Image-based Analysis
Photo-based diagnosis and classification flows, from upload and preprocessing to a clear, actionable result for the user.
Service · AI in Real Products
AI Integration
I add AI to real products, not demos. From diagnosing plant problems from a photo to natural-language venue search and extracting tariffs from uploaded bills, I build AI features inside a tested architecture, with guardrails, fallbacks and cost control, so they stay useful and predictable in production.
- Image-based Diagnosis
- Natural-language Search
- Document Data Extraction
- Guardrails & Cost Control
01Offering
My AI Integration Services
Natural-language Search
Let users search the way they speak, turning a sentence into structured filters over your own data and search index.
Assistants & Voice
In-app assistants, including voice, that answer from your product's data and guide users to the right action.
Document & Bill Extraction
Structured fields extracted from invoices, bills and documents, validated before the data enters your system.
AI-powered Automation
Classification, summarisation and routing tasks that run as background jobs and trigger alerts or next steps.
Guardrails & Cost Control
Input validation, output checks, caching, usage and plan limits, and fallbacks, so AI stays safe, predictable and affordable.
02Related Projects
Projects in This Area
TÜBİTAK Programme
Live site of Tarlamapp (opens in a new tab)
SaaS · AI AgriTech
Tarlamapp
Snap a photo of a sick plant and get a clear diagnosis with next steps.
Who It's For: Farm Advisors & Cooperatives
SaaS · City Guide
İzmir Mekan Rehberi
İzmir's guide to places to eat, drink and celebrate, with online table booking.
Who It's For: Locals, Visitors & Venues
Irish Government-backed
Live site of OneBill (Ireland) (opens in a new tab)
SaaS · FinTech / Energy
OneBill (Ireland)
Upload one energy bill and get moved to the cheapest plan in Ireland, automatically.
Who It's For: Irish Households
03Process
How I Work
- 01 step Use-case Discovery We pick the problem where AI adds clear value and define what a good result looks like for your users.
- 02 step Prototype on Real Data I test models and prompts on samples of your actual data to measure quality before committing to a design.
- 03 step Production Architecture AI calls sit behind clean interfaces, run through queues where needed and are wrapped in validation and fallbacks.
- 04 step Build & Evaluate I integrate the feature into your product and evaluate outputs against test cases, including edge and failure cases.
- 05 step Launch & Monitor After launch I track quality, latency and cost, and tune prompts, caching and limits based on real usage.
04Stack
Technologies I Use
- AI Diagnosis
- AI Search
- Voice Assistant
- Laravel
- PostgreSQL
- Meilisearch
- RabbitMQ
05Why
Why Add AI to Your Product?
Faster Answers for Users
Users get a diagnosis, a search result or extracted data in seconds instead of waiting for manual work.
Less Manual Processing
Repetitive reading, sorting and data entry move to automated flows that your team only needs to review.
A More Natural Experience
People describe what they need in their own words instead of learning filters and forms.
Differentiation
Well-built AI features make your product more useful and help it stand out in a crowded market.
Which AI models or providers do you use?
It depends on the task, the quality you need and your budget. I keep AI calls behind a clean interface, so the model or provider can be changed later without rewriting the product.
How do you keep AI costs under control?
I use caching, sensible input limits, plan-based usage limits and background processing where it fits. Usage and cost are monitored after launch, so surprises are caught early.
What if the AI gives a wrong answer?
No model is perfect, so I design for it. Outputs are validated, and where it matters users can review or correct results. In critical flows, AI suggests and a human decides.
Can you add AI to our existing product?
Yes. Most AI work is integration work: I review your architecture and data, then add the feature as a well-defined module instead of rebuilding the product.
What about our data privacy?
I send models only the data a feature actually needs and design the flow around your privacy and compliance requirements. We agree on what may leave your systems before building starts.
Have you shipped AI features in production?
Yes. Tarlamapp diagnoses plant problems from leaf photos and includes a voice assistant, İzmir Mekan Rehberi offers AI-assisted natural-language search, and OneBill extracts tariffs from uploaded bills.
How much does an AI integration cost?
It depends on the use case, the data preparation involved and the expected usage volume. Share your idea and I'll prepare a quote based on a clear scope.
07Guide
AI That Works Inside a Real Product
Adding a chat box to a website is easy. Building an AI feature that users rely on every day is a different job. It needs clean data going in, validated results coming out, sensible limits on cost and usage, and a plan for the moments when the model is wrong. I focus on that second kind of work: AI as a dependable part of your product, not a demo bolted on the side.
Image-based Diagnosis and Assistants: Tarlamapp
As the founder of Tarlamapp, an AgriTech SaaS developed at Teknopark İzmir (İYTE) as part of a TÜBİTAK programme, I built AI into the core of the product. A farmer or agricultural advisor takes a photo of a plant in the field; the system analyses the leaf, returns an actionable diagnosis and can send a WhatsApp alert. The platform also includes a voice assistant, plan-based limits for each tenant and GDD-based growth tracking.
What made this production-ready was not only the model, but the architecture around it: a framework-independent Clean Architecture core with 655 tests and machine-enforced layer boundaries. AI services can be swapped, tested and limited without touching the rest of the system.
Natural-language Search and Document Extraction
For İzmir Mekan Rehberi, I added AI-assisted natural-language search. Instead of clicking through filters, a user can type something like “sea-view seafood in Alsancak” and get relevant venues. The AI interprets the request, while the search engine and structured data do the retrieval, which keeps results grounded in real listings rather than invented ones.
OneBill, an Irish Government-backed energy platform, starts with a simple action: the customer uploads a utility bill. The system extracts the tariff, compares it with every supplier in the market and moves the customer to the cheapest plan. In flows like this, every extracted value needs to be validated before it drives a decision.
How I Build AI Features
Every AI integration I deliver follows the same principles:
- Right tool for the job: AI only where it adds clear value, conventional code everywhere else
- Guardrails: input validation, output checks and human review for critical decisions
- Cost control: caching, usage limits per plan or user, and background processing
- Replaceable providers: models behind clean interfaces, so you are not locked in
- Measured quality: test cases, logging and monitoring after launch
Just as important is knowing when not to use AI. If a rule, a well-designed search index or a simple form solves the problem more reliably and cheaply, I will say so. The goal is a better product, not more AI.
I work from Izmir, Turkey, with companies across Türkiye and abroad. If you have a product and an idea for where AI could help, whether diagnosis, search, assistants, document processing or automation, I can help you validate it on real data and ship it properly.
Available for Projects
Have an Idea for AI in Your Product?
Tell me what you want to automate or improve. I'll help you check whether AI is the right fit and how to build it properly.