Most of analytics work is not the clever model. It is the hour you spend reshaping a messy export, the second hour reconciling why two dashboards disagree, and the recurring Monday where you rebuild the same metrics deck from a fresh CSV. The thinking is fast. The plumbing is slow, and it never stops arriving.
Here is the part that matters, and the reason a normal chatbot is dangerous for this job: a plain language model guesses at arithmetic. Ask it for month-over-month growth across 40,000 rows and it will hand you a confident number that is quietly wrong, because it is predicting text, not adding columns. This agent does not do that. It runs real Python in a sandbox over the file you upload. When it tells you Q3 revenue was $1.84M and grew 12.3 percent, it got there by loading your CSV with pandas, summing the actual rows, and showing you the code it ran. You can read the code. You can rerun it. The number is computed, not invented.
That single difference changes what you can trust it with. It can clean a column of inconsistent dates, build a cohort retention table, score customers by RFM, and draw the chart, all on your real data. For the parts that are judgment rather than calculation, like how to define an active user or whether a dip is seasonal, Keimodel lets you ask Claude, GPT, and Gemini the same question side by side and keep the answer that reasons best, instead of trusting one model's first take. Set your metric definitions in Memory once so every analysis uses the same denominators, then put the recurring report on a schedule and stop rebuilding it by hand.
Capabilities this leans on
Do this first. Locked definitions mean every analysis below uses the same metrics, the same fiscal calendar, and the same exclusions, so numbers reconcile across chats.
Remember how my company defines its core metrics so every analysis is consistent. We are a B2B SaaS company on a calendar fiscal year. Revenue means recognized MRR, not bookings. An active account is one with at least one login in the trailing 28 days. Churn is logo churn unless I say revenue churn. Always exclude internal test accounts where the email domain is @ourcompany.com or the plan is 'comp'. Report currency is USD. When you compute anything, show the Python you ran and state the row count you used.
Upload the export and ask for the numbers; it runs the math in Python instead of guessing.
Here is orders.csv with columns order_date, account_id, amount, plan, status. Compute monthly recognized revenue for the last 12 months, month-over-month growth percent, and trailing-3-month growth. Exclude refunded rows (status = 'refunded') and test accounts per my Memory rules. Show the Python you ran and the final table.
Now break that same revenue out by plan tier and tell me which tier is driving the growth and which is flat or shrinking. Give me the percentages, not adjectives.
Sanity check yourself: re-run the total a second way (sum by account, then by month) and confirm the two totals match. If they do not, tell me where they diverge.
What you get: A revenue and growth table you can defend in a board meeting, with the code and row counts shown so anyone can verify it.
Turn a flat event log into a cohort triangle without writing the SQL yourself.
Using signups.csv (account_id, signup_date) and activity.csv (account_id, event_date), build a monthly retention cohort table: rows are signup month, columns are months since signup, cells are the percent of that cohort still active that month. Use my 28-day active definition. Output the triangle as a table.
Which signup cohort retained best and which fell off fastest? Give me month-3 and month-6 retention for each cohort as a clean list.
Plot the retention curves for the last six cohorts on one chart and give me the image, with a one-line read on what the shape is telling me.
What you get: A retention cohort triangle and curve chart built from raw events, the kind of thing that usually takes an analyst an afternoon in SQL.
Score every customer on recency, frequency, and monetary value so marketing knows who to target.
From transactions.csv (customer_id, order_date, amount), compute an RFM score for each customer: recency in days since last order, frequency as order count, and monetary as total spend. Quintile each dimension 1 to 5 and give each customer a combined RFM segment.
Label the segments in plain English: champions, loyal, at-risk, hibernating, and new. Tell me how many customers and how much revenue sit in each.
Export the at-risk segment as a CSV of customer_id and email so I can hand it to our email tool.
What you get: Every customer scored and bucketed, plus a ready-to-send list of who is slipping away while they are still worth saving.
Real data is never tidy; have it standardize the file first and tell you what it found.
This CSV is messy: dates are in three formats, amounts have dollar signs and commas, the country column mixes 'US', 'USA', and 'United States', and there are duplicate rows. Clean it: parse all dates to ISO, convert amounts to floats, standardize country to ISO codes, drop exact duplicates, and flag any rows with missing required fields. Tell me exactly what you changed and how many rows each fix touched.
Show me the 10 rows you could not confidently clean so I can decide what to do with them.
Save the cleaned version and give me a short data-quality note I can paste above the analysis.
What you get: A standardized file plus an honest log of what was wrong, so your analysis is not built on quietly broken inputs.
Once the data is computed, get the visuals without opening a notebook.
From the monthly revenue table you computed, make a clean bar chart of revenue by month with the growth percent labeled on each bar. Neutral colors, no chart junk, large readable labels. Give me the image.
Now a stacked area chart of revenue by plan tier over the same months so I can see the mix shift.
Give me a two-sentence caption for each chart that states the takeaway, not just what the axes are.
What you get: Presentation-ready charts generated from your real numbers, with captions that say what they mean.
When the question goes past your data, have it search the web and bring back something sourced.
Search for current B2B SaaS retention and net revenue retention benchmarks for companies in our revenue range, and summarize where good, median, and weak fall. Cite each source with a link and a date, and skip anything older than 18 months.
Compare our month-6 cohort retention from the analysis above against those benchmarks. Are we ahead, at, or behind median? Be specific.
Write a six-bullet summary I can drop into a deck: three on our numbers, three on how we stack up, every external claim sourced.
What you get: A sourced read on how your metrics compare to the market, with our internal numbers and the outside benchmarks in one place.
Scheduled tasks rebuild the report on a cadence and a connection drops it where the team already is.
Every Monday at 7am, recompute weekly active accounts, new signups, churned logos, and recognized MRR versus the prior week using my standard definitions, and send me a short digest with the deltas and one line on anything that moved more than 10 percent.
Save this whole weekly metrics routine as a reusable Skill called 'Weekly metrics report' so I can also run it on demand mid-week.
(From Slack) Run the weekly metrics report now for the last 7 days and post the summary here.
What you get: A metrics report that writes itself every Monday and lands in Slack, plus a saved Skill you can fire any day you need a pulse check.
Bookkeeping & finance
Categorize transactions, run margin and runway math, chase invoices, and close the month.
Software developers
Write and debug code it actually runs, compare how Claude, GPT, and Gemini solve the same problem, and review diffs.
SaaS founders
Landing copy, changelogs, lifecycle emails, support macros, and churn analysis.
Open the Agent, paste any prompt above, and change the details to fit your business.