Problem Solving & Technical Approaches

How I Solve Real Technical Challenges.

Technology is only as valuable as the friction it removes. Explore how I dissect complex business bottlenecks into clear technical solutions and reliable software.

Interactive Diagnostic: Select Your Current Bottleneck

What is holding your business operations or tech stack back?

Recommended Solution Architecture: Feasible in 1 to 3 Weeks

24/7 WhatsApp/Instagram AI Agent with Persistent Context & Instant Human Takeover

Recommended Stack:

Meta Cloud APIn8nClaude 3.5 SonnetSupabase

Expected Business Outcome:

< 5s response time, 60%+ automated qualification, zero lost off-hour prospects

Discuss Building This Architecture

Proven Technical Solutions (6)

Problem → Approach → Solution → Value

LLM ArchitectureCategory: AI Engineering

Structuring AI & LLM Systems from Ambiguous Ideas

Challenge 01

1. The Client Bottleneck

Clients knew they wanted to leverage ChatGPT and AI in their business, but had no clear structure for where AI fits, how to constrain model behavior, or how to avoid hallucinations.

2. Engineering Approach

Decomposed business processes into discrete functional steps. Mapped where deterministic logic (code/database) is necessary versus where non-deterministic reasoning (LLMs) adds value. Designed prompt topologies, few-shot guardrails, and schema validation.

3. The Production System

Built scoped AI micro-services with structured JSON outputs, system boundary constraints, and fallback human workflows rather than unbounded open-ended chat widgets.

4. Verified Business Return

Turned vague client enthusiasm into reliable, predictable production systems with zero catastrophic hallucinations.

Key Takeaway: LLMs should never handle what deterministic code can solve faster and cheaper. Scope AI strictly to reasoning and synthesis.

Technologies:OpenAI APIClaude APIJSON Schema / ZodTypeScriptPrompt Engineering

Solve This For Your Business

Knowledge ExtractionCategory: Data & AI

Extracting Structured Knowledge from Unstructured Data

Challenge 02

1. The Client Bottleneck

Businesses were sitting on mountains of raw consultation notes, customer emails, and document PDFs, unable to extract clean, actionable insights into their database.

2. Engineering Approach

Built a chunking and extraction pipeline utilizing strict function/tool calling and Pydantic/Zod schemas. Instructed models to enforce exact field typing, enum categorization, and confidence scoring.

3. The Production System

An automated document and communication parser that ingests incoming raw text, extracts structured entities (names, budget, intent, pain points, urgency), and populates internal database records automatically.

4. Verified Business Return

Saved dozens of manual data-entry hours each week, creating structured databases from what was previously discarded conversational text.

Key Takeaway: Never ask an LLM for freeform text when you need database records. Use strict schema enforcement and typed tool calls.

Technologies:Claude Tool CallingOpenAI Function CallingZodSupabasen8n

Solve This For Your Business

Latency EngineeringCategory: Voice AI

Sub-Second Latency Optimization for Voice AI

Challenge 03

1. The Client Bottleneck

Initial conversational voice systems felt sluggish and unnatural. 2.5+ second delays created awkward pauses and caller hang-ups.

2. Engineering Approach

Audited every millisecond in the audio lifecycle: STT endpointing, network hops, prompt length, token streaming, and TTS synthesis. Switched from REST batch polling to persistent WebSocket streams with parallel execution.

3. The Production System

Engineered a bidirectional WebSocket streaming engine with Deepgram chunked transcription, low-latency LLM streaming (first chunk ready in 250ms), and Cartesia / ElevenLabs streaming audio, cutting roundtrip delay below 800ms.

4. Verified Business Return

Transformed an unconvincing, robotic phone experience into fluid, human-cadenced conversations with seamless barge-in capabilities.

Key Takeaway: In voice AI, perceived latency matters more than model size. Stream the first sentence chunk immediately while the rest generates.

Technologies:Twilio Media StreamsWebSocketsDeepgramCartesiaElevenLabsNode.js

Solve This For Your Business

Operational EfficiencyCategory: Workflow Automation

End-to-End Workflow Automation for Operations

Challenge 04

1. The Client Bottleneck

Operations teams were manually copying data across ad accounts, contact forms, Google Sheets, internal CRMs, and email dispatchers every single day.

2. Engineering Approach

Mapped the complete lifecycle of customer data. Replaced manual handoffs with event-driven webhook listeners, payload sanitization, and automated execution pipelines with built-in error handling.

3. The Production System

Custom n8n workflows that listen for events, validate payloads, update spreadsheets and databases in real time, generate notifications, and trigger follow-up sequences.

4. Verified Business Return

Cut 20+ hours of repetitive administrative grunt work weekly, eliminating data loss and accelerating customer turnaround times.

Key Takeaway: Reliability in automation requires defensive engineering: rate limiting, idempotency keys, and dead-letter queues for failed events.

Technologies:n8nWebhooksGoogle Sheets APIResendPostgreSQL

Solve This For Your Business

API ArchitectureCategory: System Integration

Bridging Disparate Systems Through Unified APIs

Challenge 05

1. The Client Bottleneck

Clients utilized multiple legacy tools (Google Workspace, proprietary databases, external payment gateways, messaging apps) that could not communicate with each other.

2. Engineering Approach

Constructed a lightweight middleware layer that normalizes incoming webhook payloads, manages authentication tokens safely, and orchestrates cross-platform synchronization.

3. The Production System

A unified API integration hub connecting WhatsApp Business, Google Sheets, Supabase, and email systems into one coherent, synchronized ecosystem.

4. Verified Business Return

Unlocked single-source-of-truth visibility without forcing clients to replace their trusted existing business tools.

Key Takeaway: Instead of forcing a total software overhaul, meet businesses where they already work by connecting their existing tools intelligently.

Technologies:Node.jsTypeScriptREST APIsSupabaseGoogle Cloud

Solve This For Your Business

Data VisualizationCategory: Internal Tools

Centralized Operational Dashboards from Spreadsheets

Challenge 06

1. The Client Bottleneck

Spreadsheets were bloated, slow, vulnerable to accidental data deletion by staff, and failed to provide high-level visibility for leadership.

2. Engineering Approach

Preserved Google Sheets as the familiar data-entry backend for field workers while building a modern, performant Next.js frontend with role-based authentication and visual charts.

3. The Production System

A bespoke dashboard featuring live data feeds, search & filter capabilities across thousands of entries, status tracking, and executive overview metrics.

4. Verified Business Return

Provided leadership with instant operational clarity while protecting database integrity from human spreadsheet mistakes.

Key Takeaway: Don't fight user habits. If a team is comfortable entering data in Google Sheets, build an interface that reads and writes to it safely.

Technologies:Next.jsTypeScriptTailwind CSSRechartsGoogle Sheets API

Solve This For Your Business

Let's Build Something Real

Have a problem worth building?

Whether you need a high-performance web platform, an autonomous WhatsApp/IG AI agent, an n8n workflow pipeline, or a low-latency voice system, tell me what you're solving.

Discuss Your Project

WhatsApp Quick Chat

rayyan1122pk@gmail.com·+92 332 8444557·LinkedIn