From Code to Clarity: Outcomes Delivered by Cubyts AI

Left unaddressed, these challenges can lead to spiraling delays, rising operational risk, and mounting tech debt that slows down the entire product delivery engine:

• Misaligned Deliveries – When teams diverge from the product roadmap or customer expectations, it leads to delayed releases, unmet commitments, and customer dissatisfaction.

• Compliance Gaps – Without real-time checks, process and policy violations often go unnoticed until audits or failures occur, exposing the business to legal and operational risks.

• Technical Inconsistencies – Inconsistent adherence to tech standards creates rework, slows onboarding, and increases long-term maintenance costs.

• Hidden Code Dependencies – Dependencies buried deep in the codebase can cause last-minute surprises during integration or deployment, disrupting timelines and stability.

• Contractual Deviations – Gaps between what was committed in a SoW or customer agreement and what’s delivered can lead to revenue leakage, renegotiations, or even churn.

• Customer Escalations – When issues can't be traced back to their root cause, support resolution slows down, negatively impacting user trust and retention. 

To address these, Cubyts employs a multi-assistant architecture, powered by specialized AI agents that work together across your software development lifecycle. At the core of this architecture are three intelligent assistants:

• Flags Assistant – Focuses on real-time detection of feature, technology and compliance drifts, ensuring delivery stays aligned and audit-ready.

• Repo Assistant – Links execution with documentation to surface deviations and enforce tech standards.

• Trace Assistant – Connects strategy to code by mapping dependencies and tracing outcomes back to execution.

In addition to solving these operational challenges, teams using Cubyts have seen measurable business impact:

• 25% improvement in development efficiency through reduced rework, automated detection, and faster decision-making.

• 50% reduction in tech debt by proactively catching and resolving issues in real time before they accumulate.

Misaligned Deliveries and Compliance Gaps

Challenge: Ensuring that teams deliver on committed features with acceptable drifts in real-time while staying compliant with regulatory requirements.

How Cubyts Helps: The Flags Assistant continuously monitors workflows and commits, identifying feature drifts and compliance gaps before they escalate. By integrating with SDLC tools, it ensures teams are always audit-ready and aligned with business objectives.

The Flags Assistant actively monitors the software development lifecycle for emerging risks, process, feature, and tech drifts, and inefficiencies. It analyzes real-time inputs from across tools to help teams address issues before they snowball.

Powered by:

• Risk Agent – Leverages the outcomes of the semantic data graph and code graph to discover risks in the SDLC workflow as work happens.

• Impact Prediction Agent – Uses data from the Risk Agent to assess the impact of unresolved risks.

• Insights Agent – Extracts multi-dimensional insights, such as developer behavior and issue trends based on the code introduced in the current PR.

Technical Inconsistencies and Contractual Deviations

Challenge: Correlating execution deviations with complex contractual clauses and ensuring adherence to technical standards without burdening developers.

How Cubyts Helps: The Repo Assistant links design, development, and process documentation to execution, ensuring deviations are flagged early and technical compliance is enforced effortlessly.

The Repo Assistant provides deep understanding and traceability of documentation. It links context between design, requirements, decisions, and retrospectives to help teams avoid rework and misalignment.

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• Document Context Agent – Leverages the outcomes of the semantic data graph and the code graph to provide context to various documents generated during the SDLC workflow.

Hidden Code Dependencies & RCA for Customer Complaints

Challenge: Ensuring that new code changes don’t break existing dependencies while tracing customer complaints back to the exact code area for quick resolution.

How Cubyts Helps: The Trace Assistant establishes deep code-to-feature mapping, enabling teams to resolve dependencies proactively and accelerate issue resolution by pinpointing the root cause of failures.

The Trace Assistant connects the dots between strategy and execution. It reveals how product decisions flow through tickets, commits, tests, releases, and user outcomes—helping teams track and improve delivery over time.

Powered by:

• Semantic Data Graph Agent – A connected data graph that draws data from the connected SDLC tools to give the data relationships and context for any insight/interpretation.

• Semantic Code Graph Agent – Language-specific abstract syntax tree augmented by complex code relationships (and references) and decorated by human-understandable explanation to map the journey of code and establish relationships between features and code.

Additional Platform-Level Agents

• Security Agent – Responsible for authentication (email-based, social logins, SSO) and data security of resident data and data in transit, ensuring ethical data practices in alignment with SOC principles.

• RBAC Agent – Responsible for role-based access control of features offered by the assistants and roles associated with a platform user.

How Cubyts Multi Assistant, Multi Agent Eco System Work Together to Assist Development Teams

Cubyts' assistants collaborate through a tightly integrated multi-agent system that ensures every decision is grounded in real-time context. Here’s how:

• Cross-Agent Collaboration: For example, a flag triggered by live events (via integrated tool listeners) is enriched with context from the Semantic Data Graph Agent, linked to relevant code via the Code Graph Agent, assessed by the Risk Agent, analyzed for impact by the Impact Prediction Agent, and controlled through access rules by the RBAC Agent.

• Explainable Intelligence: Every insight or suggestion is backed by traceable evidence, helping teams understand root causes, rationale, and potential resolutions.

• Continuous Learning Loop: Developer actions—whether addressing a flag, modifying documentation, or resolving a risk—are fed back into the system to improve prediction accuracy and generate better insights moving forward.

Cubyts is built on the principle that an AI assistant in software development must act like a responsible teammate—informed, up-to-date, and honest when it doesn’t know something.

AI Approaches at Work in Cubyts

To enable this, Cubyts integrates multiple layers of artificial intelligence—each serving a distinct purpose and working together to deliver grounded, contextual assistance:

• Heuristic Models: These are used extensively to interpret datapoints extracted from SDLC sources to detect deviations that LLMs might not be the best fit for, especially when quick, rule-based judgments are more reliable.

• Statistical Models, Semantic Embedding & Vector Indexing: These are used where data patterns become the central pivot point for analysis. Used by the Flag and Repo assistants to understand and relate different documents. By indexing documents with context using embeddings, we can retrieve the related documents on demand for analysis and decision making.

• Generative AI (LLMs): Powers semantic code evaluation & generation, contextual summarization, flag interpretation, and user-facing responses. We use LLMs behind the scenes to generate natural language insights, describe trace dependencies, and interpret complex document patterns.

• Retrieval-Augmented Generation (RAG): Prevents hallucination by always retrieving relevant data from trusted SDLC sources—like Git, Jira, Confluence, and CI/CD pipelines—before generating any response. The generative layer never works in isolation. Each AI layer (Flags, Repo, Trace) communicates with the others. For example, a flag triggered by a CI/CD failure can be enriched by Repo context and verified against Trace lineage before it's presented to a user.

By grounding every output in your software development reality, Cubyts minimizes hallucination and ensures trustworthy automation across your engineering workflow.