Kahuna Labs Blog

By John Ragsdale, SVP Marketing, Kahuna Labs

A customer reports intermittent failures. There’s a vague error string. A screenshot. A “started happening last week.” Your team replies quickly, asks for logs, shares a few standard steps. The customer responds… slowly. They’re busy. They’re not sure where to find the right file. The thread stretches into a day, then two.

Meanwhile, the engineer assigned to the case is doing what good engineers do: trying to reconstruct context from fragments. Skimming old cases. Checking release notes. Asking a senior teammate, “Have you seen this before?” The case isn’t “hard” yet—but it’s already drifting. Progress is measured in messages, not evidence.

Then the calendar pressure hits. A renewal is near. A launch is blocked. Someone senior gets looped in on the customer side. And suddenly the temperature changes: “Can you escalate this?” Not because the issue is impossible–a code bug is indicated–but because confidence is gone. The customer doesn’t feel momentum. Your team doesn’t feel leverage. Everyone is operating with partial information and rising stakes.

This is what makes escalations so maddening: they often feel like a moment, but they’re really a trajectory—set in motion early, by missed signals that were present long before the escalation email arrived.

This is where predictive AI becomes less about “responding faster” and more about preventing the conditions that create escalations in the first place.

The Real Reasons Escalations Happen (Beyond “It’s A Hard Issue”)

In complex product support, escalations tend to come from a few recurring root causes:

1) The first few steps are wrong—or delayed

Most escalations start quietly. The initial triage misses a key diagnostic. The first response is generic. The engineer spends hours recreating context. Customers don’t escalate because the issue is complex—they escalate because they feel uncertainty and slow progress.

2) Tribal knowledge is inaccessible when it matters

Your best engineers carry “pattern memory” in their heads: which symptoms imply which root causes, what to ask next, what diagnostic will quickly collapse the search space. When that knowledge stays trapped in people—or buried in raw tickets—other engineers take longer, take more loops, and escalations happen more often.

3) Customer reality is unique, and documentation can’t keep up

Even strong knowledge bases cover only a fraction of real-world scenarios because customer environments vary significantly (configs, integrations, versions, constraints). Escalations spike when the support model assumes “one canonical flow” but reality is “a thousand variations.”

4) Support is operating without a map

Many teams are effectively navigating a maze without a current floor plan: fragmented tools, inconsistent ticket narratives, missing context, and no shared visibility into how problems typically evolve from first symptom to resolution.

Signals That Predict Escalation (Often Hours or Days Earlier)

Escalation risk usually telegraphs itself through patterns like:

• Stalled progression: multiple back-and-forth cycles without net-new evidence (no new diagnostics, no narrowing hypotheses).
• High “research load”: long time spent gathering context, searching past tickets, or asking internal SMEs what to do next.
• Mismatch of actions: customer-doable steps sent as engineer-only tasks (or vice versa), creating delays and frustration.
• Low-quality precedent: the “similar past tickets” exist, but they’re noisy, incomplete, or not aligned to the current stage of troubleshooting.
• Version/config sensitivity: the same symptom behaves differently across versions or specific configurations—so generic “best practices” fail.

Individually, these signals feel like normal variance. Together, they’re a pattern: this case is drifting toward escalation.

How Predictive AI Prevents Escalations: From “Ticket Handling” to “Path Guidance”

The most impactful shift is moving from AI that answers questions to AI that understands the troubleshooting journey.

At Kahuna, the foundation is a Troubleshooting Map™ built from historical ticket journeys—where tickets are reconstructed into step-by-step “snapshots” and clustered into repeatable paths. That means the AI can recognize not only “what this issue is,” but what stage you’re in and what paths usually succeed from here.

Three preventative strategies become possible:

1) Predict escalation risk by detecting “drift” early

When a case starts to diverge from successful historical paths—too many loops, missing diagnostics, delayed next steps—predictive alerts can trigger intervention before the customer forces it. (This is very different from simply routing “angry customers” faster.)

2) Recommend the next best step with confidence, not guesswork

Not all guidance deserves automation. Kahuna-style approaches use scoring—Credibility Score™, Completeness Score™, and a Complexity Score™ for recommended paths—so engineers can see when the system is drawing from dense, high-quality precedent versus thin, ambiguous signals.

3) Prevent escalation by removing effort, not adding process

When confidence is high, preventative automation can do the work that typically causes delays: auto-collect diagnostics, propose probing questions, and standardize decision flows—so the case moves forward with momentum and clarity.

The Preventative Mindset Shift

Escalation prevention isn’t a “new policy.” It’s a capability. The support models that win in the next era will act less like reactive firefighters and more like orchestrators—using AI to make the invisible visible: the patterns, the paths, the signals, and the next steps that keep cases from ever becoming escalations.

The goal isn’t to eliminate every escalation. It’s to ensure escalations happen for the right reasons—true novelty and exceptions—not because the system couldn’t see what was coming.

Escalations have a strong correlation to customer satisfaction with support, and high rates of escalation can impact likelihood of renewal. Leveraging AI to prevent escalations from happening will eliminate a lot of friction in the customer experience, and enable support to be seen as a relationship builder, not an element that drives difficult conversations (and potential cost concessions) come renewal time.

By John Ragsdale, SVP Marketing, Kahuna Labs

Every so often, a piece of research captures a shift you can feel happening in the market.
Constellation Research’s new paper, Augmenting and Accelerating Frontline Productivity, by industry veteran R “Ray” Wang, does exactly that. It’s one of those “Big Idea” moments that crystallizes what many of us have been sensing: the next leap in enterprise performance won’t come from automating the back office — it will come from empowering the frontline.

The report frames “frontline productivity” as an emerging market category focused on increasing decision velocity—equipping frontline teams with AI that can augment judgment, accelerate actions, and improve consistency and quality at scale. Kahuna Labs is the perfect example of this category.

The Frontline Is the New Growth Engine

For decades, innovation has flowed from the top down. Executives got dashboards, managers got analytics, and operations got automation. But the people at the edge of the business — the service engineers, technicians, and customer-facing teams — have too often been left behind.

That’s starting to change. Ray’s analysis makes a compelling case that AI is reshaping the structure of work itself. The old command-and-control pyramid is collapsing into what he calls the “diamond organization” — smaller teams, more autonomy, and more leverage from digital labor.

It’s not about replacing people. It’s about giving them decision velocity: the ability to make faster, smarter, and more contextual choices at the moment of truth. And nowhere is that more critical than on the front lines — where a single decision can make or break a customer relationship.

From Automation to Advice

Ray’s framework for AI maturity really resonated with me: from augmentation, to acceleration, to automation, to agents, and finally to advisors.

Most organizations are still stuck somewhere in the middle. We’ve built tools that do more — but we haven’t yet built systems that understand more. The leap from automation to advice is where the real transformation begins.

That’s when AI stops being a tool for efficiency and starts becoming a partner in judgment. It’s when the machine isn’t just executing instructions but anticipating what a skilled human would do next — using context, history, and intent to guide decisions.

That’s what frontline productivity in the AI era really means.

Why Context Is Everything

Here’s the hard truth: not all AI is capable of delivering a productivity leap.

Legacy SaaS systems were never designed for frontline work. They live outside the organization’s network, disconnected from the data that makes decisions meaningful — things like customer configuration, product version, or the subtle differences between one client environment and another.

As Ray puts it, “Legacy SaaS AI lacks contextual relevancy.” Without that, AI can’t deliver precision or trust.

The future belongs to in-network AI — systems deployed inside the enterprise environment, trained on its own tribal knowledge, and fluent in its unique operating reality. These systems don’t generalize. They personalize. They reason in context.

That’s what enables what Ray calls decision automation — AI that doesn’t just analyze, but acts, learns, and improves with every interaction.

From Insight to Impact

This shift has enormous implications for how we think about productivity. The goal isn’t just “doing more with less.” It’s about achieving what Constellation calls exponential efficiency — breakthroughs that are ten times faster, better, and cheaper, simultaneously.

And it’s not a theory — we’re seeing it play out in real organizations. When frontline teams gain AI that’s context-aware, predictive, and prescriptive, they stop firefighting and start foresighting. They move from reacting to issues to preventing them.

Most importantly, they’re free to focus on what humans do best: empathy, creativity, and problem-solving.

The Human-AI Partnership

In my conversations with business leaders, I often remind them that AI isn’t the end of human work — it’s the beginning of better human work.

The question isn’t what can we automate? It’s where do we want humans to shine?

Ray’s seven-factor model for balancing “machine scale” and “human touch” should be required reading for every executive designing next-generation services. It reminds us that the point isn’t to eliminate people from the process — it’s to elevate them within it.

AI can manage repetition, volume, and complexity. But humans still own creativity, empathy, and trust. The organizations that thrive in this new era will be the ones that know how to orchestrate both.

A Moment of Alignment

Having known Ray for over twenty years, he’s rarely wrong about where the industry is heading. This paper is another example of his ability to see the future a few years early.

For those of us working to bring AI to the front lines, it’s both validation and motivation.

The message is clear: the future of productivity belongs to frontline workers. The companies that get there first — with AI that’s deployed in-network, context-aware, and human-centric — will define the next generation of enterprise performance.

That’s a challenge worth rallying around. And it’s one I’m proud to be part of.

Here’s a link to access the full report, “Augmenting and Accelerating Frontline Productivity.”

Eliminating the Quiet Tax of Backlog Management

By John Ragsdale, SVP Marketing, Kahuna Labs

Every support leader knows the feeling: you make real progress—then the backlog creeps right back.

Not because ticket volume exploded overnight. Not because your support engineers suddenly got slower. But because the queue is full of a specific kind of work: long-running, context-heavy cases where the “next step” isn’t obvious, and forward motion depends on reconstructing a story from fragments.

Those tickets carry a quiet tax. Every time someone picks one up, they start by paying it again: reread the thread, re-assemble context, re-search for precedent, re-ask the same foundational questions, re-check whether anything has changed since the last touch. It’s responsible work, but it’s also compounding work—effort spent rediscovering what the organization already knows.

That tax rarely shows up in productivity dashboards. But it’s why backlogs persist even when teams are working at full capacity.

Reducing backlog without hiring more engineers starts with one mindset shift: stop treating every ticket like a standalone investigation. Instead, treat your ticket history as a living body of troubleshooting journeys—so that whenever your organization learns something new, you can apply it to every similar open case and move the queue forward with less effort per ticket.

1) Start by Attacking the Biggest Hidden Time Sink: Re-Reading and Re-Research

In complex support environments, the real time drag isn’t writing customer communications or documenting the case. It’s the time engineers spend reconstructing context:

• parsing long case histories,
• hunting for “similar” tickets,
• figuring out what stage they’re actually in,
• and deciding what to do next.

This is why even strong teams can feel stuck. The backlog becomes a knowledge swamp: the answers may exist somewhere in history, but they aren’t easy to find, trust, or apply quickly—especially for engineers who didn’t live the original case.

The fastest backlog wins come from making progress cheaper: less time spent rebuilding context, more time spent advancing the case.

2) Break Work Into “Snapshots,” Not Tickets

Similarity search often fails because tickets are messy: multiple phases, resets, tangents, missing diagnostics, and a lot of human conversation. Comparing entire tickets to entire tickets produces “close” matches that still don’t tell you what to do next.

A more useful unit is the snapshot: a point-in-time representation of the case—what’s known, what’s been tried, what the customer environment looks like, and what evidence is available right now.

When you can identify the current snapshot, you can stop asking, “Have we seen this issue before?” and instead ask:

“Have we seen this state before—and what reliably moves the case forward from here?”

That shift alone reduces the time spent reinventing the wheel across the queue.

3) Build Paths, Not Just a Library of Articles

Traditional knowledge bases struggle with enterprise support because real environments vary constantly. Configurations differ. Integrations behave differently. Versions and edge cases compound. The result: static documentation covers the generic paths, while the backlog fills with everything else.

What scales better is a Troubleshooting Map™: a set of clustered, repeatable paths that describe how issues actually progress from symptom → diagnostics → resolution.

Instead of “here’s an article,” you get something closer to:

• “Tickets that look like this tend to follow these paths,” and
• “From this snapshot, these next steps usually narrow the journey fastest.”

This is the difference between a library and a navigation system.

4) Use Confidence Gating So AI Reduces Effort (Instead of Adding a Review Step)

AI can help—but only if it doesn’t create extra verification work.

A practical pattern is confidence-based guidance. Recommendations should be accompanied by clear signals about why they’re being suggested and how reliable they are—based on the quality and density of the underlying precedent.

When confidence is high, the system can do more: propose the next best diagnostic, standardize customer questions, or automate routine evidence collection. When confidence is low, it should behave differently: flag uncertainty, suggest options, and invite human judgment. This is what Kahuna’s Confidence Score™ automates.

Backlog reduction depends on this discipline. “AI suggestions” that engineers must validate from scratch don’t save time. Confidence-gated guidance does.

5) The Backlog Multiplier: Apply New Paths to Every Open Ticket

Here’s the compounding move that changes the economics of backlog work:

Every time Kahuna identifies new snapshots and new paths on the Troubleshooting Map, that knowledge can be applied to the current queue. Instead of helping only the next ticket that comes in, new insight helps all open tickets that resemble the newly learned snapshot.

That means you can automate the continuous scan of open/backlogged cases and:

• identify which tickets match a newly discovered snapshot,
• recommend the next best step,
• auto-collect missing diagnostics where appropriate,
• and prompt the right customer action to unblock progress.

The impact is immediate: hundreds of hours saved that would otherwise be spent re-reading old threads and re-searching the past to see whether anything new has occurred.

This is how backlog work stops being linear. Learning becomes a force multiplier.

6) Make It a Closed Loop, Not a One-Time Cleanup

“Backlog blitzes” feel good, but the impact is short lived. The queue comes back because the dysfunctional dynamics creating the backlog haven’t changed.

The durable approach is an automated, continual process:

1. Observe how tickets are actually solved (journeys, not just outcomes).
2. Distill those journeys into snapshots + paths.
3. Apply new learning to the open queue automatically.
4. Improve as outcomes confirm (or correct) the recommended paths.

Over time, you reduce the percentage of cases that require full ground-up investigation. Engineers spend less time rediscovering known routes and more time handling true exceptions—the work only humans can do.

Backlogs don’t shrink sustainably by pushing teams harder. They shrink when you remove the quiet tax that keeps expensive work repeating—by turning historical troubleshooting into reusable paths, and applying every new insight across the queue the moment it appears.

How Enterprises and AI Builders Co-Evolve

By Sudhamsh Goutham Teegala, Engineering @ Kahuna Labs

Why adopt AI?

Peer pressure? Hope for miracles? Or proven value demonstrated by competitors?

For most organizations, the motivation often lies somewhere in that mix. But history tells us that new technologies rarely change a single company – they change industries. Large language model (LLM)-based AI systems are poised to do exactly that for one of the most communication-driven sectors in the enterprise world: customer and product support.

Support functions have always sat at the intersection of human expertise and complex information. AI, with its ability to interpret, contextualize, and respond in natural language, is uniquely suited to reimagine how support is delivered, scaled, and learned from. The shift is already underway – not just in adoption, but in how enterprises think about the very act of supporting their products and customers.

Adoption versus Transformation

When we talk about “adopting AI,” what are we really doing? Are we automating a single process, or are we solving an end-to-end problem?

New technologies often “grow” alongside their own adoption. As more people use a new technology, both the users and the technology evolve toward each other. Most enterprises today are still in the early stages of this curve – replacing parts of their workflows with AI-enhanced stages or solutions.

But true transformation happens when the technology matures enough – and the organization becomes ready enough – to reimagine the whole system. In product support, this may soon mean moving away from today’s ticket-driven, reactive models toward predictive, AI-first ecosystems where discovery, diagnosis and resolution flow seamlessly.

That kind of change can sound unsettling. It challenges familiar roles, teams, and even career paths. Yet, the enterprises best positioned to lead their industries forward will be those that adopt AI as a co-evolutionary force where people, processes, and tools continuously shape each other. The companies that treat AI as a partner in organizational learning, rather than a replacement tool, will be the ones to define the new industry norms.

The Builder’s Dilemma

Then there is the parallel challenge for those of us building AI systems for these enterprises. When we design tools for customer and product support, what exactly are we optimizing for?

Do we start by deeply understanding current workflows – mapping pain points, inefficiencies, and blockers – and then build solutions that fit neatly into those patterns? Or should we focus on the essence of the problem itself, looking beyond the existing process? That might mean asking what information exists (and where), what an ideal resolution looks like, and whether there is a faster AI-native path to get there.

This is not an academic question. Every conversation we have with our users subtly shapes the direction of our tools, and by extension, the future of their organizations. The questions we ask are not neutral; they influence what our tools become and how our users’ processes evolve.

Over time, by working with multiple enterprises and observing their support journeys, AI builders begin to see patterns. Some organizations need tools that integrate smoothly into highly complex systems augmenting what already works. Others, especially those in periods of growth or transformation, benefit from AI-first support paradigms that prescribe new ways of working entirely.

This interplay, between enterprise readiness and AI maturity, is where the real progress happens.

A Co-Evolutionary Future

In the end, AI adoption is not about replacing humans or processes; it is about co-evolution. Enterprises evolve their structures and mindsets to make room for new capabilities, while AI builders evolve their systems through deeper understanding of human and organizational context.

Over time, these feedback loops will redefine what “support” even means. We may move from reactive helpdesk models to more forward-looking predictive recommendation systems. Ultimately, this leads to the creation of proactive, self-correcting ecosystems capable of diagnosing and resolving problems, while also sharing knowledge for broader reuse.

The companies that thrive in this new era will not simply “adopt” AI – they will grow with it. And those of us building these tools have both the privilege and the responsibility to guide that growth wisely. Because we are not just creating software; we are helping industries discover new ways to think, to solve, and to support.

Transforming Support Tickets Into Reusable Knowledge Assets

By Shanu Vashishtha, Deep Learning Engineer, Kahuna Labs

The Fundamental Challenge

In traditional support operations, engineers receive tickets without context. They must begin from uncertainty: requesting diagnostics, gathering specifications, and iteratively narrowing the problem space.

The critical issue emerges after ticket closure. Closed tickets accumulate with highly variable documentation quality. A ticket might contain initial diagnostic questions, a reference to log collection, followed by “Scheduled a Zoom call” and “Closing as resolved”—with no resolution details, verification steps, or technical narrative.

This represents a fundamental failure of knowledge capture. The resolution information exists but not where organizational processes require it: within the ticket itself. Incomplete documentation translates to duplicated diagnostic effort, extended MTTR, and the inability to build scalable institutional knowledge.

The Completeness Score™: A Systematic Quality Metric

Kahuna’s Completeness Score is a 0-5 rating scale that measures how thoroughly the troubleshooting process was documented. The central question: can another engineer, encountering a similar issue six months later, follow the documented diagnostic trail and resolve the issue based solely on the ticket information?

The Completeness Scale

Score 0: No Engineer Engagement – No messages from support engineer, or only automated responses. No troubleshooting information available.

Score 1: Minimal Engagement – Basic acknowledgment or initial information requests without substantive troubleshooting progression. Confirms the issue existed but provides no pathway toward resolution.

Score 2: Partial Investigation – Some troubleshooting effort evident, but significant gaps in documentation or resolution verification. Provides directional hints but lacks detail for reliable replication.

Score 3: Standard Documentation – Reasonable troubleshooting with specific steps and technical detail, but missing some elements. Provides a solid starting point, though engineers may need to supplement with additional diagnostics.

Score 4: Comprehensive Documentation – Thorough step-by-step troubleshooting with clear progression, multiple diagnostic checks, and strong resolution documentation. Provides a reliable playbook for similar issues.

Score 5: Exemplary Documentation – Complete, professional-grade diagnostic documentation with all relevant artifacts, clear resolution process, and verified outcomes. Represents a reusable solution pattern requiring minimal adaptation.

Critical Modifier: When evidence exists that a Zoom call, phone call, or remote session occurred but no transcript or summary was documented, the score is reduced by 1 point (floor of 0). Undocumented synchronous work is functionally equivalent to work that never occurred from an organizational knowledge perspective.

Operational Applications

Historical Analysis: Efficient Knowledge Retrieval

When engineers search historical tickets, result sets frequently contain 50-500 potentially relevant tickets. Without quality metadata, each appears equally promising, requiring sequential evaluation.

The completeness score enables immediate quality-based filtering:

– Score 4-5: High-confidence documentation with verified resolutions

– Score 3: Adequate documentation with some gaps

– Score 0-2: Insufficient documentation for knowledge transfer

This transforms historical ticket databases from undifferentiated archives into curated knowledge repositories where high-scoring tickets serve as reusable solution templates.

Active Ticket Resolution: Pattern Matching

For newly assigned tickets, similarity searches with completeness scoring return quality-weighted results. Engineers can rapidly assess pattern prevalence, implement validated solution methodologies, identify case-specific variations, and compress resolution timelines from days to hours.

For eg: An engineer searching for “API 503 errors” finds 12 relevant tickets. Filtering for Score ≥ 4 yields 2 high-quality matches. The highest-scoring ticket documents complete diagnostic steps, root cause analysis, resolution implementation, and customer verification—enabling resolution in hours rather than days.

Proactive Completeness: Real-Time Quality Improvement

Beyond retrospective analysis, the completeness scoring system operates proactively during the active ticket’s lifecycle. The system calculates completeness scores in real-time as engineers document their work, identifying gaps and prompting support engineers to provide additional documentation before ticket closure.

This proactive approach creates a win-win scenario: not only do organizations achieve better-documented tickets for audit and knowledge purposes, but this wealth of information significantly increases the accuracy of the Troubleshooting Map moving forward. When tickets contain comprehensive diagnostic narratives, resolution steps, and verification details, the underlying AI systems can more effectively identify patterns, map issue relationships, and generate actionable recommendations for future similar cases.

Organizational Impact

Systematic completeness scoring generates cascading benefits:

1. Documentation Quality Improvement: Objective criteria transform abstract directives into measurable standards
2. Knowledge Accumulation: High-scoring tickets function as reusable templates, creating compound returns on diagnostic effort
3. Data-Driven Assessment: Quantitative evaluation of documentation capabilities independent of other performance dimensions
4. Customer Experience: Reduced time-to-resolution through rapid access to validated solutions
5. Documentation Debt Visibility: Aggregate metrics reveal systemic documentation issues for targeted improvements

Conclusion

The Completeness Score introduces systematic quality metadata to historical ticket data, enabling efficient knowledge retrieval, validated solution reuse, visible quality standards, and organizational learning. This transforms support operations from an experience-based model (knowledge in individual memory) to a documented model (knowledge systematically captured and retrievable).

The cumulative impact: reduced resolution times, decreased duplicated effort, improved knowledge transfer, and systematic accumulation of institutional expertise in retrievable, actionable form.

Implementation Note

The completeness scoring system employs AI-based evaluation to automatically assess every ticket against objective criteria: troubleshooting steps, diagnostic artifacts, hypothesis testing, remote session documentation, knowledge base references, resolution steps, and customer confirmation. This automated approach provides scalability that manual review cannot achieve, making historical knowledge systematically accessible for future diagnostic work.

Build vs. Buy is a common dilemma with AI projects. There are a lot of factors at work: company culture, privacy concerns, and cost of buying and implementing technology. In this webinar, Kahuna Labs CEO, Sanjeev Gupta, dives into the build vs. buy challenge, citing examples from Kahuna Labs customers, to help support leaders navigate this decision to arrive at the best outcome for the company, the department, and ultimately, your customers.

If you are already building in-house AI, learn how you can achieve the right build+buy collaboration.