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KILEVEL © 2026

Performance, Conversation & Quality Analytics

What You Don't Measure, You Can't Improve

It makes no difference whether your conversations happen on our platform or somewhere else. Kilevel transcribes your recordings and measures the performance of your agents — human and AI alike — against established industry metrics, against metrics you define yourself, and against analysis a model infers from every conversation. With scorecards you weight, KPI boards you assemble, and rules that speak up before anybody thinks to look.

Wherever the Conversation Happened

Calls and chats run here are analysed as a matter of course. Recordings from the contact centre you already have arrive over whichever channel your recorder already speaks — and are then treated identically: the same transcript, the same analyses, the same scorecards, the same reports.

CallsChatsWork runsUploads & ZIPDrive · FTP · S3API & URLRUN ON KILEVELCalls & chatsRECORDED ELSEWHEREYour archiveONE RECORDTranscript, analysis, score
Both rails end at the same record. What an agent said here and what a recorder wrote to your FTP server last night become the same kind of row — transcript, analysis and score.

Uploads & ZIP archives

Drop files or a whole archive on a job. The audio is staged, handed to the transcription provider as a signed link so it never passes through our application, and deleted the moment the transcript lands unless you asked for it to be kept.

Google Drive, watched

Point a job at a folder and it keeps working: new recordings are transcribed as they arrive. Files are never copied anywhere — the provider is handed a short-lived link to our audio proxy, which streams the file straight out of Drive on demand.

Your file server, over FTP, FTPS or SFTP

The way most contact centre recorders already publish their archive. Kilevel connects, lists and reads on demand; the protocol lives on the credential beside the host and port, and the recordings stay where they are.

S3-compatible object storage

AWS S3, MinIO, Cloudflare R2, Wasabi, Backblaze B2, Spaces, GCS over its XML API — one connection, differing only in endpoint, region and addressing style. The only remote source that never touches our infrastructure at all: each object is pre-signed for read, so the provider fetches it straight from your bucket.

An API job that stays open

Configure language, speakers, analysis outputs and concurrency once; then hand over one recording per call, as a link or a multipart upload, with whatever you already know about it. Repeating an id you have sent before returns the existing recording rather than transcribing — and billing for — the same audio twice.

And the details the audio cannot carry

A recording cannot tell you who the agent was or which customer called, so Kilevel never guesses. Map a file-name pattern or a spreadsheet column to agent, customer, conversation id, start time, project, stage, tags — or to fields you define. Agents can be linked to real workspace agents, customers to contacts by normalised phone number, exact matches only.

Kilevel Performance roster: an agent list beside one agent's score, direction of travel, scorecard breakdown and coaching notesKilevel Performance scorecard matrix: teams and agents as rows, scorecard sections as columns, shaded by score with below-pass cells markedKilevel Performance workbench: volume and coverage tiles, quality over time with the middle half of agents banded, score distribution, activity by hour and an analysis coverage panel
Enlarge

One Agent, Everything You Need for the One-to-One

Pick anyone on the floor and the page answers the questions a coaching conversation actually opens with: what they score, how many conversations that is drawn from, which way the last seven, thirty and ninety days went, and where the points are being lost section by section. Coaching notes are filed against the window they were written about, and marked as either a private note or something that was said to the person.

Twelve Metric Packs, So a Number Means the Same Thing Everywhere

Writing good metrics is a specialist job, and an organisation that invents its own can never compare itself to anything. So these packs are fixed: every field keeps the scale its benchmark is published on — CSAT on the five-point item, CES one to seven where low is good, an NPS-style zero to ten likelihood — and every one tells the model to omit the field rather than guess at the middle. Your own metrics sit alongside them, not instead of them.

TWELVE PACKSFixed, not inventedONE ATTACHEDTo your jobITS FIELDSFilled per conversationSAME SCALE IN EVERY ACCOUNT
A pack is stock, not a draft: you attach one to a job and it fills the same fields on the same scales for every conversation — in your account and in every other one.
Select a metric pack12
  • Six section scores out of 100 — opening, discovery, resolution, empathy, closing and compliance — over the measures almost every contact centre already keeps: first-contact resolution, satisfaction, customer effort, agent conduct. Plus the modern additions that only became measurable once transcripts got cheap: sentiment at the start and the end separately, churn risk, repeat-contact signal and root cause. Almost everybody starts here, and the sector packs then run alongside it.

    Sections · illustrative
    Resolution85
    Empathy77
    Compliance89

    A section the conversation gave no opportunity for is omitted — a guessed 50 is not a neutral answer, it is a wrong one that drags the average toward the middle.

    Sentiment shift
    FrustratedSatisfied

    Start and end are captured separately. An average would give this conversation and its mirror image — opened calm, ended furious — the same number.

    Fields in this pack
    openingScoreresolutionScorecomplianceScorecustomerSatisfactionEstimatecustomerEffortScorefirstContactResolutionchurnRiskrootCauseCategory
    • Six sectionsOpening, discovery, resolution, empathy, closing, compliance
    • Published scalesCSAT 1–5, CES 1–7 (low is good), NPS-style likelihood 0–10
    • Root causeWhy the conversation had to happen at all, as its own category
    • Start hereThe sector packs sit alongside it, never instead of it
  • Qualification, depth of discovery, objection handling — and whether the agent ever actually asked for the business. It scores the behaviour that precedes a sale rather than only the sale: a good call on a bad lead is still a good call, and marking it down teaches people to avoid difficult leads.

    Where the conversations drop out
    Decision maker
    74%
    Need identified
    61%
    Objection handled
    48%
    Asked for the business
    39%

    The biggest drop is almost never at the close. It is at the step where nobody asked.

    Fields in this pack
    callOutcomediscoveryQualityobjectionHandlingvalueArticulationclosingAttemptednextStepAgreedtalkedPastTheCloseleadQuality
    • BANT, but measuredNeed, budget, authority and timeline each as their own field
    • Past the close"Kept selling after they agreed" is a signal of its own
    • CompetitorsWho got mentioned lands as a field rather than as an anecdote
    • Lead quality apartSo scoring the call never quietly scores the list
  • Resolution quality, the effort the customer was put to, and the operational signals that say why: repeats, transfers, holds, knowledge gaps and documentation gaps. Diagnosis deliberately outweighs resolution — a symptom treated is a ticket that comes back, and it looks identical to a real fix until it does.

    What the journey cost
    RepeatedOn holdTransferredContactResolved
    Effort (CES)4.6 / 7
    First contact62%
    Had to repeat38%
    Fields in this pack
    diagnosisQualitycustomerHadToRepeatholdCounttransferCountexpectationSetknowledgeGapExposeddocumentationGapslaBreachRisk
    • The causes of effortRepeating, transferring, holding — the things an operation can fix
    • Gaps, not blameKnowledge and documentation gaps point at the process
    • Expectation setWhether anybody said what happens next, and by when
    • SLA riskBefore the clock runs out rather than after
  • Built around what regulators actually check: identity before any disclosure, third-party leakage, mandatory notices, hardship and pressure. Several items are worded so that TRUE is the bad outcome — deliberately, so that a breach can never come out as a high score.

    What an auditor checks first
    Identity verified
    auto-fail
    Told a third party
    auto-fail
    Mandatory notice
    Recording disclosed
    Pressure applied
    Hardship

    Two of these do not cost points. They fail the conversation outright, however warm the rest of it was.

    Fields in this pack
    identityVerifiedthirdPartyDisclosuremandatoryDisclosureGivenrecordingDisclosureGivenhardshipIndicatedpressureTacticsdisputeHandledCorrectlycomplianceRisk
    • Auto-failA missed identity check does not cost points, it fails the conversation
    • Third partiesDisclosure to somebody who is not the debtor is its own field
    • HardshipIndicated and handled appropriately are two fields, not one
    • Polarity flippedOn pressure and leakage, TRUE is the bad answer
  • Two-identifier verification, triage urgency, red-flag escalation — and whether a non-clinician stayed inside their scope. The pack separates "a red flag was mentioned" from "the red flag was escalated", because that gap is the finding.

    Urgency, and what followed from it
    Routine
    Soon
    Urgent
    Emergency
    Red flag mentioned
    100%
    Escalated
    71%

    The distance between the two bars is the finding — and it does not exist in any system that records only "red flag: yes".

    Fields in this pack
    patientIdentityVerifiedurgencyLevelredFlagSymptomMentionedredFlagEscalatedtriageProtocolFollowedscopeOfPracticeRespectedconsentCapturedempathyUnderDistress
    • Two identifiersBefore any patient detail, not after
    • The gap is the pointMentioned and escalated are separate fields
    • Scope of practiceClinical advice from a non-clinician becomes visible
    • ConsentCaptured where it has to be captured
  • Claim capture completeness, coverage and excess explained, fraud signals — and how somebody in the worst week of their year was handled. It scores the handling rather than the outcome: a claim nobody could pay, explained well, is still a good conversation.

    Capture · one claim
    Loss date
    What happened
    Coverage explained
    Excess explained
    Documents asked
    Timeline given
    Fraud signal
    Distress noticed

    6 of 8 captured · the two missing ones are the callback

    Fields in this pack
    policyIdentityVerifiedlossDescriptionCompletecoverageExplainedexcessExplaineddocumentationRequestedfraudIndicatorcustomerDistressdistressHandling
    • CaptureLoss date, description, documents requested, timeline promised
    • ExplainedCoverage and excess as two fields, because people confuse them
    • Fraud signalRecorded as an indicator, never as a verdict
    • DistressWhat was noticed and what was done about it, scored apart
  • Suitability, risk warnings, costs and charges, vulnerability and pressure — the items a compliance review asks about first. Including the things that must never be said: an implied guaranteed return is a field of its own.

    Two lists that must never be confused
    Must be said
    • Suitability assessed
    • Risk warnings given
    • Costs disclosed
    • Cooling-off explained
    Must never be said
    • A guaranteed return
    • Decide today or lose it
    • Past performance, no caveat
    Fields in this pack
    suitabilityAssessedriskToleranceDiscussedriskWarningsGivencostsAndChargesDisclosedguaranteedReturnsImpliedpressureAppliedclientUnderstandingCheckedcoolingOffExplained
    • SuitabilityAssessed rather than assumed, and understanding checked
    • The forbiddenImplied guarantees and applied pressure captured on their own
    • CostsDisclosed or not, as an answer with no room in the middle
    • VulnerabilityIndicated and handled, again as two fields
  • Whether the problem was reproduced, the cause found, and the explanation pitched at the person actually on the line — plus what engineering should hear about it. Suspected bugs, missing reproduction steps and raised feature requests leave support as signals somebody can act on.

    Two endings that look alike
    Symptom reportedRoot cause foundClosed onceWorkaround givenReopens in nine days

    In the ticket report both of these read "resolved". Only the transcript says which one is true.

    Fields in this pack
    problemReproducedrootCauseFoundtechnicalAccuracyexplanationPitchedCorrectlyreproductionStepsCapturedbugSuspectedfeatureRequestRaisedcustomerTechnicalLevel
    • ReproducedThe difference between a diagnosis and a guess
    • Pitched rightJudged against the customer's level, not the agent's
    • Handed onwardSuspected bugs and feature requests leave the ticket
    • EscalationWhether it was justified, not merely whether it happened
  • Booking outcome, details read back, cancellation policy explained, reminder arranged — and a no-show risk read from how the customer actually sounded. Precisely what a row in an appointments table can never tell you.

    One booking · what is missing from it
    Tue 14:30Annual service
    Name read backNumber confirmedPreparationPolicy explainedReminder set
    No-show riskElevated

    No reminder, no preparation instructions, a hesitant tone: the calendar still shows nothing but a booked slot.

    Fields in this pack
    bookingOutcomeavailabilityOfferedPromptlyalternativesOffereddetailsConfirmedBackpreparationInstructionsGivencancellationPolicyExplainedreminderArrangednoShowRisk
    • Read backName, number and slot confirmed back — or not
    • PreparationInstructions and cancellation policy as fields of their own
    • AlternativesWhether a second slot was ever put on the table
    • No-show riskRead from the conversation, before the slot is wasted
  • Why they want to leave, whether anybody asked before a discount appeared, and how recoverable they are. The pack also captures whether a service failure was admitted — the most common cause that never shows up in a cancellation report.

    The order it should happen in
    Reason heardRoot cause exploredOffer madeOutcome

    Skipped: the discount arrived before anybody asked why. That buys a month, not a customer — and in the report it looks like a save.

    Fields in this pack
    cancellationReasonreasonCategorysaveAttemptedrootCauseExploredofferMadeofferRelevantdiscountGivenwinBackPotential
    • Ask firstRoot cause explored before an offer was put on the table
    • Relevant?Offer made and offer relevant are two different fields
    • Service failureAdmitted or not — the cause behind the cause
    • RecoverableA read you can prioritise a win-back list with
  • How this customer prefers to be dealt with: communication style, what actually drives their decision, emotional state, trust, and how much detail they want — so the next person to speak to them opens the right way. Every field is a model's read of this one conversation rather than a trait somebody carries, and the confidence that ships with it is deliberately low on short calls.

    Placed, not scored
    DirectRelationalWants detailWants the answer
    Direct · wants the answer
    Confidence3 / 5

    Neither axis has a good end. Nothing here asks for a diagnosis or a condition, and none of it may be read as one.

    Fields in this pack
    communicationStyledecisionDriverinformationPreferenceemotionalStatetrustLevelpriceSensitivitySignalspreferredNextApproachconfidence
    • This conversationDescribes how somebody was here, not a trait they carry
    • With confidenceEvery read carries its own uncertainty beside it
    • Short callsThe less conversation there was, the lower it scores itself
    • For the next contactA suggested opening, not a label
  • Composure, adaptability, confidence and script dependence as coaching signals — plus the emotional load a conversation put on the agent, so hostility absorbed and recovery needed become visible instead of invisible. Those load fields describe the call, not the person's ability: ranking people with them points the tool backwards.

    Two halves that are never added up
    Coachable
    Adaptability
    Composure
    Rapport
    What the call cost
    Emot. labour
    Hostility
    Recovery

    The left half is what you coach. The right half is what you staff around — breaks, routing, support. A league table built from the right-hand column punishes people for the calls they were given.

    Fields in this pack
    composureUnderPressureadaptabilityrapportBuildingscriptDependenceemotionalLabourhostilityAbsorbedrecoveryNeededcoachableMoment

    This assesses employees: in several jurisdictions — Germany's BetrVG § 87(1) no. 6 among them — that needs the works council's agreement first.

    • PractisableEvery criterion is a behaviour somebody can change by next week
    • Load made visibleWhat a conversation cost, beside what it produced
    • Script dependenceA coaching signal, never a disciplinary one
    • Aggregate modeRecommended where codetermination asks for it

And Then Your Own Analysis

The packs are the yardstick. What makes your business unlike everybody else's is yours to define: your own fields, your own classifications, your own weighting — and a pass mark that is a decision somebody made rather than a default we shipped.

YOU SET ITThe pass markEVERY CONVERSATIONOne comparable numberAGENTSSECTIONSBELOW THE LINE
Every column is measured work — one row per agent, one column per scorecard section. The dashed plane is the only thing here that is not a measurement: it is the line you set.

Your own analysis, described rather than coded

Say what you want pulled out of every conversation and the assistant writes the field, its type, its allowed answers and the instruction the model reads. On an existing job it proposes those outputs from your actual transcripts rather than from a description of them, and nothing is applied until you press Apply.

Scorecards you weight yourself

A scorecard turns metrics into one number: pick the criteria, weight them against each other, set the pass mark, and mark the ones that fail a conversation outright rather than costing it points. Seven presets are there to argue with — they are copied, not referenced, so editing yours changes nothing for anybody else.

Measured, not judged

Agent talk share, dead air, interruptions, speaking rate and the longest uninterrupted monologue come out of the recording itself rather than out of a model's opinion. They sit beside the inferred scores and are labelled as what they are, because the two should never be read as the same kind of claim.

Classifications become metrics

Most of what a pack asks is not a number — resolution status, churn risk, call outcome, lead quality are answers from a list, and the mean of "resolved" and "unresolved" is not a quantity. Pin one answer and it becomes a percentage, which then has a trend line, a benchmark, an alert threshold and a scorecard weight waiting for it.

Measure Agents. Campaigns. Scripts. The Whole Operation.

The same numbers, approached from different directions. Every view reads the same window, the same sources and the same filters, and asks a different question of them.

By agentBy campaignBy script versionBy hourONE WINDOWThe same conversationsFOUR QUESTIONS, NOT FOUR REPORTS
Four plates, one body. The conversations are not copied and not re-analysed; all that changes is the line you cut them along.

Agents, human and AI, on one scale

The same scorecard, the same sections, the same pass mark. An AI agent that scores 91 on compliance and 71 on empathy is telling you where it may be trusted and where it may not — and it is the only honest way to compare it to the team it is meant to work beside.

Campaigns and their scripts

Filter the window down to one campaign, one flow or one version of a script, and read the same metrics back. A wording change that improved discovery and cost you closings shows up as two movements rather than one flat conversion number.

Teams, tenure and channel

The matrix groups by team, by how long somebody has been doing the job, by channel or by agent. New starters compared against new starters is a fair question; new starters compared against the floor average is a management report nobody believes.

The operation as a whole

Volume, coverage, the spread of scores across the floor, the hours the work actually lands in, and how much of it carries no agent at all. Each view narrows the one above it, so a reader who stops halfway has still learned something true.

KPI Boards Somebody Reads, and Rules That Speak Up

A quality programme fails in the gap between questions: a metric slides for three weeks, nobody opens the page, and the first signal is a churned customer. Every view can be saved as a board of its own — and beyond that, the platform asks on your behalf.

InboxWebhookAutomationYOU SET ITThreshold & minimum sampleONE EPISODEThe day it crossedFOURTEEN DAYS · THIRTEEN OF THEM QUIET
Thirteen quiet days and one that crosses the line. The episode leaves as an email, as a webhook into your Slack or Teams channel, and as an automation event you can hang further work off.

A briefing, not a dashboard nobody opens

The daily view answers the question people have before they have a question: what changed, and does any of it need me today. Every sentence in it is assembled from a number that was measured, and a card that has nothing to report disappears rather than softening into "nothing unusual".

In the inbox, daily or weekly

Choose the cadence, the hour, the timezone, the scorecard and the metric it leads on, and add whoever else should receive it. A subscription that quietly stops working says so in its own panel instead of simply never arriving.

Standing rules with a live preview

Watch a metric, a scorecard, an auto-fail rate or volume; per account or per agent; as an absolute, a change or a percentage change against a baseline. The editor tells you "this would fire for 19 of 24 agents right now" while you are still typing the number — which is the only cheap moment to find that out.

A minimum sample, on purpose

Every rule carries one, because a threshold evaluated over four conversations fires constantly and hardest at the newest and most part-time people. Episodes land as email, as a webhook any Slack or Teams channel accepts, and as an automation event you can hang further work off.

Measuring Is Usually Step One, Not Step Three

With our enterprise customers, measuring the operation regularly comes before any AI voice agent joins it — and that is the right order. The existing archive is analysed first: you find out what the operation actually does today, which kinds of conversation go wrong, and why. Only then can you answer what an AI agent should take over, and, more to the point, how you would ever know whether it did it better or worse.

When the agents do arrive, nothing about the measurement changes. They appear in the same rows, on the same scorecard, against the same pass mark — so a pilot has a baseline from day one instead of an opinion.

  1. 01

    Ingest the archive

    Your existing recordings arrive over FTP, S3, Drive, API or upload, and are transcribed.

  2. 02

    Establish the baseline

    Metric packs plus your own fields run over every conversation. You see the operation as it is, not as it is remembered.

  3. 03

    Agree the scorecard

    Weights, pass mark and auto-fail criteria are settled — and calibrated against human reviewers.

  4. 04

    Improve what is worth improving

    Coaching, script changes, process faults at the root. The numbers say where.

  5. 05

    Add AI agents

    They enter the same measurement and are held to the same line as everybody else.

01Archive02Baseline03Scorecard04Improve05AI agentsSTEP ONEMeasure firstSTEP FIVESame measurement, new agentsTHE PASS MARK DOES NOT MOVE
The order is the argument. The AI agent stands on the last tread — on the same scorecard, against the same pass mark as the four below it, because otherwise a pilot is an opinion rather than a comparison.

Measuring People Is Regulated. That Is Built In.

A quality programme that cannot be turned down, pseudonymised or aggregated is one a codetermined workplace simply cannot adopt. So those switches live where the numbers live.

Names shownEveryone by namePseudonymsTrend stays, name does notAggregate onlyOne row, minimum sizeONE SETTINGThree answersON THE PAGE WHOSE DATA IT GOVERNS
The same four agents, three settings. On the right there are no per-person figures left at all — and the combined row appears only when there are enough people in it for it not to be one person with extra steps.

Three answers to "must this measure individuals?"

Names shown, stable pseudonyms, or aggregate only — no per-person figures at all, with people in view combined into one row and only when there are enough of them for that row not to be one person with extra steps. The setting sits on the page whose data it governs, not buried in account settings.

Calibration, before anybody acts on a score

Sample conversations into a review queue, have a person score them on the same scorecard, and read back how close the two were: agreement rate, mean difference, the bias in either direction, and which criteria disagree most. It is the only screen here that says whether the numbers can be trusted, which is why it is not behind a link.

Where the law has something to say, we say so

The agent coaching pack carries its own advisory: in several jurisdictions — Germany's BetrVG § 87(1) no. 6 among them — assessing an employee this way needs the works council's agreement before it is switched on. Its load fields describe the conversation, not the person, and using them to rank people points the tool backwards.

  • Pseudonym reveals are logged
  • Aggregate mode with a minimum group size
  • Metric packs that carry their own advisory
  • Conversations sampled into a review queue
  • The model's score and the reviewer's, side by side
  • Export as PDF and CSV
12
Metric Packs

One general and eleven sector packs, on fixed scales

6
Intake Channels

Upload, ZIP, Google Drive, FTP/SFTP, S3-compatible, API and URL

Human & AI
One Scorecard

The same sections, the same scale, the same pass mark

100%
Not a Sample

Every conversation scored, not the two a reviewer gets through

Next step

Ready to Transform Your Call Operations?

Join Europe's largest infrastructure enterprises already running their call operations on Kilevel with premium AI quality.

FAQ

Frequently Asked Questions

Everything you need to know about Kilevel.

Kilevel is a full-stack AI operations platform for businesses that communicate at scale. It unifies phone calls, WhatsApp, SMS, email, and web conversations under a single AI-native operating system, with visual workflow automation, omnichannel inbox, AI conversation analysis, and real-time operational control. From solo operators to enterprise teams, Kilevel runs autonomously across your entire communication stack.

Legacy IVRs follow rigid scripts. Chatbots handle text in isolation. Kilevel is an AI-native operations platform that handles real phone calls, WhatsApp, SMS, email, and web conversations simultaneously, with genuine contextual understanding. Agents adapt mid-conversation, book appointments, trigger follow-up automations, analyze every interaction, and hand off to humans when appropriate. The entire stack runs from a single platform: inbound queue, outbound campaigns, post-call intelligence, and operational reporting.

Yes. You can build and test your AI agent for free using our library of ready-made industry templates or by starting from scratch. Design call flows, configure channels, and connect integrations before going live. When you deploy, you pay a monthly subscription fee, which already includes a bundle of free AI minutes per month. Any usage beyond the included allowance is billed on a pay-as-you-go basis. No seats, no surprise fees.

No seats, no per-user charges. Kilevel combines a monthly subscription with usage-based pricing: each plan includes a pool of free AI minutes per month, and additional usage beyond that is billed at a per-minute rate. Building, testing, and configuring agents is free. Once live, costs scale predictably with your volume. Enterprise customers with high-volume commitments can discuss custom pricing agreements.

Most teams ship their first agent within a day. Kilevel comes with a library of industry-specific templates that cover the most common call flows and channel configurations out of the box. Pick a template, configure your agent's persona and rules, connect your tools, and deploy, no code required. Custom flows are built visually, and the AI flow assistant can generate an entire flow structure from a single descriptive sentence.

Kilevel gives your team leverage, not a replacement. AI agents absorb the volume of after-hours calls, common questions, appointment bookings and multilingual conversations, so your staff can focus on complex cases that genuinely need a human. Built-in sentiment analysis and configurable handoff triggers mean the AI always knows when to step aside.

The platform, the database and your recordings run on Google Cloud in Frankfurt, Germany, and the telephony infrastructure sits in a German data centre. Speech and language providers are used through their EU endpoints wherever the provider offers one. Where a provider processes outside the EU, that transfer runs on Standard Contractual Clauses, and every such provider is named with its processing region in our public Trust Center. An Article 28 data processing agreement applies from day one, with no separate negotiation, and we never use your data to train models.

Yes. Agents can detect a caller's language and respond accordingly, or you can configure language-specific flows per channel. This is particularly relevant for European businesses serving customers in German, French, Spanish, Dutch, and other languages alongside English, all within the same platform, without duplicating your entire agent setup.

No. The platform is designed for operators first. Flow building, agent configuration, channel setup, and integration connections are all no-code. Developers who want deeper control get a full REST API, webhooks, MCP-compatible tool discovery, and prompt-level customization, but none of it is required to run a production deployment. Non-technical teams operate Kilevel every day.

Completely. Every flow, agent behavior, escalation rule, and analysis signal is configurable, visually, without code. You define the tone, language, fallback responses, and handoff conditions. Advanced users can go further with prompt modules, templating parameters, flow templates, and a full REST API. If your workflow is unusual, Kilevel almost certainly supports it.

Complete control. You define tone, language, permitted topics, and response templates using prompt modules and templating parameters, reusable building blocks your agents draw from consistently. Escalation rules trigger human handoff based on sentiment, topic, or conversation outcome. Every configuration is fully testable in a sandbox before it touches a real customer.

You have full control over what your agent says. Define tone, language, and exact response templates. Escalation rules let you specify when a conversation gets transferred to a human, based on topic, negative sentiment, or repeated failed attempts. Everything is testable before it goes live, and you can iterate on agent behavior at any time.

Yes, and often without any setup at all. Kilevel supports MCP (Model Context Protocol), which means it can automatically discover and digest integrations from modern CRMs, ERPs, and data platforms. For everything else, there are webhooks, a REST API, and pre-built connectors. Your data flows in and out on your terms.

Both directions are fully supported. Kilevel uses MCP (Model Context Protocol) to automatically discover and integrate modern CRM and ERP systems, often with zero connector setup. In the other direction, Kilevel's REST API lets your own tech stack trigger calls, campaigns, and automations externally, and pull conversation results, analysis, and outcomes back into your systems on demand.

Yes, automatically. Kilevel scales concurrent lines dynamically, whether you're handling 10 conversations or 10,000, the infrastructure adjusts without manual intervention. The Operations Control Center gives you live visibility into concurrency, queue depth, and agent performance, so you're never operating blind at scale.

Your data sits in Frankfurt, Germany: platform, database and recordings on Google Cloud, telephony in a German data centre. Credentials are encrypted at the column level with AES-256-GCM, every query is scoped to its owning account, sub-users carry project-specific roles, and administrative actions are written to a reviewable audit trail. We process only on your documented instructions and never train models on your data. The full subprocessor list, with the processing region of each, is published in our Trust Center.

Kilevel agents are designed to manage uncertainty without frustrating the caller. They can ask clarifying questions, offer alternate paths, or escalate to a live operator, automatically, based on rules you set. The Attention Suite enables immediate human takeover mid-conversation without any disruption to the customer experience.

Kilevel agents handle uncertainty without derailing the experience. They can ask clarifying questions, reroute the conversation along alternate paths, or escalate to a live operator, all automatically, based on configurable rules. The Attention Suite lets operators intervene instantly mid-call, taking over the conversation without the customer noticing any disruption.