Service · AI & Automation

AI Automation Development

AI agents. RAG systems. n8n workflows. LangChain. Real automation that works.

Most 'AI integrations' are ChatGPT wrappers that break in production. Real AI automation means proper RAG pipelines, reliable agent orchestration with fallback handling, observability, and systems that keep working when the model misbehaves.

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Projects Shipped
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Job Success
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The challenge

The gap between an AI demo and a production AI system is enormous. Demos use happy-path data and ignore latency, hallucination rates, rate limits, cost spikes, and what happens when the LLM returns unexpected output. I build for the real world.

What I build

AI Agents

Autonomous agents using LangChain that research, write, book, notify, and escalate

RAG Systems

Retrieval-augmented generation on private documents using pgvector or Pinecone

n8n Workflows

Visual automation connecting 500+ apps — production-hardened, not just demos

LangChain Pipelines

Production LLM chains with memory, tools, observability via Langfuse/LangSmith

OpenAI / Claude Integration

Assistants API, function calling, streaming, fine-tuning, structured outputs

AI-Powered SaaS Features

Add AI to existing products: smart search, content generation, data extraction

Tech stack

LangChainOpenAI APIAnthropic Clauden8nPineconepgvectorTypeScriptNestJSPostgreSQLRedisLangfuseDocker

Recent work

Chasr — AI SalesTech

GPT-4 sales assistance integrated with HubSpot workflows for a 50+ rep sales organisation

10 hours saved per rep per week. Follow-ups up 60%. $100K+ in recovered capacity per quarter.

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Which of my builds proves this

Claude Code Client Delivery

Full-stack delivery plus n8n agent workflows, with Claude Code as the primary development tool (ongoing).

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Chasr — AI SalesTech

50+ reps, 10 hours saved per rep per week, +60% follow-ups, $100K+ recovered capacity per quarter.

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AdTech Creator Monetization

Video validation, FFmpeg overlays, TikTok/YouTube publishing — campaign turnaround from weeks to minutes.

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ScamMinder — AI Scam Detection

GPT-4o classification across 14 verification phases — 20,000+ domains, sub-6s analysis.

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Related services

n8n Automation

n8n builds and Zapier or Make migrations, with error handling and idempotency designed in.

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AI Agent Development

When a workflow needs judgment rather than a fixed branch, an agent is the next step up.

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Integrations

The CRM, payment, and webhook connections most automations are actually made of.

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How we work

Most MVPs ship in 3–6 weeks; complex platforms are scoped individually once the requirements are clear.

01

Intro Call

A 30-minute conversation about your project, goals, and timeline. No commitment either way.

02

Scoped Proposal

A written scope, architecture outline, and quote. I respond to every message within 24 hours.

03

Weekly Demos

You see working software every week, with written progress updates in between. No black boxes.

04

Ship & Handover

Deployment, documentation, and a clean handover — you own everything I build.

Compliance & standards

GDPR-aware prompt engineeringEU data residency availableOn-premise LLM deploymentPII scrubbing pipelines

Frequently asked questions

What makes your AI systems 'production-ready'?
Fallback providers (if OpenAI is down, switch to Anthropic), retry logic with exponential backoff, prompt versioning, output validation (structured outputs + Zod schemas), cost monitoring, rate limit handling, circuit breakers, and full observability via Langfuse. Every AI feature degrades gracefully.
How do you build RAG systems?
Document ingestion pipeline → chunking strategy (semantic or fixed, depending on document type) → embedding generation → vector storage (pgvector for small/medium, Pinecone for large scale) → retrieval with re-ranking → generation with citations. I also implement hybrid search (vector + BM25) for better recall.
Can you guarantee GDPR compliance for AI systems?
Yes. No PII is sent to external LLMs without explicit user consent, prompts are designed with data minimisation, EU data residency is available (AWS Frankfurt/Dublin), all AI interactions are logged for audit, and I implement right-to-erasure for AI-processed data.
What's the difference between n8n and custom LangChain agents?
n8n is ideal for connecting existing apps (CRM, email, Slack, databases) without custom code — faster to build, easier to maintain for non-developers. LangChain agents are for complex reasoning tasks that require multi-step decision making, tool use, and custom logic that can't be expressed in a visual workflow.
How do you measure ROI for AI automation projects?
Before starting, we baseline the manual process: how many hours per week, what's the error rate, what's the cost. After deployment, we measure hours saved, error reduction, and throughput increase. On Chasr, AI sales automation for 50+ reps measured out at 10 hours saved per rep per week, a 60% increase in follow-ups, and $100K+ in recovered capacity per quarter.

Ready to start?

Book a free 30-minute call. No sales pitch — just a direct conversation about your project.

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Or email: contact@waseemahmad.dev