Conversational analytics
Your team asks a question. Gigi answers in the chat.
Gigi is an AI analytics agent in Slack, Teams or WhatsApp. It runs on your own governed data foundation, so your team gets the answer straight away, without waiting in line.
Runs 24/7
Answers in under 2 minutes
Your metric definitions, enforced
Confidence score on every answer
# finance
SV
Sanne, Head of Finance 18:40
@Gigi what was net revenue per country last month?
Works where your team already talks
Slack
Microsoft Teams
Live demo
See Gigi in action
Business questions typed in Slack, clarifying questions asked back. Then the answer, with tables, sources and the confidence score attached.
1
Gigi asks first
It confirms which definitions it’s about to use before it answers.
2
Gigi answers in the thread
The numbers, in a table or chart, and the main takeaways.
3
Gigi shows its confidence
A score on every answer, with four checks behind it.
Where analytics time goes today
The same bottleneck, seen from both sides of the request queue.
Business owners
Hunting through dashboards
The right number is somewhere in the dashboard, and can’t be found.
Days to weeks for an answer
The answer arrives after the decision window has closed.
Decisions made without data
People fall back on intuition, or on their own version of the numbers.
Data team
Repeating questions
Answering the same, already answered questions every quarter.
Redundant execution
Rebuilding the same dashboard, exporting the same datasets.
Time spent on easy questions
Analysts spend time on lookups instead of complex analytical work.
Gigi
AI analytics agent by POINT05
The analytics agent that answers where your team already asks
Tag Gigi with a question in a shared channel or a direct message. It opens a thread and answers in about two minutes, with the numbers, a chart when needed, and the definitions it used.
No dashboard to open, no table names to remember and no request to file.
Questions get asked where the work happens
Business questions rarely start in a BI tool. They come up mid-conversation, while someone is deciding something. No new tool, no login, no context switch.
For most people that’s the difference between asking and guessing.
One language for the whole company
Gigi reads the definitions your data team already maintains. Revenue, active customer and margin mean what your company agreed they mean, every time.
When it’s ambiguous, Gigi asks which definition you want.
Minutes, not days
Gigi runs 24/7, so there’s no queue and nothing waits for someone’s morning. A question asked at 18:40 is answered at 18:42.
Answers arrive while the decision window is still open.
Ask in an open channel, or in a DM
Tag @Gigi and it answers where it was tagged: in a shared channel where the team should see it, or in a DM when you’d rather work it out alone first.
Nothing to route, nothing to set up per team.
It answers in a thread, and remembers
Every answer opens a thread, so the channel stays readable. Ask “and the month before?” and Gigi keeps the context.
Follow-ups work like a conversation.
On the record, when you want it to be
In a shared channel the question, the number and the reasoning stay visible, so the same question doesn’t come back every quarter.
Gigi handles the repeats, your data team gets the real work.
Built so you can act on the answer
An answer is only useful if you can trust it. Everything below exists to make the numbers in the thread safe to act on.
Shows its work
Asks first
Daily self-check
Charts with Chartbuddy
For every answer
Gigi shows its work
Gigi never returns a lone number. Each answer arrives with the finding in one line, the table underneath, and a full record of how it got there.
The finding first. The headline in a sentence, before the details.
Table and chart in the thread. No export, no separate dashboard.
Sources named. Which tables and models, and which filters were applied.
Conventions stated. Business rules mentioned explicitly, not assumed.
A confidence score. Graded on question clarity, scope, data coverage and thread health.
# ecommerce
Gigi APP 10:22
Repeat customers made up 58% of Q3 orders, 4 points more than Q2.
Segment
Q3 orders
Share
Repeat
14,210
58%
New
10,290
42%
Convention used: a repeat customer has 2+ completed orders in the last 12 months.
fct_orders
dim_customer
order_status = completed
High confidence
Four checks behind every score
Question clarity
95
Scope
90
Data coverage
84
Thread health
82
When the question is harder
Gigi finds the root cause
“Why did revenue dip in March?” can’t be answered with a glance at a dashboard. The cause could be a business decision, an external factor, or a comparison month that was itself unusual. Gigi works through it the way an analyst would, so nobody has to invent the questions, write the queries and paste results into a sheet.
1
Ask
Start high level: one question in Slack.
2
Clarify
Gigi confirms the measure and the comparison.
3
Hypotheses
It writes the follow-up questions itself and tests them in parallel.
4
Query
New SQL against your warehouse, not a pre-built dashboard.
5
Answer
The finding in Slack, the full analysis in a sheet.
SV
Sanne 09:14
@Gigi why did revenue dip in March 2026?
The better your data is configured, the deeper Gigi can dig. Clean models and clear definitions let Gigi test more angles, so the analysis goes further than a lookup. Not sure how your data measures up? Take the 5-minute quickscan.
Try Gigi
Ask one of these questions about a sample webshop to see how Gigi answers. On your own data, your team can ask anything.
Demo with sample data from a fictional webshop.
Also included
Everything around Gigi that keeps it accurate, affordable and maintained, without extra work for your data team.
Validation dashboard
Check results and accuracy, tracked over time.
Usage & value dashboard
Who’s asking what, and what it’s saving.
Automated data roadmap
Prioritised data gaps, generated from real questions.
Research suggestions
Topics that deserve a proper analysis, not a lookup.
Cost transparency
The model choice is yours, and token spend stays visible.
Security & privacy
Permissions, data residency and what Gigi can see.
Automated maintenance
Upkeep handled by us, not by your data team.
More on the way
Improved continuously, across every implementation.
Questions we often get
From business owners, CFOs and data leads weighing up Gigi.
Does Gigi replace our data team?
How do we know an answer is right?
Where does our data go, and what can Gigi see?
Which chat tools does Gigi work in?
How long does it take to get started?
What does it cost?
Want to see how Gigi can help your team?
We’ll show you Gigi on a realistic dataset and talk through what it would take on yours.