AI Copilot

Describe what you want in plain language and the built-in copilot proposes designs, mappings, portals and workflow changes for your approval.

Describe the outcome, review the proposal

STRAX includes an AI copilot that works in plain language. It knows your own estate: the systems you have connected, their tables and fields, your mappings, workflows and portals. Ask it which systems feed the customer table, why a job failed last night, or which fields still lack a description, and the answer concerns your installation rather than integration in general.

The copilot also builds. Tell it "Demo ISP needs a portal where customers see their Splynx invoices and their open Zendesk tickets", and it proposes the table designs, mappings, pages and workflow changes needed to deliver that. Larger pieces of work start as a written specification you can read, comment on and refine section by section before anything is built.

Nothing is applied without your approval

Every change the copilot produces arrives as a reviewable proposal. You see exactly what it intends to change, accept it or reject it, and only an accepted proposal is applied. The copilot cannot alter your integration behind your back, and the review step is recorded, so there is always an answer to who approved what and when.

This matters because AI output is fast and plausible rather than guaranteed. The most common error in any AI-assisted integration is a sensible-looking mapping onto the wrong column, and a human review catches it in seconds. STRAX makes that review a built-in gate rather than a discipline teams must remember to apply.

Suggestions where the work happens

Beyond conversation, AI assistance runs through the routine work of integration.

Field mapping suggestions propose column pairs between a source system and your canonical model, shown beside the schema they apply to so each is easy to judge, with batch acceptance for the obvious ones.
Table mapping suggestions propose which source tables correspond to which hub tables.
Description suggestions draft plain-language documentation for systems, tables and fields.
Log analysis reads a failed job's output and explains the likely cause in plain terms.

All of these land in the same review queue, pending a person's acceptance or rejection. Mapping a newly connected system that once took days of column-by-column work becomes an afternoon of review.

The AI suggestions queue: copilot proposals for queries, tables, workflows and portals, each accepted by a person before it was applied.

Bring your own model

The copilot runs on the language model you choose. STRAX supports the major AI providers, and it equally supports a private model hosted inside your own network, which suits organisations whose data governance rules keep AI traffic on premises. Model choice is a configuration setting, so you can change provider without changing how you work. Portals can carry their own separately configured assistant for end users, restricted to exactly the capabilities the portal's builder permits.

Better descriptions, better answers

The copilot draws on what you have written about your estate. A system described as "Splynx: billing, source of truth for invoices" produces markedly better proposals than one left blank, so the documentation the platform encourages you to keep pays for itself twice.

Where this fits

The copilot accelerates every capability described in this section, from connecting systems to portals and workflows. For the platform as a whole, see What is STRAX?, or request a quote to see it working on a real estate.