AI Integration Services for Your Systems

Integrating a large language model into an existing application is a different engineering discipline from building a new AI product from scratch. The authentication architecture, the latency profile, the token cost model, the fallback routing and the output validation layer all need to be engineered for the system you’re connecting into, not the demo environment. We integrate OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral and AWS Bedrock models into web applications, mobile apps, enterprise platforms and custom software with the production discipline that turns a promising prototype into a feature users actually trust. Prompt versioning, semantic caching, streaming response handling, output validation and the cost monitoring that keeps your AI invoice predictable.

AI agents that can’t connect to your systems, your data and your workflows aren’t agents, they’re chatbots with ambition. We integrate AI agents into the operational fabric of your business: CRM platforms where agents qualify leads and draft follow-ups, ERP systems where agents monitor exceptions and trigger procurement workflows, communication platforms where agents handle tier-one support autonomously and internal tools where agents execute multi-step research and reporting tasks without constant human oversight. Tool integration, API orchestration, state management across agent steps, human-in-the-loop design for the tasks that need it and the observability layer that tells you what your agents are actually doing in production.

The richest business context your AI systems can access is already sitting in your CRM and ERP, locked behind data models your AI vendor’s demo never accounted for. We integrate AI capabilities directly into Salesforce, HubSpot, Microsoft Dynamics, SAP, NetSuite, Zoho and ERPNext using stable API architectures, careful field mapping and the change control discipline that keeps your existing workflows intact while AI makes them measurably smarter. Predictive lead scoring surfaced inside your CRM. Demand forecasting connected to your ERP’s inventory module. AI-generated summaries embedded in the deal and account records your sales team already lives in.

Retrieval-Augmented Generation is the pattern that makes LLMs useful over your proprietary data instead of generic internet knowledge. We design and build RAG pipelines that connect your knowledge base, your documentation, your product data and your operational content to the LLM layer cleanly: document ingestion and chunking strategy, embedding model selection, vector database deployment and management, hybrid search architecture, reranking for retrieval accuracy and the evaluation harness that measures whether your RAG system is actually returning the right content before it reaches users. Pinecone, Weaviate, Chroma, FAISS, pgvector and managed vector solutions across all three major cloud platforms.

AI systems are only as reliable as the data flowing into them. We design and build the data pipelines that keep your AI integrations fed with clean, timely and correctly structured inputs: data contracts that enforce schema consistency, validation layers that catch quality issues before they reach the model, ingestion pipelines from operational databases, data warehouses, SaaS platforms and streaming sources and the monitoring that alerts your team when data quality degrades before the model output does. Apache Kafka, Airflow, Spark, dbt and cloud-native pipeline tooling across AWS, Azure and GCP, matched to your existing data infrastructure.

Natural language processing, computer vision and document AI aren’t standalone products, they’re capabilities that generate value when they’re embedded directly into the applications and workflows where the work happens. We integrate NLP for text classification, entity extraction, sentiment analysis and semantic search into customer support platforms, content management systems and operational dashboards. We integrate computer vision for defect detection, quality control and visual inspection into manufacturing and logistics workflows. We integrate document AI for OCR, invoice processing, contract extraction and form automation into the back-office systems that still run on paper-based processes.

Predictive models that live in a data science notebook don’t change how your business operates. Predictive models surfaced inside the BI dashboards, CRM views and operational systems your teams already use every day do. We integrate forecasting models, churn prediction systems, demand planning engines and anomaly detection into Power BI, Tableau, Looker and custom analytics platforms so the AI insight is visible at the point of the business decision, not buried in a model output file your operations team never opens. End-to-end from feature pipeline to production model serving to the visualization layer your business users actually navigate.

Some AI decisions can’t wait for a round trip to the cloud. Defect detection on a production line, anomaly flagging on edge devices, real-time inference in field equipment and predictive maintenance on connected assets all need AI that runs at the edge, close to the data, with offline tolerance built in. We integrate compact ML models onto gateways, edge devices and embedded systems using TensorFlow Lite, ONNX Runtime and vendor-specific edge AI tooling, with MQTT and HTTPS sync back to central systems, over-the-air update capability and the ring buffer architecture that keeps your edge AI useful during network outages.

AWS Bedrock, Azure AI Studio, Google Vertex AI and SageMaker are powerful platforms that are also easy to over-engineer, over-provision and over-spend on without the right integration architecture. We integrate cloud AI platform capabilities into your existing applications and data infrastructure through consistent, abstracted interface layers that keep your workloads portable, your spend governed and your vendor optionality intact. Model selection across foundation model catalogs, inference endpoint management, cost monitoring wired into your FinOps practice and the multi-cloud AI architecture that doesn’t lock your product roadmap to a single platform’s release cycle.