AI Technology Stack

LangChain: The Intelligence Layer

LangChain is the open-source orchestration framework that connects large language models to your data, tools, and business processes. It handles the plumbing between the AI model and everything it needs to be useful: your databases, APIs, document stores, and internal systems.

When you need an AI agent that can search your knowledge base, reason about the results, call an external API, and generate a structured response, LangChain is what wires all of that together. Its extension, LangGraph, adds stateful multi-step workflows where agents can loop, branch, and maintain context across complex sequences of actions.

LangChain has surpassed 90 million monthly downloads and is trusted by organisations including Cisco, LinkedIn, Klarna, JPMorgan, Workday, and Replit. It is model-neutral, meaning we can connect it to our private locally-hosted models for sensitive workloads or to frontier models from Anthropic, Google, and OpenAI when the task demands it.

Example use case: A customer support agent that takes an inbound query, searches your internal knowledge base using retrieval-augmented generation, checks the customer's account status via your CRM API, and generates a personalised response grounded in your actual data. Single agent, single interaction, completed in seconds.

CrewAI: The Collaboration Layer

Some tasks are too complex or multi-faceted for a single AI agent to handle well. CrewAI sits on top of LangChain and enables multi-agent collaboration, where specialised agents each with their own role, expertise, and tools work together on a shared objective.

Rather than building one agent that tries to do everything, CrewAI lets us define a team. A researcher agent gathers information. An analyst agent interprets the findings. A writer agent produces the final output. CrewAI manages the delegation, sequencing, and communication between them, ensuring each agent contributes its specialist perspective to a higher quality result.

Example use case: Automated due diligence on a potential acquisition target. A financial analyst agent pulls and interprets accounts data from Companies House and financial databases. A legal agent reviews filings and checks for outstanding litigation or regulatory issues. A market research agent assesses the competitive landscape and sector trends. A report writer synthesises everything into a structured output ready for human review. Each agent has different tools and domain focus, and CrewAI coordinates the entire process.

Temporal: The Reliability Layer

Temporal is not an AI framework. It is a workflow orchestration engine for distributed systems that solves the hardest problems in production deployment: retries when something fails, timeouts, state persistence across long-running processes, exactly-once execution guarantees, and coordination across multiple services.

AI agents in production fail regularly. API rate limits, model timeouts, transient network errors, and processes that span hours or days all create fragility. Temporal wraps around the AI layer to ensure that if step three of a ten-step workflow fails at 2am, it retries from step three rather than starting over. Nothing gets dropped, nothing gets duplicated, and the system recovers automatically.

This is what separates a demo from a production system. When your AI workflows need to run unattended over extended periods with guaranteed completion, Temporal provides the durability and reliability that makes that possible.

Example use case: An automated regulatory compliance workflow that monitors incoming regulatory updates, uses AI to analyse whether they affect your business, drafts updated policy documents, routes them for human review and approval, then files the necessary responses. That process might span days or weeks. The AI reasoning handles the analysis and drafting. Temporal ensures the entire pipeline is durable, that nothing gets lost if a service restarts, and that human review steps can pause the workflow indefinitely and resume exactly where they left off.

The Full Stack: How They Work Together

Each technology solves a different problem. Together, they form a complete production AI platform.

LangChain provides the intelligence, connecting AI models to your data and enabling individual agents to reason, retrieve, and act. CrewAI provides the collaboration, coordinating teams of specialist agents to tackle complex multi-faceted tasks that benefit from different perspectives. Temporal provides the reliability, ensuring that entire workflows complete successfully regardless of failures, timeouts, or processes that span days or weeks.

Example use case: An end-to-end deal pipeline for business acquisitions. Temporal orchestrates the overall workflow across weeks or months, tracking each stage from initial screening through to completion. CrewAI manages multi-agent teams performing financial analysis, legal review, and market assessment at each stage. LangChain powers each individual agent's ability to read documents, query databases, and generate outputs. If the legal review agent hits a rate limit overnight, Temporal retries it cleanly without losing the financial analysis that already completed. Human decision points pause the workflow until approval is given, then execution continues automatically.

This layered approach means we build AI systems that are not just intelligent, but collaborative and resilient. The result is technology your business can trust to run in production, not just perform well in a demonstration.

Conclusion

Intelligent agents. Specialist collaboration. Production-grade reliability. One platform.