KIRA.id starts with one inbox for every customer channel, an AI that answers the repetitive questions, and a clean handoff to a human. On top of that, businesses can switch on follow-up automation, document & data, marketing and sales, and forecasting step by step.
Problem
Conversations live in WhatsApp, Instagram, Facebook, TikTok, and Gmail, and agents miss messages bouncing between them.
Solution
Manage every channel from one inbox with assignment (Mine / Unassigned / All) and unread badges.
Example
See WhatsApp, Instagram, Facebook, TikTok, and the Kira AI agent side by side, with full conversation threads.
Problem
Customers wait hours for a human while simple questions pile up.
Solution
Kira AI Agent triages, answers common questions and only escalates what needs a person, with a native WhatsApp preview.
Example
Customer: "Where is my order SFK569163902?" → Agent confirms delivery in 1–2 days, instantly.
Problem
Broadcasting updates or answering FAQs over WhatsApp is manual and error-prone.
Solution
Build auto-reply rules (keyword, contains, private), broadcasts and a configurable bot, all from one gateway.
Example
Rule: message contains "bantuan" (private) → auto-reply "Halo, apa yang bisa saya bantu? :)"
Problem
You cannot improve support you cannot see, volume, agent status and peak hours are invisible.
Solution
Live dashboards show open conversations, agent status and a 7-day conversation-traffic heatmap by hour.
Example
Spot that Tuesdays spike at 21:00–22:00 and staff agents accordingly.
Problem
AI answers are only as good as the knowledge it can reach, siloed data leads to wrong replies.
Solution
Central infrastructure pulls from your knowledge base, email, websites, enterprise tools and social media.
Example
One brain that reasons over docs, inboxes, your site and social, for consistent, accurate answers.
Problem
Automation breaks at the worst time and you are left alone with a cryptic error.
Solution
Real human support, a dedicated engineer, headset on, ready to help you configure and fix things.
Example
Reach the team via hello@kira.id or WhatsApp +62 851 9648 1709 whenever you are stuck.
Problem
Customer conversations often end without follow-up, and teams miss opportunities because there is no reminder.
Solution
Schedule follow-ups automatically from incoming conversations, then let KIRA send reminders or next offers with context.
Example
A customer asks about treatment pricing → a few days later KIRA automatically sends a follow-up message and reopens the conversation.
Problem
Existing contacts often lack enough context to follow up personally.
Solution
Enrich each contact with a business summary, services, and relevant social channels so follow-up messages feel personal.
Example
An aesthetic-clinic contact is enriched with services, location, and Instagram/Facebook accounts so follow-up feels tailored.
Problem
Keeping a social feed alive means constantly writing, editing and scheduling posts, a full-time job that competes with running the business.
Solution
Generate on-brand content with AI and publish it straight to your connected social channels from one workflow, no copy-paste between apps.
Example
Workflow: Make content → Post on social media. AI drafts the post, you approve, and it goes live across your accounts automatically.
Problem
Long videos (podcasts, webinars, demos) are packed with value but rarely get watched, and cutting them into shorts by hand is slow.
Solution
Drop in a long video and the AI transcribes it, finds the best moments, crops to vertical 9:16, adds subtitles and titles, and exports ready-to-post clips.
Example
One 11-min podcast → 13 portrait clips with translated Indonesian captions, person-crop and silence removal, via a 7-step pipeline.
Problem
Teams juggle several brand accounts and never know which content actually performs.
Solution
Manage multiple social accounts in one place, preview how each Reel renders on mobile, and track real view counts per clip.
Example
Run several brand accounts side by side and compare each content piece in one dashboard.
Problem
Reaching contacts you already know means switching tools and copy-pasting lists into separate channels.
Solution
Turn your enriched contacts into an outbound campaign, pick the sender and channel, then message automatically with your approval.
Example
Workflow: Pick contacts → Send WhatsApp / Email. One campaign reaches qualified contacts across both channels at once.
Problem
You launch campaigns but cannot tell which audience, channel or message actually converted.
Solution
Track every campaign by status, channel, audience, sent, open rate and click rate in one table.
Example
Compare a WhatsApp promo (68.2% open, 23.7% click) against an Email reminder (52.1% open, 18.4% click).
Problem
Teams drown in PDFs, Word docs, CSVs and HTML, and sending them to a third-party AI means leaking sensitive data.
Solution
Process files client-side, powered by your own OpenRouter key. Nothing leaves your machine unless you choose to.
Example
Keep only strategy documents, invoices over $1k, or React/TS resumes, scored and summarized automatically.
Problem
Reviewing documents one by one is slow and inconsistent across a whole team.
Solution
Drop up to 50 files (PDF, Word, TXT, MD, CSV, HTML) and let the AI score each against your natural-language criteria.
Example
Set a "keep score ≥ 50" threshold; export the results as a JSON report or a ZIP of kept files.
Problem
Rigid rules engines cannot capture "a contract with a non-compete clause" or "a go-to-market strategy deck".
Solution
Type what matters in plain English; the AI returns a match score and a short summary for every document.
Example
Example criteria: invoices > $1k, resumes for React/TS roles, contracts with non-compete clauses.
Problem
Enterprise data is unstructured and scattered, making it hard to power any AI use case safely.
Solution
Connect your private, unstructured enterprise data to a managed AI layer, with on-edge processing available for maximum privacy.
Example
Run chat and data processing locally on-device, so customer data never has to leave your infrastructure.
Problem
Planning inventory, staffing or budget on a single guess is risky when demand swings seasonally.
Solution
Train on your historical data and forecast the future with a central point estimate plus lower/upper confidence bounds.
Example
Forecast total impressions with a 95-score history; the model reproduces seasonal peaks and quantifies uncertainty.
Problem
Simple forecasts that only use past values miss the real drivers of demand like spend, launches or holidays.
Solution
Feed external variables (marketing spend, campaign dates, holidays, traffic) alongside history for context-aware predictions.
Example
Combine total_impressions_history with xreg forecasts to predict a March peak of ~25M within a 17M–27M range.
Problem
A single number hides risk; teams over- or under-provision because they do not see the range.
Solution
Use the lower and upper bounds to plan for the likely worst and best case, not just the average.
Example
Size infrastructure and staffing to the confidence interval instead of a single point estimate.
Problem
AI initiatives stall because models cannot reach the data and tools already living in your cloud.
Solution
Integrate cloud services and private AI infrastructure so models operate on your real, live data.
Example
Wire data pipelines (e.g. Google Cloud Dataflow jobs) directly into your automation workflows.
Problem
Public AI tools put proprietary data at risk and rarely meet enterprise compliance needs.
Solution
Run on private AI infrastructure, scalable, secure compute you control, on-prem or in your own cloud.
Example
A single platform fee plus pay-per-use infrastructure, estimated in advance so there are no surprises.
Problem
Brittle scripts break silently and nobody notices until reports are wrong.
Solution
Build repeatable pipelines with clear job graphs, status and metrics for every stage.
Example
Read → transform → map → write, each stage marked Succeeded, with encrypted managed storage.
Ready to see it on your own data?
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Operated by SAMUEL MATTHEW KOESNADI · Indonesia