KEPLER FEATURES

Ask your database a question.
Get the shortlist — or the change — done.

It doesn't just find answers. It files the note, moves the stage and sets the reminder, as cards you approve.

Book a free demo See how it works ↓

Assistant 4,912 candidates indexed

who did we speak to about data roles in Manchester last quarter?

working search_candidates aggregate_records

14 people. Ranked by most recent contact — Mia Tanaka was yesterday, on the Northwind intake call.

Data roles — Manchester — last quarter 14 candidates Save as list

CandidateSpoke toLast contactStage

Mia TanakaAisha RahmanCall · yesterdayInterviewing

Paul OkaforTom MercerEmail · 02 JulScreening

Jonas BergEllie FoxCall · 27 JunScreening

Showing 3 of 14Open in Candidates

next Suggested follow-up: who of these is already on the Northwind Capital shortlist?

Move Mia Tanaka to Interviewing Applied · 09:41

ApplicationMia Tanaka — Senior Data Engineer, Northwind Capital

StageScreening → Interviewing

Written to the record as you · via Assistant · in Mia Tanaka's history

Under the hood

How it works.

It finds what was never typed in

Keyword search dies at the name field. Kepler reads the unstructured stuff — notes, CVs, call transcripts, emails — with hybrid retrieval, so the words people actually said find the people they were about.

led a team through a migration

note

Sarah Whitfield

“…she led a team through a migration at Ardent — full re-platform, on time…”

Mia Tanaka

“…led the migration of our trading platform across three teams…”

transcript

Jonas Berg

“…yeah, I moved the whole team onto the new stack — that was the migration…”

Searches the words people actually used

"Led a team through a migration" surfaces the CV that says exactly that — and the ones that say it differently in a note or a transcript.

14 candidates match. Want them as a list?

Northwind Capital6

Ardent Systems5

Everywhere else3

Counts before it lists

Ask for a number, get a number — then the breakdown behind it. The list only happens when you want one.

Mia Tanaka

“Head of Data Platform, Ardent Systems — re-platform lead, 9 yrs”

email · 12 mar call · 24 min cv

Every answer shows its sources

Each result carries where it came from — the email, the call, the CV — so you can defend the shortlist to your client.

It does the admin, as proposals

When the answer is a change, the assistant doesn't apply it. Every edit comes back as a card — sourced, reversible, applied only when you confirm.

Move Mia Tanaka → Interviewing?

StageScreening → Interviewing

from today's call Reject Confirm

Changes arrive as cards

Move a stage, set a field, create a task — each proposed on its own card, applied only when you confirm.

who's interviewing this week for @Northwind Capital?

@ pulls the record — person, job or company

@-mention any record

Pull a person, job or company straight into the question. The mention resolves to the record — not to a guess.

CV sent — Senior Data Engineer, Northwind Capitalby you · Tue

Stage changed to Interviewingby you · via Assistant · just now on the record

Written where it belongs

Confirmed changes land as you, in the record's own history — not in a side channel nobody audits.

Big asks, handled end to end

Real asks aren't one command — they're a morning's work. Long runs stay inspectable, results become reusable lists, and every answer inherits exactly what you're allowed to see.

Re-score 214 candidates background run

Pull everyone against the specdone

Score against the rubric133 of 214

Draft the shortlist summaryqueued

Pause resumes where it stopped

Runs you can pause

Big asks — "re-score everyone against this spec" — progress step by step in the background, and stop the moment you want a look.

Data roles — Manchester — last quarter 14 candidates

Save 14 candidates as list

List created — findable in Lists

Saves answers as lists

Any result set becomes a reusable list in one click, so the work compounds instead of evaporating.

Answer scoped to your access

Six candidates match. Two are withheld — records you don't have access to.

reads as you · writes as you · never around you

Inherits your permissions

It sees what you see and writes as you — never around you. Confidential records stay confidential.

The data boundary

It reads what you can read.
It writes nothing without you.

The assistant lives inside the permissions you already have. That's the whole trick — and the whole boundary.

What it reads: the records, notes, CVs, emails and call transcripts you can already open. Nothing else, nothing further.
What it never does: write without a card. Every change is a proposal you confirm — nothing lands on its own.
What it never does: train on your data. Your database answers your questions. It doesn't become anyone else's.

Questions,
answered

What recruiters ask before they trust an assistant with the database.

What can the AI assistant actually do in my CRM?

Two things. It answers questions about your database in plain English — across notes, CVs, call transcripts and emails, not just profile fields — and it does the admin those answers imply: moving a stage, setting a field, filing a note, creating a task. Answers cite their sources and changes arrive as cards you confirm.

Does it search CVs and call transcripts, or only the fields someone filled in?

Everything you can already open. Kepler uses hybrid retrieval over the unstructured material, so a phrase like "led a team through a migration" surfaces the CV that says exactly that — and the ones that say it differently in a note or a transcript.

Can it change records without me?

No. Every edit comes back as a card — sourced, reversible, and applied only when you confirm it. Confirmed changes are written as you, in the record’s own history, not in a side channel nobody audits.

Can it see records I’m not allowed to see?

No. The assistant inherits your permissions exactly: it reads what you can read and writes as you, never around you. Confidential records stay confidential, and your data is never used to train AI models.

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