Custom LLMs, RAG systems, and AI agents engineered for your specific workflows, not generic tools hoping to fit.
Generic AI doesn’t know your processes, terminology, or edge cases. Every answer needs correction.
Pre-built tools don’t connect to your existing systems. Data stays siloed, workflows stay manual.
You need to own the model behavior, not rent someone else’s. Your IP, your rules, your infrastructure.
Fine-tuned models that speak your industry’s language and understand your specific domain knowledge.
Your documents, your knowledge, instantly accessible. AI that answers from your actual data, not guesswork.
Autonomous workflows that handle complex multi-step tasks, make decisions, and execute across systems.
Customer-facing AI that actually resolves issues, not deflects. Trained on your support knowledge.
1
Map your workflows and identify high-impact AI opportunities
2
Design the technical approach, data strategy, and integration points
3
Develop in sprints with continuous stakeholder feedback
4
Production deployment with monitoring and ongoing refinement
Enterprise-grade document ingestion with layout-aware parsing, OCR, table extraction, and metadata structuring — turning PDFs, contracts, and forms into structured, agent-ready data.
Dockling | Unstructured | Azure Doc Intelligence | Pydantic v2
Stateful, graph-based agent orchestration with checkpointing, conditional routing, and retry logic.
Workflow automation via LLM-enhanced pipelines and custom orchestration for complex agent topologies.
LangGraph | LangChain | N8N | Pydantic v2
High-performance async APIs for agent surfaces and tool endpoints. Built on open standards for agent-tool connectivity (MCP) and multi-agent coordination (A2A).
FastAPI | MCP Protocol | A2A Protocol | gRPC
Flexible vector and relational storage. Hybrid retrieval (vector + keyword + metadata filters) over pure similarity search for enterprise RAG.
Chunking strategies tuned per document type.
pgvector | Qdrant | Weaviate | Redis | Milvus
We support both containerised (Kubernetes) and cloud-native managed deployments: Azure Functions, App Service, and AI Foundry; AWS Lambda, ECS, and Bedrock; GCP Cloud Run and Vertex AI. The right model is chosen per workload — Kubernetes for portability, managed services for speed and cost efficiency.
Kubernetes | Azure Functions | App Service | Foundry | AWS Lambda | ECS | Bedrock | GCP Cloud Run | Vertex AI | Docker
Challenge A global Medical Affairs consulting partner needed answers from complex, multi-source datasets spanning CRM records, field interactions, and qualitative feedback. But even simple questions
Challenge A global food supply chain compliance provider had no structured way to extract usable data from the high volumes of PDFs its clients submitted
Challenge One of Europe’s largest retailers faced a growing operational bottleneck: fragmented exception handling that drained engineering resources and slowed incident response. Monitoring tools were
Challenge A U.S.-based bank, processing thousands of financial transactions daily, faced a major operational bottleneck: the manual handling of roughly 2,500 monthly exception transactions, 80%
Automation rate in banking exception handling
Down from 2 hours for document processing
30-minute technical discovery call. We’ll assess feasibility and outline the path forward.
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