Data analytics, structured and unstructured
Your dashboard tells you what happened. Your customers and your documents already said why.
Most of what a business knows is in the calls, chats, tickets, reviews, survey answers and scanned documents nobody has time to read. We build systems that read all of it, score it against rules your own people write in plain English, and pin every finding to the exact moment or line that produced it.
At a glance
- Coverage
- Every conversation, not a sample
- Checks
- Anything you can describe in a sentence
- Every flag
- Confirmed by a person
- Your data
- Your cloud, personal information stripped first
What it covers
Everything read, every finding with its receipt.
Every conversation, not a sample
Calls, chats, tickets, reviews and survey verbatims, read in full. A QA team hears two to five in a hundred; a dashboard counts the rest without ever saying what was said. The conversation that matters most is the one a sample was never going to find.
Both sides scored
Most of this market sells customer sentiment. We score the customer side and the agent side, because the agent side is where the fixable findings are: an accurate answer that left out the part that mattered, a next step nobody asked for.
A new check is a sentence
Write “flag any conversation where the customer asked why the price went up and the agent did not explain what changed”, and every conversation from then on is scored against it. No tagging exercise, no training set. Definitions are versioned, so you can show which rule was in force when something was scored.
Every finding carries its receipt
The exact transcript moment, the audio beside it, and the rule version that was in force. Defensible in a coaching session, an audit or a regulatory review, and the reason a team accepts a flag instead of arguing with a score.
Documents read once, checked your way
Bills, invoices, forms and applications read in batches, with each team’s own rules applied: a VIN is 17 characters, this rate must match the tariff, these four documents must agree. Only the exceptions come back to a person.
Your cloud, your region, personal information stripped first
Deployed into your own account. Personal information is removed before any text reaches a model, recordings never leave your perimeter, and it sits on top of the systems you already record into. Nothing replaced, nothing migrated.
How we do it
Start with a handful of checks, then let them compound.
01
Connect what you already record
Call platforms, helpdesks, app store reviews, survey exports, document folders and email. Real time streaming, scheduled sync or file import. Under an hour from data connected to the first insight on your own conversations.
02
Start with a handful of checks
Five or six, written with the people who set the standard. Risk and quality first, because that is where the pain is sharpest and the evidence most needed.
03
Your reviewers mark it, we tune against real disagreements
Your QA lead reviews beside the system and says where it is wrong, with the reason. Every disagreement makes the next check better, and every review that turns up something new becomes a check that runs on every conversation from then on.
04
The library compounds
Once quality and compliance are covered, the same pass over the same conversations feeds customer care, product and demand: recurring issues ranked, feature requests counted, the questions nobody could answer. No second ingestion, no second vendor.
05
A flag is a candidate, never a verdict
The system reports nothing to anyone on its own. Each flag is a possible issue with its evidence attached, and a person confirms it. Your QA team stops listening for the calls worth reviewing and starts deciding.
What you get
Numbers you can trace to the source.
- Every call, chat, ticket, review and survey answer read and scored on both sides
- A library of checks in your own words, versioned, growing with every review
- A risk ordered review queue, a quality index per agent, and coaching that comes with the moment attached
- Recurring issues, escalation risk, feature requests and unanswered questions, ranked and counted
- Document extraction pipelines with your checks applied and an exceptions queue
- Dashboards with every number traceable to the transcript or the page it came from, in your own cloud
Where we have done it
Real engagements, named.
Read case studyAI engineering · Life insurance · Australia
An Australian life insurance broker, roughly 500 calls a week: 9,218 calls read in full in four months. On 8,395 of 8,479 scored calls no next step was asked for, a structural gap invisible at any sample size. 19 calls in 8,460 carried a high or critical escalation signal that a 2% sample would have missed. Five themes carried 5,047 of 5,736 recurring issues: a short, fixable list.
Vehicle logistics groupRead case studyAI engineering · Vehicle logistics · India
A vehicle import and logistics group: three departments typing Bills of Lading, freight bills and supplier invoices into spreadsheets, replaced by one pipeline that reads them, applies each team’s checks, and marks what looks wrong.
Questions
Questions we get asked.
- We already have a QA scorecard, a CX dashboard and a conversation intelligence tool.
- Each is good at what it was built for. The scorecard reaches a sample, the dashboard counts without listening, and the sales tool watches seller technique. The question is what happens to the 95% of conversations none of them opens, and to the agent side of the ones they do.
- Does this mean a machine grades our people?
- No. It reads everything, attaches the evidence, and hands your team a shortlist. A person confirms every flag, and your team keeps its own scorecards and its own judgement. The right measure is not how many flags it raises but how many your team agrees with once they open the evidence.
- What can it score?
- Anything you can describe in a sentence. Process adherence, accurate but incomplete, next step asked, empathy and tone, escalation risk, churn and price shock in the customer’s own words, advice boundary and disclosure for regulated sales, follow up and trade in for dealers, crash triage buried in five star reviews.
- Our documents are scans with stamps and handwriting. Will it work?
- That is the normal case and it is why extraction alone is not the product. Uncertain reads are flagged with the original beside them; the pipeline never silently guesses.
- What accuracy do you promise?
- None until it has run on your volume and been counted. Estimates are labelled as estimates; measured results as measured. Everything in the proof above is a measured count from one operation, and one operation is not an industry.
- Is this the same as business intelligence?
- It feeds it. BI needs structured data, and most of yours is not structured yet. This is the step that makes it so, with the evidence kept.
What clients say
In their words.
01 / 04
Before Metricsense, checking calls was a manual process: we selected files for review. Now the platform analyses every call, and we review by risk. It has identified customer experiences we would not have picked up before, and it lets us scale our quality assurance program without increasing headcount, with our team still the human in the loop. It helps us protect our customers, our business and our agents.
Russell Cain
Founder and CEO, Life Insurance Direct
Also under AI engineering
Where it leads next.
Voice and conversational AI
Agents that take the call, and systems that read every call your people take.
RAG and knowledge systems
Answers from your own documents and data, with the source shown every time.
Evaluation, observability and guardrails
Look at your data first. Then measure what you found.
Not sure this is the right place to start?