Custom AI Agents & Chatbots
RAG-powered domain assistants and chatbots that actually work — not the demo kind.
· Reviewed by senior engineers
01 What it is
What this service is
A custom AI agent is software that uses a large language model to reason over your data, call your tools, and complete a task — answering customer questions from your knowledge base, drafting outbound emails from your CRM, generating reports from your data warehouse. A chatbot is the conversational interface in front of it.
Devinsta builds agents using Anthropic's Claude or OpenAI's GPT models, with retrieval-augmented generation (RAG) over your content, tool use / function calling for actions, and a structured prompt architecture that's testable and version-controlled.
02 What it's for
What it's for
You need an agent when an LLM is the right tool for the job and a chat UI is the right surface — customer-facing support, internal knowledge access, sales assistance, document drafting, code review. If you've tried ChatGPT for the task and seen 80% promise but 20% real value, an agent is the path to 95%.
The deal-breaker isn't the model — it's the data, the tools, the prompts, and the evaluation harness. We do all four.
03 How to use it
How to engage devinsta
Discovery establishes the use case, the data sources, and the success criteria (specific evaluations: "the agent must answer X correctly in Y% of cases"). We then build the agent, measure against the evaluations, iterate prompts and retrieval, and ship to a controlled audience for real-world tuning.
04 How to deploy
How we deploy it
Agents deploy as APIs or as embedded widgets. The chat surface can be your own (Intercom, Zendesk, your product) or a hosted one. Model calls go through a thin gateway that handles caching, fallback, rate limiting, and observability.
We maintain an evaluation suite — a set of representative inputs with expected outputs — that runs on every prompt or model change. If a change regresses any test, it doesn't ship.
05 What we provide
What you get from us
- Agent design with system prompts and tool definitions
- RAG implementation over your data (Pinecone, pgvector, Weaviate)
- Tool / function-calling integration with your systems
- Evaluation harness with regression tests
- Chat UI (embed, widget, or product integration)
- Observability and continuous improvement
- Ongoing prompt and retrieval tuning
FAQ
Common questions
Can the agent answer questions from our docs and our database?
Yes — that's what RAG does. We embed your docs into a vector database and let the agent retrieve relevant chunks before answering. For structured data (your customer database, order history) we use function calling to query directly.
What about safety and accuracy?
We design with guardrails — content filters, refusal patterns for out-of-scope questions, citation requirements ("every claim must cite a source"). And we run an evaluation suite weekly to measure regression.
Will users actually use it?
Only if it's good. Most chatbots fail because they're built and then ignored. We design with a real success metric (deflection rate, satisfaction, task completion) and tune until it hits target.