At Eightfold, we don’t think of AI as a feature — we think of it as an operating model.
As our customer base expanded and our platform evolved across hiring, talent management, and workforce intelligence, the nature of support changed. Tickets became more complex, more contextual, and more tightly coupled to customer-specific configurations, data flows, and historical decisions.
Scaling support by adding headcount would have slowed us down and diluted expertise. Instead, we chose a different path: build high-quality, agentic internal systems that scale knowledge, context, and judgment — in production.
This led to the creation of Helixa, powered by our Digital Twin platform, which together form an internal intelligence layer that augments our support engineers with deep context, fast reasoning, and institutional memory.
Helixa: The agentic support engineer
Helixa is not a chatbot. It is a production-grade agentic system designed to operate like a senior support engineer — one who can reason, investigate, and synthesize across systems.
When a ticket arrives, Helixa doesn’t summarize it. It investigates it.
How Helixa thinks: The “brain” behind the agent
Unlike standard LLM wrappers, Helixa is built on a rigid Systematic Investigation framework (the “Debug Orchestrator”) that mirrors how our best engineers think.
Rather than guessing, Helixa follows a disciplined investigation workflow:
- Hypothesis generation based on ticket signals
- Targeted data discovery (what logs, configs, or flows matter)
- Read-only execution against live systems
- Semantic code analysis to trace logic and failure paths
- Root-cause triangulation grounded in evidence
This mirrors real engineering behavior — not prompt engineering.
What Helixa connects: The “knowledge” behind the agent
Support engineers succeed by connecting dots across many sources. Helixa does the same, in real time:
- Ticket context: Extracting critical parameters from the ticket description and screenshots upfront to target live system queries.
- Visuals: Using Vision models to “see” screenshots and identify the exact product page.
- Knowledge base: Synthesizing insights from our documentation and thousands of past resolved tickets to identify patterns.
- The codebase: Leveraging our code repositories as a data source to understand system logic and trace errors.
- Live system data: Safely querying production logs, configuration settings, and relevant customer data from live systems in real-time.
Finally, it shares the investigation and analysis results with the support engineer for verification, enabling them to send a high-quality response back to the customer with high confidence and velocity.
Measuring impact: The “truth” algorithm
We don’t trust AI blindly. We built an automated evaluation pipeline to measure Helixa’s actual impact.
Every time a human engineer solves a ticket, our system runs a “Challenge”:
- The comparison: An LLM (acting as a Principal Engineer) compares Helixa’s proposed root cause against the actual human resolution.
- The score: It assigns a confidence score (YES, LIKELY YES, NO).
- The result: We track exactly how often Helixa got it right.
As seen in our recent internal reports, Helixa acts as a force multiplier, accurately diagnosing issues in between 60% and 65% of cases before a human even picks up the ticket. This allows our engineers to validate a solution rather than hunting for the problem.
Digital Twin: The customer expert & organizational intelligence layer
While Helixa solves technical problems, our Digital Twin (DT) solves context problems. Support isn’t just about fixing bugs; it’s about understanding the customer.
Connecting the dots
The Digital Twin creates a dynamic profile of every customer. It aggregates their ticket history, configuration state, and recent interactions to answer high-level questions that used to take hours of research.
Use case: Pre-call preparation
Imagine a Customer Engineering team preparing for a critical review with a large enterprise client.
Instead of manually reviewing 50 recent tickets, an engineer can simply ask Digital Twin: “Can you share the main patterns of recurring issues raised by this customer?”
As one of our engineers recently shared:
“The response was spot on… It summarized the top recurring problems. For each category, Digital Twin provided a short explanation, referenced major Jira tickets, and showed relevant counts.”
This turns a “status update” call into a strategic consulting session. The engineer walks in knowing exactly what’s top of mind for the customer, backed by data, without spending the entire morning compiling spreadsheets.
Why this matters
Together, Helixa and the Digital Twin do something fundamentally different from traditional AI tools:
- Reduce onboarding time by making expertise instantly available
- Scale institutional knowledge without tribal loss
- Support team transitions with full historical context
- Maintain continuity even as products and teams evolve
This is not automation. It’s intelligence at scale.
The road to Helixa 2.0
We are just getting started. Our roadmap for Helixa 2.0 focuses on closing the gap between “Artificial” and “Human” intelligence even further, with a goal of increasing our resolution accuracy to 80-85%:
- Human-approved knowledge Injection: We are moving from raw data to curated wisdom. We are building a pipeline where “human-approved” knowledge from resolved tickets is formally indexed. If a human solves a novel issue, Helixa learns it instantly for the next time.
- Product area specialization: We are enhancing Helixa’s context window by grouping knowledge based on our Administrator Console structure. This grouping maps to specific navigation points for setting up custom configurations across different Eightfold products and features. By ingesting these configurations and their feature descriptions, and augmenting them with precise code pointers, Helixa gains deeper context to analyze tickets more effectively.
Conclusion
By combining the deep technical reasoning of Helixa with the broad customer context of the Digital Twin, we aren’t just automating support; we are elevating it. We are removing the drudgery of log-diving and history-checking, freeing our engineers to do what they do best: applying empathy and creativity to solve complex problems for our customers.