AI Development That Works From Your Own Data
We build AI features that work from your own data: assistants that answer from your documents, document reading, and automated decisions with a person in the loop. Money Ball, our sports-stats assistant, checks a live database before it answers.
Reviewed by Azmat Rana, CEO & Founder · Updated
Where We Use AI
AI is worth building when the input is messy, such as documents, free text or open questions, and a wrong answer can be caught.
Core capability
Answers from your records
Chat and search over your own databases and documents, using retrieval so each answer comes from a real record.
Document reading
OCR and handwriting recognition that pull fields out of scans and forms. Natif.ai processes incoming documents this way.
Routing and triage
Models that read free-text messages and send each one to the right team. Contakti routes support tickets across email, chat, SMS and voice.
Writing assistance
Drafting tools that work inside an existing workflow, such as email replies a person reviews before they go out.
AI at the core of a product
Generative features a product is built around, such as Aiode’s virtual musicians that help producers compose.
Project story · Money Ball
Stopping an AI chatbot from making up sports stats
Money Ball lets NFL fans and analysts ask for player and team numbers in plain English. The hard part of a product like this is trust: the answer has to be right, and football stats change every week.
- Build time
- 3 months
- Approach
- RAG over live stats
- Stack
- Node.js, Django, PostgreSQL, Redis, Docker
- Live at
- app.moneyball-ai.com
The problem
General language models answer confidently even when they are wrong. They are wrong most often on specialized figures that change quickly, such as this season’s player and team stats. A chatbot that invents numbers is worse than no chatbot at all.
What we changed
- 01Built a retrieval-augmented generation (RAG) setup that makes the model query the live sports database before it answers, so replies rest on current figures.
- 02Added a real-time team performance engine that keeps the underlying numbers up to date.
- 03Split data ingestion into Dockerized microservices so new stats are taken in quickly.
- 04Wrapped it in a chat interface where fans ask questions the way they would ask a friend.
Where it stands
Fans and analysts get football figures pulled from live data, in a chat. The same approach carries over to most business AI work: connect the model to your own data and make it look things up instead of guessing.
See Money Ball in our portfolioFrom Strategy To Scale
We start with one task, prove it on real examples and widen it only once the answers hold up.
Discover
Choose The Task
We pick a task where the input is messy and mistakes can be caught, and agree how success is measured.
Build
Ground It In Your Data
We connect the model to your documents or databases and test it on real examples before anyone relies on it.
Scale
Monitor The Answers
In production we track accuracy and failures and keep a person in the loop where mistakes are costly.
Technology Behind Our AI Solutions
The models, frameworks and data tools we build AI features with.
Hugging Face
TensorFlow
PyTorch
Python
Node.js
React
Next.js
PostgreSQL
MongoDB
Google Cloud
GraphQLWhere AI Earns Its Place
AI is worth building when it saves real time on work you already do and its output can be checked. These are the places we see it pay off, and two where it does not.
Reading documents at volume
Invoices, forms, handwritten notes and contracts. Natif extracts fields from low-quality scans and handwriting so staff stop keying them in.
Answering from your own data
Chat and search over your products, policies or records, grounded with retrieval so answers come from your sources. Money Ball works this way.
Drafting work people already do
Email replies, meeting notes and first drafts that a person approves before they go out.
Scoring against clear criteria
Assessing candidates, leads or content against agreed rules, with a person making the final call. Evalufy uses AI to assess video interview answers.
Not where mistakes go unchecked
If a wrong answer can cost money or harm someone and nobody reviews it, keep a person in the loop or wait before automating it.
Not before the data exists
AI needs records, documents or history to work from. If they are scattered or missing, organizing them is the first project.
AI Solutions In Action
AI products from our portfolio.
Related services
Projects like this one usually involve these as well.
Further reading: How to stop an AI chatbot from making things up
Prefer to add engineers to your own team?
Hire full-time developers in the stacks we use for this work.
Hire Python and Django developersAI Solutions FAQs
What teams ask before putting AI in front of customers.
Document processing (Natif), chat over live data (Money Ball), assessment tools (Evalufy), plus AI agents and recommendation features inside existing products.
By grounding it in your data. We use retrieval so the model looks answers up in your databases or documents before replying, as in Money Ball. Where a mistake would be costly, we add checks and human review.
OpenAI and Anthropic models, open models from Hugging Face, and custom models in PyTorch or TensorFlow. We choose per task, weighing accuracy, cost and where your data is allowed to go.
We agree up front where your data may go, then design around it. That covers which model providers can see it, whether it stays in your own cloud account, and which parts of the system can access what.
Yes. Most of our AI work adds a capability to a product that already exists, such as document extraction, search or drafting, connected through your current APIs and databases.
Yes. We build agents that read context, call your tools and APIs and carry out defined workflows. Each one has limits on what it can change, and a person approves the steps that matter.
You do. Everything we build for you, including the source code and the intellectual property in it, belongs to you.
Have A Task AI Could Take On?
Tell us what the input looks like and what a good answer is. We reply within one business day to set up a discovery call.