Nova AIS Assistant

The copilot that speaks your ontology.

Nova AIS Assistant is the natural-language layer inside Nova AIS. Ask anything about your data, your workflows, or your agents, and it answers from your ontology, not a generic model. Then it can invoke the agent that does the work, and hand you a receipt you can audit.

Who it is for

Where it lives

Not a chatbot bolted onto a page. The language layer of the platform.

The Assistant sits on top of every Nova AIS workflow, built on the same ontology architecture that runs the rest of the platform. Builders and end users both reach the same data through it, without learning the platform first.

See the platform it lives inside, Nova AIS

One copilot, two kinds of user

For the builder, and for the person at the desk.

A copilot that knows your platform

Ask in your data, and it can invoke the agent that does the work. No SQL, no training week.

  1. Ontology queries

    Ask which loans flagged for review this quarter, in plain language. It pulls from your objects, relationships, and events and returns rows you can act on.

  2. Multi-agent invocation

    It calls the phone, docs, collections, or lab agent on your behalf and returns the result inline. No SQL and no platform training required.

  3. Workflow drafting

    Describe a pipeline that scans new docs and flags failed checks, and it returns a runnable spec you can review before it ships.

  4. An audit trail

    Every question, every answer, and every agent call is logged with the model and prompt context, so you can show how any result was produced.

Skip the platform tour

An end user, a lab tech, a call-center agent, a planner, asks in plain Tagalog or English and gets an answer already in their data.

  • Plain-language input: ask for yesterday's critical results and it is done.

  • Code-switches the way Filipino users actually speak, mid-sentence.

  • Contextual to the surface: it knows whether it is looking at the dashboard or the lab.

  • A receipt for every answer: click to see the query and the model behind it.

What ships today

Already running, not a roadmap.

Each of these is live inside the platform now. The schools edition opens as a separate pilot in Q3 2026.

  1. Knows your objects

    It loads your ontology on the first request. Ask which patients had a critical result this week and it reads the actual Patient object, not a generic model's guess.

  2. Invokes the right agent

    Phone, docs, collections, lab. It routes the request to the agent that does the work and returns the result inline, in the same thread.

  3. Speak data, get answers

    Plain language to query to answer, across call transcripts, lab samples, collected accounts, and scanned documents. One query language for the whole platform.

  4. Same copilot, every surface

    It lives inside the dashboard, the lab view, and the platform itself. The context shifts with the surface; the engine underneath is one.

  5. English, Tagalog, Cebuano

    Native, not translated. It code-switches mid-sentence the way people actually speak, tested on call-center, lab, and planning workflows.

  6. A receipt for every answer

    Click any answer to see the underlying ontology query and the model that produced it. Logged for retention and review.

Ask the Assistant your own question.

Tell us the workflow you would want to ask about. We set up a sandboxed account, walk you through three real queries on your kind of data, and you decide if it earns a place in your stack.

Book a 20-minute call

Twenty minutes on a video call. We listen, you talk, we figure out together whether this is worth doing.

No slides, no demo, no pitch deck. You leave with a clearer sense of the shape and what it would take.

  • Tell us what is on fire and what is working, briefly.
  • We will ask a few specific questions about your stack and team.
  • You will get a clear yes, no, or referral by the end of the call.

Before you go

Want a free website mockup?

We will build a free mockup of your new site, no charge and no commitment, so you can see exactly what it would look like before you decide anything.