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AI Integration & Automation
built for production, not a demo.

Most AI features fail quietly. A RAG pipeline that returns stale embeddings. An LLM call with no timeout that locks a request thread at peak load. A prompt that works in evaluation and hallucinates in production.

I build AI systems in Node.js and Next.js with token cost controls, structured output validation, and failure handling that your on-call engineer can actually debug at 3am.

Assad Ullah engineers production AI features using OpenAI, Anthropic, LangChain, and vector databases like Pinecone and pgvector. RAG pipelines, LLM-powered workflows, streaming interfaces, and intelligent automation built to handle real user load — with cost monitoring and structured error handling from day one.

Deploy IntelligenceExplore AI Builds

Replies within 24 hours. No retainer required.

Capabilities

Applied
Intelligence.

I don't just wire an LLM to a text box. I design the retrieval strategy, chunk documents for semantic accuracy, set token budgets per request, and build the fallback paths your system needs when a model API returns a 529.

01

LLM API Integration

Production integration with OpenAI, Anthropic, and open-source model APIs. Prompt engineering that accounts for context window limits, token costs, and output consistency. Model fallback strategies for when primary APIs are unavailable or latency spikes.

OpenAIAnthropicPrompt EngineeringToken Optimization
02

RAG Pipeline Architecture

Retrieval-Augmented Generation pipelines that connect language models to proprietary data without fine-tuning. Document ingestion, chunking strategy, embedding generation, vector store indexing, and retrieval tuning. The quality of a RAG system lives in the retrieval layer. That is where the engineering work is.

RAGVector SearchEmbeddingsPinecone / pgvector
03

Streaming Chat Interfaces

Real-time streaming UI for LLM responses: token-by-token rendering, conversation history management, and context-aware follow-up handling. Built for production latency targets with graceful degradation when the model is slow.

Streaming UIWebSocketsServer-Sent EventsContext Management
04

Intelligent Automation Workflows

Multi-step automation pipelines that use LLMs for classification, extraction, summarization, or decision-making within larger business workflows. Human-in-the-loop escalation paths for cases where model confidence is low. Automation without a fallback is just a different kind of manual process.

Workflow AutomationClassificationData ExtractionHuman-in-the-Loop
05

AI Agent Systems

Tool-using agent architectures with function calling, external API access, memory layers, and structured output parsing. Designed with execution boundaries that prevent runaway agent loops. An agent without constraints is not a production feature.

Function CallingAgent MemoryStructured OutputTool Use
06

Evaluation and Observability

LLM output evaluation frameworks, prompt regression testing, latency monitoring, and cost tracking dashboards. If you cannot measure whether the model is giving correct answers, you cannot improve it. And you will not know when a prompt change broke something.

LLM EvalsCost MonitoringLatency TrackingPrompt Versioning

Technical Ecosystem

Built with modern
scalable technologies

I use proven technologies like React, Next.js, Node.js, and AWS to build scalable SaaS platforms, high-performance APIs, and production-ready systems.

Core Stack

ReactNext.jsNode.jsLaravelAWSStripe

Vector and Data02

PineconepgvectorEmbeddingsDocument ChunkingRAG Pipelines

Backend and Streaming03

Node.jsPythonWebSocketsServer-Sent EventsRedis

Evaluation and Ops04

Prompt VersioningLLM EvalsCost MonitoringLatency DashboardsGuardrails
AI

Applied Intelligence Integration

OpenAI APIAnthropic APIOllamaHugging FaceLangChain

Integrating AI into real products using LLM APIs, automation workflows, and scalable data pipelines — built for production, not demos.

Workflow

How I Build It.
Context before code.

Phase 01

Use Case Definition and Data Audit

The most expensive AI mistakes happen before a single API call is made. This phase defines exactly what the model needs to do, what data it needs access to, what a correct output looks like, and what a bad output costs. Vague AI briefs produce vague, unmeasurable systems.

Phase 02

Model Selection and Pipeline Architecture

RAG versus fine-tuning versus prompt engineering. The right answer depends on your data, latency requirements, and cost tolerance. Not on what is trending. Vector store selection, chunking strategy, retrieval design, and cost-per-query are all modelled at this stage before anything gets built.

Phase 03

Integration Build with Real Data

LLM API integration, embedding pipelines, vector database ingestion, and streaming UI are built iteratively against real data. Not synthetic test cases. Prompt logic is version-controlled. Regression cases are defined as the system is built, not added as an afterthought before launch.

Phase 04

Evaluation and Guardrails

Output quality is evaluated against the benchmarks defined in Phase 01. Not subjectively. Not by feel. Guardrails, fallback handling, rate limiting, and cost monitoring are confirmed as working before any deployment decision is made. This phase is a gate, not a checkbox.

Phase 05

Deployment and Ongoing Observability

The feature goes to production with latency monitoring, cost dashboards, and prompt versioning in place. AI systems degrade quietly. A prompt change, a model update, or a shift in your data can erode output quality without throwing an error. Observability is what tells you before your users do.

View Full Methodology

AI & Automation Case Studies

01

Kodezi

AI-powered web IDE SaaS

View Case Study

Kodezi is not a thin wrapper around an LLM API. It's a full in-browser IDE — Monaco Editor with multi-tab state, diff views, codebase-aware context — with OpenAI integration that understands your actual project, not just the snippet you paste in. I built it from v1 through v4: the initial MVP, KodeziChat with real-time Socket.io streaming, a credits-gated subscription system enforced at the API level, a VS Code extension with native-feeling Webview UI, and separately, an automated system status tracker that replaced manual monitoring entirely. The 200K user milestone and Product Hunt Launch of the Month were outcomes of getting the product architecture right across four iterative versions.

200K active users reachedProduct Hunt Launch of the Month — February 2023Monaco Editor web IDE with multi-tab and diff viewOpenAI API integration with full codebase contextKodeziChat: Socket.io real-time AI streamingStripe subscriptions with credits-gated feature accessVS Code extension UI via Webview APIAutomated 90-day system status tracker
View Full Portfolio →
SaaS Development

I design websites, but when the job is too technical I contact Assadullahch. This is the second time he has helped me with a tough and highly technical job using his vast knowledge, skill and expertise. He is always professional and patient to make sure you're satisfied.

Raymond O.

Founder & CEO

Who This Is For

Right fit for
serious builders.

Founders Adding AI Features to an Existing Product

You have a working SaaS and want to integrate AI: chat interfaces, document processing, intelligent automation. Without rebuilding your stack. The key constraint is that it needs to work reliably under real usage with predictable costs. Not just in a controlled demo.

  • Existing product with a defined AI feature scope
  • Need LLM API integration, not model training or fine-tuning
  • Want production-ready output with observability from day one

Teams Replacing Manual Workflows with Intelligent Automation

Your team spends hours on tasks that are high-volume and structurally repetitive: data extraction, document classification, content processing, decision routing. LLMs handle these well when the pipeline is built properly. The failure handling and accuracy evaluation are where most implementations fall short.

  • High-volume manual data or content processing
  • Workflow steps that could be automated with language models
  • Accuracy and failure handling matter, not just speed

Teams Whose First AI Integration Underperformed

The context window filled up and responses degraded. Costs ballooned unpredictably. Output quality had no measurement framework. These are common failure modes in AI integrations built without production constraints in mind. I have fixed enough of them to know how to avoid them in the first build.

  • Previous AI integration with quality, cost, or reliability issues
  • Need RAG, streaming UI, or vector search done properly
  • Require guardrails, cost monitoring, and evaluation from day one

Related Services

SaaS Development

Multi-Tenant Platforms Built for Real Scale

Most SaaS platforms fail at the architecture level before they fail at the product level. I build multi-tenant backends with proper tenant isolation, Stripe subscription billing that does not leak revenue, and RBAC systems that do not turn into a maintenance nightmare six months in. End-to-end, from schema design to production deployment.

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Fintech & Payment Systems

Stripe Infrastructure That Handles Real Money

Payment bugs are not like other bugs. A race condition in your webhook handler or a missing idempotency key does not just throw an error. It moves real money in the wrong direction, or does not move it at all. I build Stripe Connect onboarding, marketplace payout systems, and subscription billing infrastructure where the failure modes are handled explicitly, not optimistically.

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Web & Mobile Development

Next.js, React Native and Performance Engineering

A slow web app is not just a bad user experience. It is lost revenue and lost search ranking. I build Next.js web applications and React Native mobile apps optimized for Core Web Vitals, SEO, and real-world load. Rendering strategy, bundle architecture, and deployment pipeline are treated as first-class engineering concerns, not configuration to figure out at the end.

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FAQ

Common Questions.
Straight answers.

The questions engineering leads ask before greenlighting an AI feature. Answered here so we spend the first call on your actual problem, not the basics.

Yes. But the first question I ask is whether the problem actually needs AI or whether a well-structured query and a good UI would solve it faster and cheaper. If AI is the right tool, I integrate it through a clean middleware layer so it does not become load-bearing spaghetti inside your core application logic.

Streaming chat interfaces with session memory, RAG pipelines over private document stores, LLM-powered data extraction from unstructured inputs, semantic search over large datasets, and background automation agents that trigger on webhook events. All of it running in production, not in a demo environment with clean data and no edge cases.

LLM APIs are not databases. They time out, return malformed JSON, and occasionally hallucinate structured output that breaks a downstream parser. I build with fallback logic, output validation against a defined schema, and retry budgets with exponential backoff. The user experience should not degrade visibly when the model has a bad moment.

By treating the model as a versioned dependency. I pin model versions in production, version prompt templates in source control, and write integration tests against expected output shapes rather than exact text. When OpenAI ships a behavior change in a new model version, your feature does not break silently because a test catches it first.

Yes. I build RAG pipelines with proper chunking strategies, embedding storage in a vector database, retrieval tuned for your query patterns, and a reranking step where relevance matters more than raw similarity score. The part most teams get wrong is the chunking and retrieval layer. Getting that right is the difference between a system that surfaces useful answers and one that confidently returns irrelevant context.

Yes. Trigger-based pipelines, scheduled jobs, webhook-driven processing, and LLM steps where classification or extraction is needed. I map the full workflow before writing code to find the failure modes first. An automation that silently skips records or fails without alerting anyone is worse than the manual process it replaced.

By being deliberate about what actually needs an LLM call and what does not. Prompt length, model selection, caching repeated queries, and batching where latency allows all have a direct impact on your monthly API bill. I track token usage per feature from the start so cost does not become a surprise conversation after your user base grows.

ExplorePortfolio About My Process Contact

Start an AI Project

Ready to add
AI to your product?

Tell me what you need the AI to do, what data it has to reason over, and what a wrong answer costs you. I will recommend the right architecture, model the token cost per query, and scope what it takes to ship something that holds up in production — not just in a demo.

What to Expect

  • Response within 24 hours
  • Free architecture scoping call
  • Clear proposal with timeline & cost
  • No obligation to proceed
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