AI integration services
What does AI integration services cover?
AI integration services that put useful intelligence into the software you already run.
We integrate models, retrieval, and automation into products you already run. A model call returns a shape the system can store, and a failed or partial answer has a path that does not pretend it succeeded.Discuss this capabilityThe work
A model call can fail. The product still has to.
TripGlide's assistant reads a group and the weather, then writes the day. That holds only if the model returns a shape the app can store, and if a bad answer has somewhere to go. We put a schema, an eval set, and a fallback in the path before a prompt is treated as the product. The same rule covers retrieval, a workflow, and an MCP server in front of a system you already operate.
Best for Product teams adding AI to a live system, internal operations, and knowledge workflows.
Core capability
- AI integration into existing apps
- Retrieval and knowledge systems
- Agentic and automated workflows
- Evaluation and guardrails
What moves forward
- Feasibility evidence
- Integrated workflow in your product
- Operable production system
How we approach it
The call is allowed to fail.
- 01
Bind the model to a schema
Tool calls and extractions return structured output. The application rejects a payload that does not match. A free-text answer does not become a row, a booking, or a payment.
- 02
Keep an eval set next to the prompt
Prompts are versioned against a golden set for that task. A change that fails the set does not ship. We do not treat a single good demo as evidence.
- 03
Name the fallback
A timeout, a partial answer, or a low-confidence result goes to a person or a smaller path. The user sees that state. The workflow does not invent a success.
- 04
Put a person on the risky branch
Drafting a reply, routing an order, or clearing an invoice can run ahead of a person. Sending money, deleting a record, or talking to a customer waits for an explicit approval. Retrieval filters stay beside the operational data.
Technology choices
A capable stack starts with the constraints.
These are technologies we can bring to this work when they fit the product, team, and operating environment.
How we choose technology- OpenAIOpenAI integration for evaluated product capabilities and automation.
- Anthropic ClaudeClaude AI integration for reasoning and knowledge workflows with explicit controls.
- n8nn8n automation across the tools a team already uses.
- LangChainLangChain development for retrieval and tool-using model workflows.
- Node.jsNode.js development for APIs, integrations, and event-driven services.
- PostgreSQLPostgreSQL development for relational foundations of operational products.
- PythonPython development for APIs and data jobs that should not sit inside a click.
AI integration services
Make the next decision concrete.
Share the outcome, constraints, and current system. We’ll help identify the right starting shape.