Spott and Kepler are chasing the same idea from opposite ends. Spott has built roughly twenty separate AI features, each with its own switch. Kepler has built one assistant that works across the whole database, and a matching engine we measure.
Spott is the broader product today and Kepler is the deeper one. Spott ships more modules — outbound campaigns, a BD pipeline, interview scorecards, a client portal, telephony — and pushes cross-record AI work out to ChatGPT or Claude through an MCP server. Kepler keeps the agent inside the app, where it can search across every candidate, email and call transcript you own and then act on what it finds, and it publishes one price with every feature switched on.
Last checked 6 Sep 2026 · Written from Spott's own public documentation and pricing pages. Tell us if we have something wrong and we will correct it.
Spott’s Ask AI is scoped to the record you have open; cross-record work goes out to an external chat app through their MCP server. Kepler’s assistant sits in a sidebar on every page, reads the whole workspace, and can create and update records once you approve.
Both products score candidates against natural-language criteria. Kepler shows a verdict and the evidence behind every single requirement, so a consultant can see why someone ranked where they did rather than trusting a number.
Kepler is USD 119 per user per month billed annually, with every feature included and unlimited records. There is no edition above it, and AI usage draws on an included monthly allowance.
Dimensions where Kepler is the stronger product. Spott is ahead of us on several others — campaigns, opportunities, scorecards, the client portal, telephony and mobile — and the blog post covers those in full.
| Kepler | Spott | |
|---|---|---|
| The assistant | ||
| Scope of the AI assistantWhether it can answer a question that spans records. | App-wide sidebar that reads the whole workspace and acts across records | Ask AI panel scoped to the open record; cross-record work goes to ChatGPT or Claude via MCP |
| Acting on an answerReading is half the job. | Creates and updates records, builds shortlists, saves a result as a list — behind an approval step | Answers questions about the record in view |
| What search readsThe corpus, not the ranking. | Candidates, CVs, notes, emails and call transcripts, semantic and keyword together | AI-ranked search over notes and attachments labelled as CVs |
| Matching | ||
| Evidence per requirementCan a consultant see the reasoning? | A verdict and the supporting evidence for every criterion, expandable under the row | A 0–100 score with a thumb per criterion |
| Measured qualityWhether anyone checks the ranker. | A standing evaluation programme with recall and precision gates and a cost ceiling per job | No published evaluation; docs note matching degrades above roughly 2,000 candidates in the filter pool |
| Trusting the record | ||
| Field-level historyWhat changed, who changed it, and from what. | Per-cell history with the source of every AI-suggested change, and undo | Activity panel shows the change and its source; no per-cell history or undo |
| Two people, one recordThe quiet data-loss bug in most CRMs. | Optimistic concurrency across resources, so a stale edit is refused rather than silently applied | Not described in their documentation |
| Automatic profile updatesApplied, or proposed for review? | Emails, calls and messages become proposed field updates you approve or reject | Scheduled profile refresh; no review surface is documented and history reports only Updated or Profile not found |
| Your careers page | ||
| Custom domainWhether the jobs rank for your brand. | Your own domain, server-rendered and indexable, with structured data per role | Branded pages hosted at app.spott.io/careers/ |
| Getting in and paying | ||
| MigrationWho does the unglamorous half. | Done for you on every plan — extraction, mapping, de-duplication, a sample of your own records to check | Import tooling and onboarding documentation |
| EditionsHow many decisions before you can buy. | One edition. Every feature included, unlimited records and storage | CORE and PRO seat plans, plus a separate credit balance for AI and enrichment |
| AI cost modelWhat happens when the team uses it. | An allowance is included; additional capacity is available on request | Metered credits per action — work email 1, personal email 3, phone 10, profile refresh 5 |
Spott’s Ask AI answers about the candidate you are looking at, and their documentation is explicit that cross-record agentic work belongs in ChatGPT or Claude over MCP. Kepler’s assistant is a sidebar on every page: it searches everything you own, shows the records it used, and can act on them once you approve.
| Candidate | Match | Role title | Current employer | Skills | |
|---|---|---|---|---|---|
| Sophia Bennett | 92Strong | Senior Data Engineer | Monarch Analytics | Python, Spark, AWS, Terraform, dbt | |
| Amir Hassan | 81Good | Data Engineer | Northwind Capital | Python, Spark, EMR, SQL, Airflow | |
| James Okafor | 74Good | Senior Data Engineer | Juniper Systems | Spark, Azure, Databricks, Kafka, Scala | |
| Mei Tan | 68Weak | Engineering Manager, Data | Atlas Cloud | AWS, Leadership, Python, Snowflake | |
| Elena Rossi | 55Weak | Analytics Engineer | Forma | dbt, SQL, Python, Looker | |
| Oliver Chen | 49Off spec | Platform Engineer | Northwind Capital | AWS, Kubernetes, Terraform, Go |
Assessment
A strong fit for the brief. Sophia has run a production Spark platform on AWS and has managed a team through a migration. Financial services exposure is via a payments client rather than a bank.
Criteria · 3 of 4
Decision
↑ ↓ next candidate · Esc close
Both products mine conversations for profile changes. The difference is what happens next. Spott’s scheduled refresh documents no review step and reports only whether a profile was updated. Kepler routes every change through a proposal you accept or reject, with the email, call or message it came from attached.
Three candidates · this week
Re: Senior Data Engineer at Northwind
Thanks for sending the details over. The role sounds great, and I can start on 3 October if the timing works for them.
Are you still keeping half an eye out?
I handed my notice in this morning, so I am actively looking now.
James · 11:26For my next move I am looking for £105,000. I would want a step up for a move.
| Candidate | Available from | Status | Expected salary | Notice period | Company | Owner | Skills | |
|---|---|---|---|---|---|---|---|---|
| Sophia Bennett | Not set | Passive | £90,000 | 1 month | Monarch Analytics | Priya Nair | ||
| Amir Hassan | 14 Oct 2026 | Passive | £85,000 | 2 weeks | Northwind Capital | Priya Nair | ||
| James Okafor | Immediate | New | £100,000 | 3 months | Juniper Systems | Tom Reilly | ||
| Mei Tan | Not set | Passive | S$160,000 | 2 months | Atlas Cloud | Tom Reilly | ||
| Elena Rossi | Not set | Active | €78,000 | 1 month | Forma | Tom Reilly | ||
| Oliver Chen | Immediate | New | £88,000 | None | Northwind Capital | Priya Nair | ||
| Rahul Menon | 2 Nov 2026 | Active | £92,000 | 1 month | Juniper Systems | Priya Nair | ||
| Yuki Tanaka | Not set | Passive | ¥14,000,000 | 3 months | Atlas Cloud | Tom Reilly | ||
| Hafiz Rahman | 20 Oct 2026 | Active | S$145,000 | 2 weeks | Meridian Partners | Priya Nair | ||
| Clara Nowak | Not set | New | €84,000 | 1 month | Forma | Tom Reilly | ||
| Daniel Okonkwo | Immediate | Active | £78,000 | None | Northwind Capital | Priya Nair | ||
| Ana Ferreira | 8 Dec 2026 | Passive | €96,000 | 3 months | Juniper Systems | Tom Reilly | ||
| Tomas Novak | Not set | Active | €71,000 | 1 month | Monarch Analytics | Priya Nair | ||
| Grace Adeyemi | 27 Oct 2026 | Active | £105,000 | 2 weeks | Atlas Cloud | Tom Reilly | ||
| Ivan Petrov | Not set | Passive | €88,000 | 2 months | Forma | Priya Nair | ||
| Leila Haddad | Immediate | New | £82,000 | None | Meridian Partners | Tom Reilly | ||
| Marcus Reid | 16 Nov 2026 | Active | £98,000 | 1 month | Monarch Analytics | Priya Nair | ||
| Nadia Kaur | Not set | Passive | S$172,000 | 3 months | Northwind Capital | Tom Reilly |
Kepler's CV parsing and formatting are ported from RemakeCV, our own product, which 300+ recruitment agencies use. The migration that fills your database on day one runs on an engine that has been reading real agency CVs for years, not on something written for this launch.
RemakeCV is a Kepler product. It is the same team and the same code, which is why we are willing to point at it — and it is the only outside evidence on this page, because Kepler itself is new and has no customer stories yet.
Whatever your current system gives you, plus any loose CVs and spreadsheets sitting in folders. If you are not sure how to get the export out, we will walk you through it.
Extraction, mapping, de-duplication and validation — the unglamorous half most vendors leave on your desk. Your custom fields are recreated rather than flattened into notes.
Fifty of your own records, fully parsed — your people, not a demo set. Tell us what we got wrong and we fix it and re-run, as many times as it takes.
The full database goes in, mailboxes and calendars connect, and history backfills onto the right records. We stay on it until your team is working in Kepler.
£89 per user / month, billed annually
Two seat plans, CORE and PRO, plus credits
Spott does not publish per-seat pricing on its website; the plan names and the credit model come from their documentation. AI and enrichment are metered separately from seats, with published per-action costs — a work email is 1 credit, a personal email 3, a phone number 10, a profile refresh 5 — pooled per workspace, carried over, and expiring after a year.
That model is a genuine strength for them: it lets expensive AI ship without eroding margin. It is also a second budget to forecast. Kepler puts the same class of work inside the seat price, with a flat add-on if a team wants to stop thinking about it entirely.
It depends on which half of the product you need first. Spott is broader — outbound campaigns, an opportunities pipeline, interview scorecards, a client portal, telephony integrations and a mobile app are all live, and Kepler has none of them. Kepler is deeper on the AI itself: one assistant that works across your whole database rather than a panel per record, matching with readable evidence behind every requirement, and per-field history with undo. An agency that runs on outbound sequences should look hard at Spott. An agency whose value is in a large database it cannot currently search should look hard at Kepler.
Yes. Spott’s Ask AI is available on candidate, job, application, opportunity and call records, and answers questions about the record you have open. For work that spans many records, Spott’s own documentation points you at their MCP server, which lets ChatGPT or Claude query your Spott data from outside the product. Kepler takes the other approach: the assistant is a sidebar inside the app, it reads the whole workspace, and it can write changes back once you approve them.
Kepler is USD 119 per user per month billed annually, which is USD 1,428 per seat per year, with every feature included, unlimited records, and free migration. Additional AI capacity is available on request. Spott does not publish per-seat pricing; their documentation describes CORE and PRO plans with a separate pooled credit balance for AI and enrichment actions, priced per action. You would need a quote from Spott to compare the totals.
Kepler has campaigns — multi-step email sequences with audiences, delivery windows, exit criteria and reply classification. Spott’s sequencing is more developed: they also ship manual steps that become assignable tasks, per-mailbox daily caps with send jitter, automatic mailbox warm-up, delegated sending and campaign templates. If sequencing is the centre of how your desk works, Spott is ahead of us on it today.
An assistant that works across the whole database from inside the product rather than one scoped to a single record; search that reads emails and call transcripts as well as CVs and notes; matching with readable per-criterion evidence, held to internal recall and precision gates before a ranker change ships; per-cell field history with undo and the source of every AI suggestion; optimistic concurrency so two people editing one record cannot silently overwrite each other; a careers site on your own domain rather than a subpath of the vendor’s; and one edition at one price with migration included.
Kepler runs the migration for you on every plan. You send an export from Spott plus any loose CVs and spreadsheets; we handle extraction, mapping, de-duplication and validation, rebuild your custom fields as fields rather than flattening them into notes, and show you fifty of your own records fully parsed before the full load. Activity, stages and history come across, not just names and email addresses.
Bring your hardest search to a 15-minute call. We will run it live, on real records, and talk through what a migration would look like.