The answer your team is still waiting on, and the move it implies.

Marketing, CRM, eCommerce, BI and growth ask in plain language. aiRA reads the warehouse you already have, answers with the reasoning shown, and builds the segment or the journey that answer calls for. Nothing reaches a customer until a person approves it.

The aiRA workspace: a thread on the left showing the question “Show me customer insights for April 2026”, the schema discovery and SparkSQL it ran, and a versioned headline-KPI artifact; on the right, the Customer Insights dashboard it produced, with a spend chart and a cohort table carrying a suggested action per cohort.

What it does

aiRA does not reply. It builds you something.

Not a report, and not a chat. The answer arrives with the reasoning that produced it and the move it implies, built against your own data and held for a decision.

One thread

The person with the question is the one who gets the answer.

No ticket, no queue, no analyst in between. Marketing, CRM, eCommerce, BI and growth ask in their own words and get the answer back in the same sitting, which is the difference between a question worth asking and one that waits for the next sprint.

Your business context

No modelling project before the first answer.

Industry playbooks ship pre-built and your own business context layers on in days, against the two to four months of modelling a semantic layer needs before it can answer anything at all.

The move

Everywhere else, the answer is where it ends.

A chart, a narrative and an export, and turning any of it into a campaign is a separate brief for a separate team. aiRA builds the segment and drafts the journey in the same thread that answered the question, and holds them there until a person approves.

Its working

You can check the method, not just the picture.

The query it ran, the assumptions it made and the documents it drew on all come back with the answer. An analyst can audit the reasoning, and the person who asked does not have to take the number on trust.

Your domain

It knows what a points programme is.

Not a text-to-SQL layer waiting to be told what loyalty means. aiRA reasons in the vocabulary the business already uses: points programmes, allocation and expiry, cohorts, lapse windows and the offers that move them.

How it fits

aiRA sits between your
data and your channels.

You already own the warehouse. You already own the channels. What is missing is the judgement in between, and today that runs on analyst tickets and Monday meetings.

System of record

Databricks Snowflake Google BigQuery Amazon Redshift

Reads where your data already sits

aiRA

The intelligence layer

System of action

Braze Adobe Salesforce Marketing Cloud Capillary Engage+

Executes anywhere, in any channel

  • Zero copy

    aiRA reads your data in place. No migration, no rebuild.

  • Nothing to rip out

    It plugs into what you already own and pushes to what you already run.

  • A person approves

    Every move waits on your approval before it goes live.

Security

Your data stays where it is, and nothing writes without you.

  • The model is Anthropic Claude on AWS Bedrock, running inside your own region, stateless and with zero retention. Capillary never trains on brand data.
  • Four layers before the model sees a row: database access control, RBAC scoping, masked-by-default views and payload truncation.
  • Retrieval is tenant and brand isolated, so cross-brand analysis is architecturally impossible rather than disallowed by policy.
  • Queries run SELECT-only in a sandbox with no outbound network, and every customer-impacting write stops at a person, on an audit trail.
  • SOC 2 Type II
  • CCPA
  • GDPR

Also ISO 27001 and PCI DSS certified, and PDPA compliant. HIPAA-capable under a BAA. Aligned to the NIST AI Risk Management Framework and the EU AI Act.

Getting started

Live on your own data in two to four weeks.

Four phases with a named output each, run by a Capillary success manager. Your team is needed in the first and the third.

  1. Phase 01

    Business requirements

    Your business context, your tiers and the questions your team actually asks.

    OutputA signed-off requirements document

  2. Phase 02

    Training and testing

    aiRA is pointed at your warehouse and trained on your context. Read access is all it needs.

    OutputA configured instance on your data

  3. Phase 03

    Brand testing

    Around thirty representative questions, checked against your own numbers until the gap closes.

    OutputA parity report you sign off

  4. Phase 04

    Go live

    Seats get access, the approval gate is wired to the people who own it, and the credit ledger starts.

    OutputaiRA live for your teams

The parity gate

99% agreement, minimum

aiRA is not switched on for a brand until it reproduces your own reporting at least that closely, so the first answer your team sees is one you can check against a number you already trust.

The question your team is still waiting on.

Thirty minutes, one real question, answered live. We reply within one business day with a time and the security brief.

Book a working session