AI integration & automation
We put large language models to work inside real products: assistants grounded on your data, agents that call your systems, and automation that removes manual steps. Built with evaluation and guardrails from day one.
What we do
- Assistants and copilots grounded on your data (retrieval-augmented generation).
- Agents that call your internal APIs and tools to complete multi-step work.
- Document understanding: extraction, classification, and summarization pipelines.
- Workflow automation that removes repetitive manual steps from operations.
- Evaluation harnesses, guardrails, and monitoring for AI features already in production.
How we work
AI features fail in production for boring reasons: no evaluation set, no fallbacks, no monitoring. We treat an LLM like any other unreliable dependency and engineer around it. Discovery first, so we only build where AI genuinely pays off; then a fixed-scope milestone with measured quality, not a demo.
Stack
Anthropic and OpenAI APIs, self-hosted models where data requires it, Laravel and Node.js backends, PostgreSQL with pgvector, queue-based pipelines.
Questions we hear
What kinds of AI integrations do you build?
Chat assistants and copilots grounded on your own data, document processing and extraction, agents that call your internal APIs, and workflow automation. We work with hosted providers such as Anthropic and OpenAI as well as self-hosted models.
How do you keep AI features reliable?
Every integration ships with an evaluation set, guardrails on inputs and outputs, fallback behavior, and monitoring, so quality is measured rather than assumed.
What does an engagement look like?
A short paid discovery to identify where AI genuinely pays off, then a fixed-scope first milestone you can put in front of users. We stay for iteration or hand over cleanly.
Have a project in mind? Write to hello@longisoft.com