AI products that work on your own data, in production
Hadidiz Flow designs and builds AI products for startups and growing businesses: assistants that answer from your documents, agents that take actions in your tools, and LLM features inside the product you already have. We have shipped them for SaaS companies, consultancies, media businesses and sports coaches, and we stay until they are reliable with real users, not just impressive in a demo.
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AI Development services
RAG assistants on your own data
Chat with your documents, help center or Google Drive. Multi-tenant knowledge bases, source-grounded answers, access control, and retrieval tuned for both accuracy and cost.
AI agents and automations
Agents that use tools: send email, update records, run workflows in n8n or in your own backend, with retries, fallbacks and structured outputs so they keep working in production.
LLM features inside your product
Summaries, decision briefs, drafting, classification and extraction built into your app with OpenAI, Anthropic Claude or Google Gemini, chosen per task for quality, speed and cost.
Custom GPTs and branded AI chat
Branded chat portals on the OpenAI Assistants API that put your proprietary GPT in front of customers without handing your training data to anyone.
Fine-tuning and open models
When retrieval is not enough: fine-tuned and open-source models (Ollama, Hugging Face) behind one interface, so you can compare them on real traffic and switch by configuration.
AI inside mobile apps
Generative image and audio features in iOS and Android apps, with AI calls kept server-side and subscriptions handled by the stores.
Products we have shipped
Multi-Tenant RAG Dashboard for Rapid Client Onboarding with Custom Knowledge Bases
From concept to deployed RAG SaaS MVP in weeks—complete with Drive ingestion, assistant management, and spam-safe access control
From Spreadsheet Overload to 60-Second Decision Briefs: Signelle's AI Data Platform
From Raw Data to Actionable Brief in 60 Seconds — 300k+ Documents Processed
Stabilizing an AI Concierge for SpeakerDrive to Boost Activation and Enable One-Click Outreach
From fragile agent workflows to a stable, tool-using AI concierge with secure Gmail sending
Launching a Branded CustomGPT Chatbot Portal with Secure OpenAI Assistants API
From a barebones test page to a production-ready, branded AI chat portal—deployed overnight
From Film Session to Player's Phone in 2 Minutes: GLDS Performance Brief
Coaches go from film session to delivered brief in under 2 minutes — no app required for players
Automating Movie Q&A on X with Hybrid RAG + Fine-Tuned LLMs
Shipped a modular X bot that reliably replies to mentions with up-to-date movie answers—switching between Local RAG, Hosted RAG, and Fine-Tuned inference via config.
What AI development means for your business
AI development is building software that uses large language models (LLMs) to do work that used to need a person: answering questions from your documents, summarizing and analyzing data, drafting messages, or taking actions in other tools. The model is only one part. What makes it useful is the engineering around it: retrieving the right information, controlling cost, handling failures, and keeping each customer’s data separate.
When to bring us in
- You have documents, data or a help center that customers or staff keep asking questions about.
- Your team spends hours a week on work that is mostly reading, summarizing or drafting.
- You want to add AI features to a SaaS product or mobile app you already run.
- You built an AI prototype that works in a demo but breaks, gets slow or gets expensive with real users.
How we keep AI reliable and affordable in production
Most AI projects fail after the demo, not before it. These are the problems we design for from day one, each taken from a real client build:
- Retrieval tuned for cost as well as accuracy. For NorthernAI we cut retrieval results per query from 20 to 5, and for Celestix from 20 to 16, which lowered token cost without hurting answers.
- Fallbacks and structured outputs for agents. SpeakerDrive’s agents went from repeated retries and broken JSON to zero blocking failures once memory, prompts and tool calls were reworked.
- Long-running jobs handled properly. Holmz generates reports that take 3 to 5 minutes; the product runs them asynchronously, caches results for 30 days and never exposes the partner API key.
- The right model for each job. Fast, cheap models where speed matters, stronger models where reasoning matters, and the option to switch providers without rewriting the product.
From first call to launch
Scope call
30 minutes on the problem, the data you have and what working means for you. You get an honest read on feasibility, even if we are not the right team for it.
Written scope and estimate
What we will build, the stack and models we recommend and why, the milestones, and the cost, in a short document you can share internally.
Prototype on your real data
We build the riskiest part first, so answer quality, speed and running cost are proven on your data before the full build.
Build in milestones
The production build with a demo at every milestone. Security, access control, monitoring and fallbacks are part of the build, not an afterthought.
Launch and handover
Deployment, documentation, and support while real users arrive and the product meets real-world data.
Questions we get asked
How long does it take to build a RAG assistant or an AI MVP?
It depends on scope and on the state of your data, but our recent builds show the range. NorthernAI’s branded CustomGPT chat portal was live on their dev server within 24 hours of the offer being accepted, and a multi-tenant RAG SaaS MVP with Google Drive ingestion shipped in under a month for Celestix. After a scope call you get the timeline in writing.
Which AI models do you work with?
OpenAI (GPT models and the Assistants API), Anthropic Claude, Google Gemini, and open models through Ollama and Hugging Face. We choose per task: Signelle’s decision briefs run on the Claude API, while GLDS uses Gemini 2.5 Flash Lite to draft observations in about 3 seconds.
Do we need RAG or fine-tuning?
Most products should start with retrieval-augmented generation (RAG): it answers from your current documents, stays up to date without retraining and is cheaper to change. Fine-tuning helps when you need a specific style, format or narrow skill. For AskMovieBot we built local RAG, hosted RAG and a fine-tuned model behind one configuration switch, so the client could compare them on real traffic.
Is our data safe with an AI product you build?
We keep API keys and proprietary data server-side, isolate data per customer or user, and can deploy on your infrastructure. For NorthernAI, the chatbot connected to the client’s document-trained CustomGPT through the OpenAI Assistants API without their training data ever being handed to developers.
Can you fix an AI product that someone else started?
Yes, and a lot of our work is exactly that. SpeakerDrive came to us with multi-agent n8n workflows breaking in production, with up to 12 AI retries in a single execution. After we reworked the memory, outputs and fallbacks, there were zero blocking workflow failures.
How do we get started?
Book a free 30-minute call through our contact page and tell us what you want the AI to do. We will tell you whether it is feasible, roughly what it would take, and whether we are the right team for it.
Have an AI idea, or an AI project that is stuck?
Tell us what you want it to do. In a free 30-minute call we will tell you whether it is feasible, what it would take, and whether we are the right team.
Book a free 30-minute call