Khanya
Cross-Platform AI-Assisted Product Build
Produit multiplateforme complet assisté par IA, développé depuis la phase conceptuelle jusqu'à l'architecture, l'implémentation, les intégrations, les tests et le déploiement en production.
Khanya démontre la capacité à transformer une idée produit complexe et ambiguë en un système opérationnel, couvrant conception logicielle, architecture de données, intégrations, garde-fous IA, tests et déploiement.
The same systems-thinking applies to business operations: complex processes can be broken down, structured, and turned into reliable internal tools.
Beyond superficial prototypes to working software
The objective behind Khanya was to engineer a resilient, cross-platform system capable of handling structured financial decision workflows, multi-channel messaging inputs, and AI-assisted analysis without compromising safety or data integrity.
What was built
Cross-Platform Application
Clean client interface delivering responsive web and mobile experiences.
Backend & Data Layer
Structured data models, security rules, and real-time state synchronization.
Messaging Integration Layer
Webhook handlers, conversation session store, and multi-channel routing.
AI Guardrail & Eval Pipeline
Prompt templating, schema validation, and systematic output evaluation.
Hard problems solved & commercial relevance
Controlled AI Behavior & Hallucination Defense
Standard LLM integrations risk outputting plausible-sounding but completely fabricated numbers or uncontrolled advice in sensitive workflows.
Important calculations and values were strictly bound to verified application data and deterministic business logic rather than allowing the AI model to freely invent figures.
AI can be safely embedded into sensitive operational workflows (intake, quoting, customer updates) without risking unvetted or hallucinated output.
Stateful Multi-Channel Messaging Workflows
Messaging platforms (WhatsApp, Telegram) present async network retries, duplicate messages, and stateless webhook calls that break linear workflows.
Engineered persistent conversation state, strict message deduplication, user identity mapping, and controlled multi-step state machines across external channels.
Enables robust operational assistants, automated customer intake, and field-worker submission tools via messaging apps without losing workflow state.
Multi-Layer Data Integrity & Business Rules
Relying solely on frontend validation allows corrupted states, concurrency conflicts, or invalid workflow skips to hit production databases.
Enforced validation and permission rules at application, API, and database security layers, ensuring illegal state transitions cannot be written.
Core operational rules are engineered into the software foundation rather than trusting that every team member remembers manual checklists.
AI Response Quality Evaluation
Deploying AI systems on the assumption that a prompt is 'good enough' leads to silent degradation and edge-case failures.
Built an internal evaluation harness that systematically tests AI outputs against defined rubric criteria, response benchmarks, and guardrail constraints.
AI implementations can be tested, monitored, and maintained with the same engineering rigor as traditional software.
Have a complex operational process that needs dependable engineering?
Let's map out your requirements, data rules, and integration boundaries in an Internal Systems Sprint.