AI assistants & agents

What it is

An AI assistant that answers from your knowledge and acts inside your tools — retrieval-grounded, permission-aware and measurable.

Why businesses need it

Support queues and internal search swallow hours every week. An assistant only earns its cost when it is correct about *your* product, not generically plausible.

What MIWEBX delivers

• Customer-facing support and pre-sales assistants
• Internal knowledge assistants over docs, tickets and wikis
• Task agents that draft, classify, route or update records
• Retrieval-augmented generation over your own corpus
• Human handoff and escalation paths built in, not bolted on

Core features

• Chunking, embedding and retrieval tuned to your document shapes
• Citations back to the source for every answer
• Guardrails: refusal behaviour, topic limits, PII handling
• Tool calling with explicit permissions per agent role
• Conversation quality review loop with graded examples
• Cost and latency budgets tracked per conversation

Technical capabilities

• Model access sits behind one internal interface, so a provider swap is a config change rather than a rewrite
• Retrieval grounding returns the passage the answer came from, so the UI can cite it
• Evaluation harness: a graded question set replayed on every prompt, model or index change
• Refusal and out-of-scope behaviour written as tested cases, not left to a fallback
• Tool calls executed with scoped credentials, separate from the end user's own permissions
• Token and cost budget enforced per conversation, with context trimmed before the request leaves the service
• Request and response logging with personal data redacted and a defined retention window

How we build it

• Find the question or task asked often enough to be worth automating
• Scope which sources the assistant may read and which it may never touch
• Prototype retrieval against real documents before any interface work
• Grade draft answers on a reviewed example set and tighten grounding where they fail
• Wire tool actions with an approval point on anything that writes to a system
• Monitor failures, handoffs and cost per conversation, re-running the eval on each change

Technology stack

• OpenAI APIs
• Anthropic APIs
• Python
• TypeScript
• PostgreSQL + pgvector
• Node.js
• FastAPI

What you receive

• The assistant, deployed with its own environment and keys
• Ingested knowledge base with a documented refresh process
• Evaluation set and scorecard for answer quality
• Guardrail and escalation configuration
• Admin view of conversations, failures and cost

Business benefits

• First-line questions answered without a person in the loop
• Answers consistent across agents instead of per-person
• Institutional knowledge stops living in one employee's head
• Support capacity grows without linear hiring