public APIs. Full GDPR and enterprise security compliance from day one.
GPT-4, Claude 3.5, Gemini, Llama, Mistral — each has different strengths for different tasks. We design the right architecture for your use case, not the one we know best.
Building a RAG demo is easy. Integrating it reliably into a production application with proper error handling, latency management, cost monitoring, and security is hard. That's exactly what we do.
Every integration we build includes proper observability, cost monitoring, retry logic, fallback handling, and security controls. No demos dressed up as production systems.
All integration code, RAG pipeline configuration, and documentation is yours on completion. We hand over everything — no lock-in, no ongoing license dependency on us.
All three include NDA before day one, ISO 27001 certified process, and ROI modelled before any development commitment.
Every engagement starts by understanding your specific situation — not by proposing technology. ROI is scoped before any code is written.
We map your current process, identify the top opportunities, and model the ROI — before any commitment.
Architecture, data flows, integration plan — reviewed and approved by your team before development starts.
Built into your existing stack via secure APIs. Tested against real data before go-live. Zero disruption.
Live with performance dashboards. As your needs grow, the solution scales — no additional resource required.
You want AI features in your product but your team doesn't have LLM integration experience and you can't afford to learn on a production system. We build the integration properly, hand it over with full docs.
You know what you want to build but need integration engineering resource to connect LLMs to your existing data infrastructure. We build the RAG pipeline, observability, and application layer.
You want to adopt AI but your security team won't allow data to leave the network. Private LLM deployment inside your Azure or AWS environment gives you full AI capability with zero data egress.