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Stop reading CVs,
start deciding.

GoTeam reads hundreds of applications end to end, scores every one against the same rubric and puts the candidate's own sentence next to each score.

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89 percent of a CV score is computed from the CV in code

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Wherever the application comes from

Candidates upload a PDF through the application form. On the Kurumsal plan, DOCX, ODT and scanned files in a Google Drive folder are converted to PDF and land in the same pool. The source format never changes the score.

PDF
DOCX (on Drive import)
LinkedIn export
Google Drive (Kurumsal)
Talent pool

From application tracking to interview questions, from document collection to employee management, eight modules sit in the same panel. One engine runs underneath all of them: the evidence pulled out of a CV is scored against the position criteria and shown together with its reasoning.

Five dimensions, one rubric. No item without a quote can ever reach the score.

Evidence Score

Every score is tied to a sentence in the CV. An item without a quote never reaches the score. A gap where no evidence was found is not held against the candidate, it is taken out of the calculation.

Position Fit

A score from zero to one hundred, using the position's own weights. The gate on a required criterion does not open until someone writes a reason. You set the weights, and the total has to add up to exactly one hundred before you can save.

Candidate Ranking

The pool is ranked against the position. You see not only the ranking but the reason behind it. When a new position opens, the same pool is assessed again and past candidates are never lost.

Search the pool
in plain sentences

Type instead of building filters and ask your question in natural language. The system scans the whole historical pool by meaning, catching context rather than keyword matches. Every matching candidate comes back with the sentence from their own CV.

Every position
sits in one panel

Every position you open stays in the list as Active, Draft or Closed. You write the criteria, give each one a weight, and you cannot save until the total is exactly one hundred. The gate on a criterion you marked as required does not open until someone writes a reason.

No black box. A CV goes in, evidence comes out. Scroll and follow it step by step.

The CV goes in

The PDF the candidate uploads is read end to end; what is taken is the text itself, not the page layout. An input gate first confirms the file really is a CV, and on an empty or unrelated file the run stops before any credit is spent. This check runs by itself on every upload, with no extra setting to switch on.

It is broken into parts

Skills, roles, tenure, education and languages are extracted as separate fields. Every piece stays tied to the sentence it appears in, so which fact came from which line can be shown at any moment. Nothing is cut loose from its sentence and interpreted freely; the evidence chain for the next steps is built right here.

It goes through the same rubric

Five dimensions are measured with the same fixed rubric; 89 percent of the score is computed from the CV in code, so the model invents no numbers. An item whose quote cannot be verified never reaches the score, which is why an instruction hidden in a CV cannot move it. For position fit you set the criteria weights, and nothing is saved until they total exactly one hundred.

It comes back with reasons

The result is not a single number but a reasoned list: next to every score sits the CV sentence that produced it. An item with no evidence drops out of the denominator and is not counted against the candidate. The items left open turn into interview questions specific to the position, and the last word always belongs to a person; the AI never changes a status.

The rest of hiring sits in the same panel too. Ten modules, each with a view from the product.

M.K. · Senior Frontend Developer
AI suggestionStrong fit88

Interview-ready

All three required criteria were matched with a literal quote from the CV; no evidence was found for team management.

Model doesn't change status
Status
PendingReviewingInterviewAcceptedRejected

AI only suggests; status changes are applied with human approval.

Only an authorized user changes status, and every change is logged with who and when.

One engine fits three ways of hiring. What changes is the scale, not the evidence rule.

Team scale

For HR teams

Instead of opening hundreds of CVs one by one you get a ranked list, and the reason for each place sits right next to the score. You query the pool in natural language and revisit past candidates for a new position. Pre interview questions come out automatically from the gaps in a candidate's CV. Every decision is written to the audit log, so who changed what and when always stays visible.

Single opening scale

For founders and small businesses

If you are a small company opening a single position, your own branded careers page goes live without writing any code. CVs are read in the background as applications arrive, so the score and the reasoning are ready before you open the panel. A candidate who misses a required condition is not thrown out; they drop to the bottom of the list with the reason, and you can open the gate yourself by writing one. The Mini plan starts with a single job post, and you stay in the same panel as you grow.

Hundreds of applications

For high volume hiring

When hundreds of applications land at once, not one slips through: all of them are read against the same rubric and ranked. A bulk status change applies to the shortlist in a single action, with filters and search still on the same screen. Interview invitations and reminder emails go out from templates automatically, so you never write to candidates one by one. On the Profesyonel and Kurumsal plans the position count and the monthly assessment quota grow.

And we carry it that way: every company's data stays closed to everyone else, names are masked by role, and every touch is recorded. KVKK and GDPR compliance is not a setting bolted on afterwards; a user without the right permission never sees a candidate's name, email or phone in the clear. A deleted record does not vanish from the database at once, soft delete keeps it recoverable.

Role based maskingA user who cannot see a name cannot see the delete action either, and their exports come out masked as well. Masking is applied on the server; the blur on screen is only the last layer.
Audit logWho changed what, and when: all of it is on the record. AI searches, bulk approvals and status transitions go into the same log.
Data closed outside your companyYour company's pool belongs to your company alone. Even a platform administrator cannot reach the data without formally switching into your account first.

On the left, the familiar view. On the right, the same applications after they went through GoTeam. The difference is not the time saved, it is that the reason behind every candidate is visible.

WITHOUT GOTEAM
  • 300 CVs, most of them never opened
  • A list left half finished in Excel
  • "Why this candidate?" still unanswered
  • Older applications lost
WITH GOTEAM
  • All read end to end and scored
  • The pool ready, in order and with the reason
  • The candidate's own sentence next to every score
  • The pool stays; a new job post reassesses it

The questions we hear most about pricing, KVKK and scoring; every answer rests on how the product actually behaves.

An applicant tracking system (ATS) is software where applications are collected, reviewed and tracked from a single panel. GoTeam adds AI on top: every CV is read end to end, candidates are scored against the position criteria, and status changes are tracked in an audit log. Interview scheduling and document collection run in the same panel.

GoTeam reads every CV end to end instead of scanning it for keywords. Skills, roles, education, languages and experience are normalized into a structured candidate profile. It tells "I know React" apart from "I used React on three projects", and every inference is tied back to the real passage in the CV.

Yes. KVKK and GDPR rules are written into the product itself. Role-based PII masking, a double-chained audit log and data retention policies are standard. A user without the pii:view permission sees candidate names, emails and phone numbers masked. Personal data is processed only within the limits of permission and purpose.

Yes. Every company gets a branded careers page and an online application form, fully white label. You publish your open positions, candidates apply, and CV analysis starts automatically in the background. No code is needed; the page goes live under your own brand.

Every candidate is scored from 0 to 100 against the weighted criteria of the position and lands in a Strong / Review / Weak decision band. The score is not a black box: you see the CV sentence behind every point. You set the criteria and the weights, and the ranking updates live.

No. The AI cannot change any application status on its own and cannot reject a candidate by itself. It only produces evidence-based recommendations; advancing, interviewing and rejecting always stay with the HR team. Every status change is made with human approval and written to the audit log.

Yes. GoTeam runs job and internship applications on the same evidence-based engine. You can define separate criteria and weights for internship positions, collect applications in the pool and rank them by position fit. The document collection and onboarding chain covers internship processes too.

GoTeam comes in four packages: Mini, Starter, Professional and Enterprise. Prices are published on the site and include VAT; Mini is 1,499 TL a month and Professional is 9,999 TL a month. On the annual plan you pay for 10 months and use 12. Tiers differ by the number of monthly AI evaluations, and re-scoring after a criteria change is free.

Open your panel and publish your job post. By the time the first applications land, the scores and the reasons are already waiting for you.