AI Intelligence
AI where it actually pays off in your operation — conversations scored by your rules, agents that carry routine between your tools, and reports that draft themselves.
The problem with “doing AI”
There are two popular ways to add AI at work. One is pasting things into a chatbot and asking what to do — quick, unstructured, and a different answer every time. The other is buying an “AI platform” that transcribes everything, summarizes everything, draws a sentiment graph — and quietly becomes a graveyard of summaries, because the output comes from a black box nobody trusts, and none of it lands where the work happens.
We build the third thing: AI does only what it’s good at — reading language and moving information — and the judging runs on rules your team wrote.
What we build
Four services. Take what your operation needs.
Capture and transcription. Calls and chats alike — sales, support, onboarding, QA. From wherever they happen: telephony, meeting tools, messengers, your CRM. Every language your team works in, with personal data anonymized if anything has to leave your environment.
Extraction into structured fields. Pain signals, objections, budget, timeline, retention risks — strict, consistent fields, not a free-text summary.
Scoring by your rubric. The criteria are written with your team. Rules do the scoring — same conversation, same score, every time — and every point traces back to a quoted line. No black box.
Writeback where the work happens. The CRM, the ticket, the QA board — so routing, dashboards and coaching can actually use it.
Filing, routing, reminding. A message lands in the team chat — an agent files it, routes it to the owner, sets the reminder. Nothing waits for someone to notice it.
A morning brief. One agent reads the calendar, the inbox and the task board — and hands you the day’s priorities before you’ve opened them yourself.
A weekly wrap that writes itself. What moved, what stalled, what needs a decision — assembled from what actually happened, not from memory.
We wire these chains across the tools you already use — chat, tasks, CRM, mail, docs. The routine carries itself; people make the judgment calls.
Reports that draft themselves. AI connected to your warehouse, BI and CRM — pulling the numbers, drafting the analytical report, flagging what moved and why it matters. The report that took a day becomes a question you ask.
Private where it must be. When data can’t leave the building, we deploy on models that run on your own servers.
Analysing content and social media is its own service — Content & Performance Analytics →
An assistant built on your materials. Playbooks, recorded calls, product docs — answers that quote your playbook, not the internet.
Ramp measured in weeks. A new hire questions the assistant all day instead of pulling seniors off their work — that’s where the onboarding months usually go.
Before any score or report counts, we validate it against work your managers already checked — the system earns the team’s trust with numbers, not promises.
Going deeper: a system that learns
Rubric weights re-tuned against real outcomes — which scored leads closed, which flagged clients actually churned — so the scoring reflects your market today, not the day the rules were written.
Where each rep or support agent loses points across all their conversations — so coaching targets the real weak spot instead of giving general advice.
The same scoring pointed at support and retention: which conversations kept a client, and which lost one.
Agents that act on what the scoring finds — route the deal, alert the team lead, open the task.
Are we measuring what actually predicts revenue and retention?
Who this is for
A team running calls or chats at volume — sales, support or BPO — where managers can review only a fraction by hand.
Multi-language, multi-market teams, where the lead can’t listen to half the conversations at all.
Companies that need a defensible record of quality — every score traceable to a quoted line.
A team drowning in routine an agent should carry — filing, routing, reminding, reporting.
Anyone already burned by an AI tool that produced summaries nobody read.
What you get
A working pipeline in your stack. Rules and rubrics documented and owned by your team. Validation on your real conversations and data before you rely on any of it. And a clean handoff — the system runs in your accounts, on your own servers if it has to.
Proof
If your managers hear one conversation in twenty — or your week is eaten by routine an agent should carry — tell us your volume, your languages and your tools.
We’ll tell you what AI would look like on your stack.
Let's talk