AI Integration Developer for SaaS Products
I add AI features to SaaS products that already have users, and build AI-first products from scratch. That covers LLM-powered features, automation workflows and recommendations, using OpenAI, Anthropic or open-source models, with the backend orchestration and the interface both handled by one senior engineer.
What you can hire me for
LLM-powered product features
Chat assistants, summarisation, search and content generation built into your existing product.
AI workflow automation
Replace repetitive manual steps with reliable, monitored automation workflows.
Recommendation engines
Personalised suggestions based on how your users actually behave.
AI-powered dashboards
Natural-language queries and automated insights on top of your customer data.
Model selection and cost control
Choose between OpenAI, Anthropic and open-source models based on quality, latency and cost, and keep usage costs predictable.
Production hardening
Rate limiting, fallbacks, logging and evaluation so AI features behave reliably at scale.
Results from past projects
freelancer status on Upwork
performance gain on a real estate transaction platform used by JLL and Cushman & Wakefield
on a CRM serving 2M+ users
report generation on a financial compliance platform for KPMG
operational costs delivered for NanoByte Technologies
Technologies I use
- OpenAI API
- Anthropic API
- Open-source LLMs
- Next.js
- React
- TypeScript
- Node.js
- PostgreSQL
- AWS
Who this is for
- SaaS founders who want to add AI to a product that already has users
- Startups building an AI-first product from scratch
- Product teams that need to move an AI prototype into production
- Teams unsure which model or approach fits their use case and budget
How I work
- Step 1
Use-case scoping
Identify where AI creates real value for your users, and where it does not.
- Step 2
Prototype
A small working version to test quality and cost before committing to a full build.
- Step 3
Production build
Backend orchestration, interface, monitoring and safeguards.
- Step 4
Measure and iterate
Track quality and cost, then improve based on real usage.
What does AI integration involve?
AI integration means adding capabilities such as text generation, summarisation, search, classification or automation to a product by connecting it to a language model. The model is only part of it. Most of the work is deciding what to send it, checking what comes back, and making the whole thing fast, safe and affordable.
Where AI adds value in a SaaS product
These are the use cases that most often pay for themselves.
- Summarising long records, calls or documents so users read less.
- Natural-language search and question answering over your own data.
- Drafting emails, reports or descriptions users then edit.
- Classifying and routing support tickets or leads.
- Automating repetitive back-office steps.
OpenAI, Anthropic or open-source models?
Hosted models from providers such as OpenAI and Anthropic are the quickest route to good quality. Open-source models suit cases where data must stay on your own infrastructure or where volume makes per-call pricing too expensive.
The right choice depends on quality needs, latency, cost per request and your data-privacy constraints. Testing two or three models on your real examples before committing is usually worth a day of work.
Making AI features reliable in production
Demos are easy. Production behaviour is what matters.
- Evaluate outputs against real examples before shipping and after every change.
- Add rate limits, timeouts and fallbacks for when a provider is slow or down.
- Log prompts and responses so problems can be investigated.
- Control cost with caching, smaller models where they are enough, and per-user limits.
- Decide what data is sent to third parties and document it.
Common mistakes when adding AI to a product
- Adding AI because competitors have, without a clear user problem.
- Skipping evaluation, so quality quietly degrades.
- Ignoring cost until the first large invoice arrives.
- Sending sensitive data to a provider without a decision on privacy.
- Building a complex agent when a simple prompt would work.
What clients say
Feedback from 5-star projects on my Upwork profile.
“Outstanding experience working with Zagham on our SaaS platform. He understood the requirements immediately, executed the build flawlessly, and handled edge cases with great problem-solving skills. Fast turnaround, great communication, and top-tier code quality. 10/10 developer.”
“Really happy with the updates. New features were added to our e-commerce site exactly as requested, with great communication throughout the project.”
“Love working with Zagham as always!”
“Very responsive and quick to get things done.”
“Very responsive. Quick and efficient with his work.”
Frequently asked questions
Can you add AI to an existing SaaS product?
Yes. I integrate LLM-powered features, recommendation engines and automation workflows into existing platforms, handling both the backend and the frontend.
Which AI models do you work with?
OpenAI, Anthropic and open-source models. I help you choose based on quality, latency, cost and data privacy needs.
How do you keep AI costs under control?
By choosing the right model for each task, caching where possible, limiting usage per user and tracking cost per feature from the start.
How long does an AI integration take?
A focused feature can go from prototype to production in a few weeks. Larger AI-first products follow a typical MVP timeline of 8 to 16 weeks.
Do you handle data privacy concerns?
Yes. We decide up front what data is sent to which model provider, and I can sign an NDA before we discuss your product.
How do we start?
Book a free 30-minute call or email me a short brief describing the feature you have in mind.
How much does it cost to add AI to a SaaS product?
A focused feature can be a small project. A full AI-first product follows a typical MVP range of $15,000 to $60,000. Ongoing model usage costs are separate and depend on volume, which is why cost tracking is part of the build.
Do I need to train my own model?
Almost never. Most products get good results by combining a hosted model with your own data through prompting and retrieval, which is far cheaper than training.
Can AI use my company's data safely?
Yes, with decisions made up front about what is sent to which provider, what is stored, and what stays on your own infrastructure. I can work under an NDA.
How do you stop the AI from giving wrong answers?
You cannot eliminate errors completely, but you can reduce them by grounding answers in your own data, testing against real examples, showing sources, and letting users check or edit results.
Can you add AI to a product built by someone else?
Yes. I review the existing code first, then add the feature in a way that fits how the product is already built.
Tell me about your project
Book a free call or send a short brief. I reply within one business day.