AI features that earn their place#
Plenty of AI demos look impressive in a meeting and then fall apart on real users: confident wrong answers, unpredictable costs, slow responses and no way to tell what went wrong. The model is rarely the problem. The problem is everything around it: what information it's given, what it's allowed to say, how its answers are checked, and what happens when it isn't sure.
I build AI features the way I'd build any production system: with a clear job to do, measurable quality, guardrails, logging, cost controls and a sensible fallback. The aim isn't to add AI to your product; it's to make a specific workflow faster, cheaper or better, and to prove it.
When AI integration makes sense#
- Your team answers the same questions repeatedly from documents, policies or a knowledge base, and customers wait for replies.
- People spend hours on documentation or copying information from free text, emails or files into structured records.
- Users can't find things because search only matches exact keywords.
- You already have an AI prototype that works in demos but isn't reliable enough to put in front of customers.
- Your competitors are shipping AI features and you need them built properly inside your existing Laravel or React product.
What I build#
RAG chatbots and assistants#
Assistants that answer from your documents, knowledge base or product data, with a defined persona and scope. They can capture leads, hand over to a human, and decline gracefully when a question is out of scope.
AI search#
Vector embeddings let users search by meaning rather than exact keywords, so "how do I cancel" finds the article titled "Ending your subscription". Often combined with traditional keyword search for the best of both.
Document automation#
Drafting, summarising and filling in documentation from structured inputs, so people review and approve instead of writing from scratch.
Smarter data entry#
Turning free text, emails or uploaded files into structured, validated records, with a person confirming anything the model is unsure about.
LLM workflows#
Classifying, routing and enriching data inside your existing processes: tagging support tickets, extracting fields, flagging risk or drafting replies for review.
How a production AI feature is put together#
Retrieval quality comes first. In a RAG system most answer quality comes from finding the right passages. I spend time on how documents are split, which embedding model is used, and how search is tuned, often with PostgreSQL and pgvector, which keeps your data in one database you already back up and secure.
Guardrails are designed, not hoped for. The assistant gets a defined persona and scope, stays within its sources, refuses out-of-scope requests, and declines politely when it doesn't know. Prompt injection and data leakage between users are treated as security concerns.
Quality is measured. Before launch I assemble a set of real questions with known good answers. Every change to prompts, retrieval or models is checked against it, so improvements are proven and regressions are caught.
Costs are controlled. Caching, sensible context sizes, cheaper models for simple steps and per-user limits keep per-request costs predictable. You'll know the expected running cost before we build.
Your team stays in control. Knowledge can be updated without code, and conversations are logged so you can see what users ask and where answers fall short.
Common AI integration mistakes I help avoid#
- Stuffing everything into the prompt. Sending whole documents is slow, expensive and less accurate than retrieving the few passages that matter.
- No evaluation set. Without real questions and known good answers, nobody can tell whether a change made things better or worse.
- Ignoring permissions. An assistant that can search every customer's data will eventually reveal it. Retrieval must respect the same access rules as the rest of the app.
- No fallback. When the model is unsure or the provider is down, users should get a graceful answer or a human, not an error or a guess.
- Unbounded costs. A popular feature without caching or limits can turn into a surprise invoice.
AI projects I've delivered#
- Henceforward AI knowledge platform: a RAG chatbot combining Anthropic models, Hugging Face embeddings and PostgreSQL with pgvector. It has persona and guardrail controls, captures leads autonomously, lets the team update its knowledge without code, and embeds on any site through a global CDN. Carl-Peter Lehmann of Henceforward rated me the best freelancer they have worked with on Upwork.
- SafetySpace: AI-powered safety assistance, automated documentation and smarter data entry inside a Laravel and Vue.js safety management platform, where I acted as technical lead. Its founder, Ahmed Al-Hassany, described me as "Extremely knowledgeable and hard-working".
Where AI fits in a larger product#
AI features almost always sit inside a bigger application, with users, permissions, billing and data of their own. Because I also build the surrounding SaaS platform or Laravel backend, the AI respects the same rules as the rest of your product: a user's assistant only searches content that user is allowed to see.
For AI features that need to search large or messy datasets, the data work matters as much as the model. See real estate & data platforms and web scraping & data extraction.
Working together#
We start with a free 30-minute call to pick the use case with the clearest payoff. Then I build a small prototype on your real data, so we can judge quality on evidence before committing to a full build. Production work runs at a fixed scope or on a retainer, with ongoing tuning as real usage comes in.
Have an AI use case in mind? Book a free call and we'll check whether it's worth building before you spend on it.