Service · AI SaaS

AI SaaS Development

RAG systems. AI agents. LLM applications that survive production.

Shipping an AI demo is easy. Shipping an AI SaaS that stays fast, accurate, and affordable under real traffic is a different discipline — one proven across ScamMinder (20,000+ domains analyzed, sub-6s analysis, 99.9% uptime), an AI SEO platform running 13 background pipelines, and AI sales tooling used by 50+ reps.

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55+
Projects Shipped
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Job Success
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The challenge

The gap between an LLM demo and a production AI product is enormous. Demos ignore hallucination rates, latency spikes, provider outages, token cost blowouts, and what happens when the model returns something unexpected. A production AI SaaS needs structured outputs, fallback providers, observability, and cost controls designed in from the first commit — not patched on after the first outage.

What I build

RAG Systems

Retrieval-augmented generation over private data — chunking, embeddings, hybrid search, re-ranking, and answers with citations

AI Agents

Multi-step agents with tool use, guardrails, human escalation paths, and full traceability

LLM Application Development

Classification, extraction, generation, and chat features with structured, schema-validated outputs

AI SaaS MVPs

Complete products around an AI core — auth, billing, dashboards, and the model pipeline itself

AI Pipelines & Background Jobs

Durable queued workloads for enrichment, generation, and publishing at scale

Distribution Surfaces

Chrome extensions, Telegram bots, and public APIs that put your AI product where users already are

Tech stack

TypeScriptNext.jsNode.jsPostgreSQLSupabasepgvectorOpenAI GPT-4oClaude APIGeminiOpenRouterInngestStripe

Which of my builds proves this

GTM Signal Intelligence

Monitors 20–30 target organizations, interprets signals in business context, and recommends outreach — human-approved (ongoing).

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ScamMinder — AI Scam Detection SaaS

14 verification phases with GPT-4o classification, Stripe/PayPal billing, a Telegram bot, and a Chrome extension — 20,000+ domains analyzed, sub-6s analysis, 99.9% uptime.

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AI SEO Platform

Next.js + Supabase content platform with 13 Inngest pipelines, 8 CMS adapters, 100 API routes, 79 migrations, and 74 test files.

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Chasr — AI SalesTech

GPT-4 sales assistance wired into HubSpot for 50+ reps — 10 hours saved per rep per week, follow-ups up 60%, $100K+ in recovered capacity per quarter.

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AdTech Creator Monetization

$150K-funded startup — Gemini video validation, FFmpeg overlays, and TikTok/YouTube publishing. First 10 creators and 100 videos, with campaign turnaround cut from weeks to minutes.

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Related services

AI Agent Development

Agentic systems with tool use, evals, and guardrails that hold up under real traffic.

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RAG & LLM Development

Retrieval architecture, document AI, and the cost controls that keep an LLM feature viable.

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AI Chatbots

Web, Telegram, and WhatsApp assistants that are rate-limited and spend-capped by default.

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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 data handlingPII redaction pipelinesEU data residency availableSOC 2 ready architecture

Frequently asked questions

What does it take to run an LLM feature in production?
Structured outputs validated against schemas, retries with exponential backoff, fallback providers for when a model or API degrades, cost and latency monitoring per request, and observability that lets you trace any answer back to its prompt and context. ScamMinder runs a 14-phase verification pipeline with GPT-4o classification this way — 20,000+ domains analyzed with sub-6-second analysis and 99.9% uptime.
How do you build RAG systems that retrieve the right context?
Ingestion pipeline, chunking tuned to the document type, embeddings, vector storage (pgvector for most workloads, a dedicated vector database at larger scale), retrieval with re-ranking, and generation with citations. Hybrid search — vector plus keyword — usually beats either approach alone, and an evaluation set catches retrieval regressions before your users do.
Which model providers do you work with?
OpenAI (GPT-4o), Anthropic Claude, and Google Gemini, plus OpenRouter for routing across providers. Recent builds include GPT-4o classification in ScamMinder, an OpenRouter-driven AI SEO platform, GPT-4 sales assistance in Chasr, and Gemini-powered video validation for a creator monetization platform.
How do you keep AI inference costs under control?
Route each task to the cheapest model that meets its quality bar, cache aggressively, batch background work through job pipelines instead of on-demand calls, cap token budgets per request, and monitor spend per feature — so a cost spike is an alert, not a surprise on the invoice.
Can you add AI features to an existing SaaS product?
Yes — much of this work is integration, not greenfield. The AI SEO platform publishes into 8 different CMSs through a single adapter layer, and Chasr wires GPT-4 into an existing HubSpot-based sales workflow. AI features should slot into your current architecture, not force a rewrite.
What does an AI SaaS engagement cost?
Most engagements start around $5K; smaller well-scoped work is considered case-by-case. Book a call or email me and you'll have a reply within 24 hours.

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