Less manual work. Faster decisions. Lower costs.

Permission-aware semantic search across 50,000+ SharePoint documents. Search time dropped from 12 minutes to 45 seconds, with zero permission violations in production.

We built a permission-aware semantic search layer on top of the existing SharePoint environment: SharePoint Graph API integration for document indexing, permission mapping, and metadata extraction Semantic vector search via Qdrant — natural language queries like "Find the contract template we used for government clients in 2023" Permission enforcement at query time — users only see results they are authorized to access, matching SharePoint's department-level access controls exactly Azure OpenAI embedding models for semantic understanding, with query expansion for better recall Built in TypeScript, deployed as a production system within the client's Microsoft ecosystem The permission-aware architecture accounted for roughly 40% of the total project effort — but it was non-negotiable for enterprise deployment.

Multi-tenant AI customer service platform for a Dutch energy retailer operating 20+ whitelabel brands. Each brand gets its own tone, knowledge base, and escalation logic — powered by a single LangGraph pipeline with Azure OpenAI and deep CRM integration.

We built a multi-tenant AI support platform where each brand operates as an isolated tenant with its own knowledge base, system prompt, tone of voice, and escalation rules — all running on one shared LangGraph pipeline backed by Azure OpenAI. Brand-aware LangGraph agent: incoming messages are routed through a stateful graph that loads the correct brand context, retrieves relevant knowledge (contracts, FAQ, policies), and generates responses matching that brand's tone and rules Deep CRM integration: the agent pulls real-time customer data (contracts, payment status, meter readings) to give personalized answers — not generic FAQ responses Multi-channel support: handles both email and live chat, with different response strategies per channel (structured email replies vs. conversational chat) Intelligent escalation: when the AI detects it can't resolve an issue (complaints, complex disputes, edge cases), it routes to a human agent with full conversation context, customer history, and a summary of what was already tried Brand onboarding workflow: new whitelabel brands can be configured with their own knowledge base, tone, and policies without code changes — just content and configuration The architecture follows our three-layer approach: Context (brand-specific knowledge retrieval and customer data), Reasoning (LangGraph agent with Azure OpenAI), and Action (CRM updates, email drafting, escalation routing). This keeps the system modular — we can swap models, update knowledge, or add channels without rebuilding the core.

Private AI platform for a global maritime engineering company. Open-source models running on-premise with Kubernetes — zero data leaves the building.

We built a private AI platform on their existing Kubernetes infrastructure, designed for air-gapped operation with zero external calls: Self-hosted open-source LLMs (Llama 3, Mistral) running on-premise via Kubernetes — no tokens leave the network Qdrant vector database for secure document retrieval, running as a stateful Kubernetes service with encrypted storage RAG pipeline with LangChain for engineering document search — technical manuals, project specs, and operational procedures PII redaction layer that strips personally identifiable information before any document enters the AI pipeline Full audit logging on every query and response, with compliance dashboards for the security team GitOps deployment pipeline so the client's DevOps team can maintain and extend the platform independently Honest trade-off: open-source models score roughly 85-90% of GPT-4 quality on domain-specific tasks, but with complete data control and predictable costs. For this client, that trade-off was straightforward.

A 4-week Proof of Concept showing how a RAG-powered agent can turn raw ESG data and CSRD requirements into consistent, audit-ready narrative sections — giving sustainability teams a solid first draft instead of a blank page.

We built a LangGraph agent with a Qdrant vector store containing the full ESRS standards and CSRD regulatory text. The agent takes structured ESG data (CSV/Excel exports) as input, retrieves the relevant disclosure requirements for each topic, and generates narrative paragraphs that reference the underlying numbers. Azure OpenAI (GPT-4o) handles the text generation, while the RAG pipeline ensures the output stays grounded in actual regulations rather than hallucinating requirements. During the 4-week PoC, we tested the agent on 8 ESRS topics (E1-E5, S1, G1, G2) using sample data. The generated drafts covered ~70% of required disclosures accurately on first pass. The sustainability team reviewed and edited the outputs — their feedback was that the agent got the structure and regulatory alignment right, while they focused on adding context and nuance that only domain experts can provide.

A 4-week Proof of Concept demonstrating how an AI agent can automatically screen clients against sanction lists and public sources, producing structured risk profiles that analysts can review and approve.

In a 4-week Proof of Concept, we built a multi-step LangGraph agent that takes a company name and jurisdiction as input, then autonomously queries EU/UN sanction lists, the Dutch KvK registry and open news sources. The agent uses Azure OpenAI (GPT-4o) for reasoning and Qdrant to store and retrieve the client's own risk framework — so the output follows their specific policies, not generic rules. The output is a structured risk profile with source citations, risk classification and flagged items requiring human attention. During the PoC we tested against 25 historical client dossiers — the agent correctly identified all previously flagged risks and surfaced three additional findings the analysts confirmed as valid. The analyst always makes the final call; the agent handles the legwork.