Set up KPI Manager

Setup is done once per agent and takes a few minutes. Open the agent, go to the KPI Manager tab, and work left to right.

Step 1 — Add a data source

A data source supplies the live value of a metric. Open the Data Sources panel and choose one of the two tabs.

API Endpoint
Python Code

Use this when the number already exists behind an API in your systems. Callkaro will call it once per batch of calls, sending the phone numbers involved.

Field	What to enter
Unique Name	A short identifier you will reference from your metrics — for example `crm` or `update_lead_status`.
URL	The full endpoint URL in your system.
Method	POST.
Headers	Any authentication or content headers the endpoint requires, as key/value pairs.
Parameters	What Callkaro sends for each call. `phone_number` is always included and cannot be removed; add any other call variables you need.
Dummy cURL	A generated, read-only preview of the exact request Callkaro will send. Copy it and test it against your endpoint before saving.

Response shape matters. Your endpoint’s response is expected to be wrapped as { data: { ... } }. A source named crm returning { data: { conversion_rate: 85 } } is referenced from a metric as crm.data.conversion_rate.

Use this when the number needs to be computed, joined across systems, or cleaned up before it is usable. You write the logic; Callkaro runs it securely on the backend, once per batch.

Field	What to enter
Unique Name	The identifier your metrics will reference — for example `budget_quality`.
Parameters	Which call variables to include in each entry of the payload. `phone_number` is always included.
Payload Preview	A live, read-only preview of the exact list your function will receive.
Python Code	Your logic, written inside the fixed `async def generate_kpi_data(payload: list) -> dict` signature. Only the body is yours to edit.

The rules for the function:

payload is a list with one entry per lead in the batch, each carrying phone_number plus any parameters you configured.
It must return a single JSON-serialisable dict summarising the batch — returned directly, with no wrapper.
httpx is available for HTTP calls with no import needed.
Whatever keys you return are what your metrics can reference, as <unique_name>.<field>.

async def generate_kpi_data(payload: list) -> dict:
    for entry in payload:
        phone_number = entry.get("phone_number")
        # Your logic here

    async with httpx.AsyncClient(timeout=8.0) as client:
        response = await client.get("https://your-system/metrics")
        data = response.json()

    return {
        "l2s": 5.4,
        "s2c": 4,
        "message": "Focus on s2c for now",
    }

A useful trick. The output of a Python source also becomes context the RCA agent can read when explaining a miss. A short diagnostic string — like the message field above — is often as valuable as the number itself.

Step 2 — Define your KPI metrics

A metric turns a raw field into a tracked business number with a target. Open KPI Metrics and select Add.

Field	What to enter
KPI Metric	The name of the number you are tracking — for example `Conversion Rate`. The RCA agent reasons about this name, so make it descriptive.
Source	Where the value is read from, written as `<source_name>.<field>`. See the referencing rules below.
Value	The target this metric is expected to hit, as a percentage — for example `85`.

How to reference a source

Source type	Returns	Reference it as
API Endpoint	`{ data: { conversion_rate: 85 } }`	`api.data.conversion_rate` — note the extra `.data`
Python Code	`{ l2s: 5.4 }`	`budget_quality.l2s` — no wrapper

One metric is built in. Every agent starts with connected_to_conversion_percentage — the share of connected calls that converted. Callkaro calculates it for you from platform data, so it needs no data source. Its name and source are fixed; you only set its target value.

Step 3 — Write the RCA prompt

This is the single highest-leverage field in KPI Manager. It tells the analysis which calls to look at and what to care about. It occupies the whole left panel of the screen because it deserves the room. Use it to specify any of the following:

Scope — a region, a lead source, a campaign, a post-call outcome, or a specific metric.
Exclusions — call patterns that are artefacts rather than agent failures.
Method — how to analyse, for example comparing failed against successful calls, or examining one step of the conversation.
Priority — what matters most when several issues surface.
Leads — specific calls, call IDs or time windows, when you already suspect where the problem is.
Business context — anything about your business the analysis could not infer from the data.

A weak prompt	A strong prompt
”Find out why conversion is low."	"Only check the conversion rate for calls in Chennai, Tamil Nadu. Ignore calls that disconnect in the first 5 seconds — those are dialer artefacts, not agent failures. Compare converted against non-converted calls at the pricing objection step, and prioritise anything that affects the opening 30 seconds.”
Analyses everything, prioritises nothing, and produces generic findings.	Narrows the call set, removes known noise, states the method, and sets priority — so every finding is relevant.

Stuck on wording? Use the Generate with AI control next to the RCA Prompt heading. Describe what you want in your own words and it drafts a prompt for you, which you can then edit.

Leaving the prompt blank is valid — the analysis then covers all calls and all metrics. The field accepts up to 10,000 characters.

Step 4 — Choose the model and the automation setting

Both controls sit in the header bar at the top of the screen.

Control	What it does
Auto RCA	Switches the automatic daily analysis on or off for this agent. On by default. Turning it off stops the nightly analysis only — you can still run an analysis on demand from the dashboard at any time.
RCA Model	The AI model the analysis runs on for this agent. Available options: GPT-5.4 (default), GPT-5.6 Sol, GPT-5.6 Luna, Claude Opus 4.6, and Claude Sonnet 4.6.

Step 5 — Save

Select Save. Configuration is stored per agent and picked up on the next run.

Changes are not retroactive. Saving updates what the next run will use — the endpoints it calls, the metrics it tracks, and the prompt that scopes it. Reports already generated are unchanged.

Getting started

Pricing

Voice AI Agents

Chat Agents

AI FDE

AI Auditor

KPI Manager

Phone Numbers

Calls

Webhooks

Website Widget

Whatsapp (Facebook)

Integrations

Step 1 — Add a data source

Step 2 — Define your KPI metrics

How to reference a source

Step 3 — Write the RCA prompt

Step 4 — Choose the model and the automation setting

Step 5 — Save

​Step 1 — Add a data source

​Step 2 — Define your KPI metrics

​How to reference a source

​Step 3 — Write the RCA prompt

​Step 4 — Choose the model and the automation setting

​Step 5 — Save

Step 1 — Add a data source

Step 2 — Define your KPI metrics

How to reference a source

Step 3 — Write the RCA prompt

Step 4 — Choose the model and the automation setting

Step 5 — Save