Turn technical knowledge into faster resolution
Technical support is diagnosis under time pressure. What decides the speed is how much of the diagnosis the organization holds, and how much only its most senior engineers carry.
With ClearMash, we found true partners for the journey, and together we have achieved Cellcom’s service goals while dramatically enhancing our ability to maximize our knowledge in every customer interaction.
The flexibility in accommodating changes, seamless integrations, and creative solutions made ClearMash a true partner.
Why this matters in technical support
Documentation describes the product as designed, not as deployed. The manual explains what the product does. The customer has an older release, an unusual configuration, an integration nobody documented and a symptom that appears in none of it.
Between them sits a diagnostic path that exists mainly as experience: the senior engineer who recognizes the pattern at once, and the first-line service agent who works the same symptoms from scratch, or escalates and adds a handover.
Release velocity makes it worse. Every version changes some behavior, deprecates something, fixes something and introduces a new failure mode. Support knowledge is written after the release rather than with it, so the gap between the product and the knowledge is permanent and roughly the length of your release cycle.
The costs accumulate in specific places: repeated diagnosis of issues already solved, escalations into engineering that interrupt development, customers running versions where the fix already exists, and self-service content that cannot tell the customer whether the article applies to their build.
- The challenge:
Fragmented product documentation
Release notes, manuals, internal wikis and ticket history each hold part of the picture.
With ClearMash Brain:Knowledge that keeps pace with releases
Support knowledge changes on the day the product does.
- The challenge:
Expert dependency
Resolution quality depends on who is on shift.
With ClearMash Brain:Shorter time to diagnosis
The right questions come first, whoever is on shift.
- The challenge:
Repeated troubleshooting
The same issue is diagnosed from scratch because the last diagnosis lives in a closed ticket.
With ClearMash Brain:Less repeated work
A problem solved once becomes applicable knowledge instead of a closed ticket.
- The challenge:
Version confusion
An answer that is correct for the current release is wrong for the one the customer runs.
With ClearMash Brain:Self-service that respects versions
Customers apply the fix written for the release they actually run.
- The challenge:
Escalations into engineering
Engineers spend time re-diagnosing problems support could have resolved with better knowledge.
With ClearMash Brain:Fewer escalations into engineering
Development keeps the time it currently spends re-diagnosing support cases.
- The challenge:
Weak AI troubleshooting
An assistant with product documentation but no diagnostic method offers plausible next steps in no useful order.
With ClearMash Brain:AI support with a method
Assistants triage in a defined order and know when to stop.
What is documented. How is remembered.
What: the product, the release, the known issues, the configuration options, the compatibility rules, the point at which support ends for a version. How: the order in which symptoms are eliminated, the question that resolves the ambiguity fastest, the check that saves an hour, and the threshold at which this becomes an engineering problem.
The What is usually written down somewhere. The How is not. It is carried by the people who have seen enough cases to have built a mental decision tree, and it leaves when they do.
That is why hiring more first-line staff rarely improves resolution rates and why an AI assistant built on product documentation plateaus quickly. Both were given the material and not the method.
Diagnosis follows a known path instead of a personal one
The reasoning experienced engineers apply becomes something the organization holds, applies and improves.
Symptoms lead to the questions that narrow them, in the order that narrows them fastest, with the version and configuration of this customer already accounted for. What comes back is the next step.
Known issues and workarounds reach first line while they are still current, so the customer is told about the fix in the release they can actually install rather than the one that shipped last week.
An AI agent working the same path can take the routine diagnosis as far as it reliably goes, gather what an engineer would have asked for, and hand over with the picture already assembled.
Version-aware answers
Every answer knows which releases it applies to, and says so before the customer tries it.
Diagnostic paths in a fixed order
The question that eliminates the most possibilities comes first.
Known issues that arrive on time
What engineering already knows reaches support before customers start reporting it.
Escalation with the work already done
When a case reaches engineering, it arrives with the diagnostic history attached.
What a resolution is built from
These are what narrow a fault to a fix, and support needs them held together rather than taken in turn.
- WHATWhat release and configuration this customer is actually running.
- WHICHWhich known issue matches these symptoms, and which workaround to give.
- WHENWhen the behavior changed, and which release changed it.
- WHEREWhere the fault sits: the product, the integration or the environment.
- HOWHow to diagnose it step by step without escalating.
Exports stopped completing. The behavior changed in a recent release.
First line narrows it and gives the workaround.
One symptom, several candidate causes. The facts about the build this customer is actually running arrive from the Brain, the other branches go quiet, and the one that matches becomes the workaround for the release they run - at first line.
What Releases · Issues · Compatibility
- Known issue
- Workaround 4.2
- Release notes
- Config options
- Compatibility
- Support window
- Error codes
- Environment
- Log signature
- Product limits
- Patch level
How Diagnosis · Checks · Escalation
- Triage order
- First question
- Elimination
- Repro steps
- Data to collect
- Check sequence
- Workaround use
- Escalation bar
- Handover pack
- AI agent skill
- Closure check
Context Decides which What and which How to act on, for this build
- Who This customer
- When Since 4.0
- Where Deployment
- Which Release run
- Why Why it broke
- Support engineersone diagnostic path
- Customersguidance for their build
- AI agentstriage in a defined order
- The diagnosis is kept
A live case, replayed with the Brain
A customer reports that scheduled exports stopped completing. The behavior changed in a recent release, there is a known workaround, and it applies only where a particular authentication method is in use.
Without that connection made in advance, first line reproduces the issue, checks the current documentation, finds nothing that matches, and escalates. With it, the first question narrows the case immediately, the workaround is offered with the release it applies to, and engineering is never involved.
Where a Brain sits between the product and the ticket
Release notes, known issues, the manual and the ticket history are all in the building. The diagnosis sits between them.
The Brain sits on top of your issue tracker and your support desk. Each system it reads holds a fragment of the picture, and no engineer gets to read all of them inside a call. The Brain reads them together, holds the elimination sequence your experienced engineers already run, and puts the next step beside the case.
Your tracker stays the tracker and your desk stays the desk, with the content where it is today. What the Brain adds between them, and carries into where the diagnosis shows up, is version awareness: an answer that knows which releases it applies to, and says so before a customer tries it.
What the Brain reads to narrow a fault
- Entitlement and installed release Which release and edition this customer runs, and what the contract covers.
- Release notes and change history What each release changed, deprecated or fixed, which is why answers move.
- Known issues and workarounds What engineering already knows is broken, and what holds until the fix.
- Documentation and manuals The product as designed, where a diagnosis starts and rarely where it ends.
- Ticket history and past resolutions How this symptom was resolved before, including the case nobody wrote up.
Knowledge orchestration layer
ClearMash Brain
What · How · Context
Which release, which fault, and the next question.
Where the diagnosis shows up
- The support desk, mid-case The question that eliminates the most, beside the ticket that is open.
- The public support site The same path for a customer at two in the morning, with the release marked.
- Engineering trackers An escalation that arrives worked rather than reported, in your tracker.
- AI agents on first response The method as far as it goes, then a handover with the history attached.
- In-product help Guidance for the release the customer runs, inside the product itself.
One diagnostic path for the engineer on shift, the customer at two in the morning and the AI agent taking first response.
Support engineers
A diagnostic path that reflects what the best engineer would do, available to whoever picks up the case.
- Faster time to first useful step
- Less version guesswork
- Fewer escalations
- Faster onboarding onto new products
Customers
Guidance that matches their build, and self-service that can tell them whether an article applies.
- Faster resolution
- Fewer failed attempts
- Less repetition of the same details
- Greater confidence in the product
AI agents
Product knowledge plus a defined diagnostic method, with a clear point of handover.
- Structured triage
- Fewer irrelevant suggestions
- Complete handover to a human
- Consistent first response
Tangible impact
The changes a working Brain makes, in the order a support organization meets them.
Business impact
- Reduction in expert escalations 65%
- Reduction in inquiry handling time 28%
- Increase in self-service adoption 30%
- Reduction in new employee onboarding 70%
- Increase in customer satisfaction 15%
- Increase in first contact resolution (FCR) 40%
- Increase in employee satisfaction 62%
- Reduction in compliance violations 14%
Technological impact
- AI agent accuracy from 85% to 97%
- Increase in AI agent speed 25%
- Reduction in AI costs 74%
Additional measures
Alongside them, the measures a support desk already has in its tracker:
- Time to diagnosis
- first-contact resolution on technical cases
- escalation rate into engineering
- reopen rate
- cases closed in self-service without contact
- the proportion resolved on the first suggested step
A support desk can read every one of them out of the queue it already runs, which makes them a fair test of whether diagnosis improved.
Expert diagnosis is knowledge
The part that writes down most easily is the part that costs the most time. Before the final judgment call on a hard case there is a repeatable elimination sequence, one your experienced engineers run so quickly they no longer notice they are running it. That sequence is knowledge, and it can be written down, reviewed and improved.
The judgment call stays with the expert, and capturing the sequence is what keeps them available to make it, because the elimination that comes before it now runs without anybody senior in the room.
A diagnostic sequence written once serves the engineer on shift, the customer trying to self-diagnose at two in the morning, and the AI agent handling first response.
Why ClearMash for support
ClearMash manages the diagnostic know-how that decides how long a case takes, beside the product knowledge it depends on.
It keeps them aligned to the release your customer is running, and serves them to engineers, self-service and AI agents from one place.
Reviewed like code
Diagnostic knowledge changes through named review, carries its version, and is scoped by role for engineers, customers and AI agents alike. Ticket history and telemetry stay in your tooling. The Security page holds the certifications and the specifics.
See a diagnosis that knows which release it applies to
Book a demo and we will show you an elimination sequence running as knowledge: the next question to ask, the releases an answer applies to, and the same path put in front of a customer.
Prefer to start with the numbers? The impact estimator works them out