You will get a private AI system on YOUR infrastructure , data NEVER leaves.
Rising Talent
Project details
For law firms, healthcare operations, financial services and any business that legally or contractually can't send data to ChatGPT: I build AI systems that run entirely on YOUR infrastructure. Local models on dedicated NVIDIA hardware — no public clouds, no third-party APIs, nothing ever leaves your perimeter. And with AI regulation tightening (EU AI Act, HIPAA, GDPR, multiplying US state privacy laws), getting AI capability without data exposure is becoming a competitive edge, not just a compliance box.
This isn't theoretical. I run a document-intelligence platform this way for a legal-services operation: 100,000+ operations and records processed 100K + case files analyzed on dedicated NVIDIA hardware with local LLMs — 100% accuracy in the client's high-confidence validations, fully air-gapped.
What you get: the same AI capabilities everyone else rents from the cloud (document understanding, knowledge assistants, workflow automation), with every output traceable, every decision auditable, and your compliance team able to verify exactly where data lives.
Comparable on-premise AI engagements from consultancies start at five to six figures.
This isn't theoretical. I run a document-intelligence platform this way for a legal-services operation: 100,000+ operations and records processed 100K + case files analyzed on dedicated NVIDIA hardware with local LLMs — 100% accuracy in the client's high-confidence validations, fully air-gapped.
What you get: the same AI capabilities everyone else rents from the cloud (document understanding, knowledge assistants, workflow automation), with every output traceable, every decision auditable, and your compliance team able to verify exactly where data lives.
Comparable on-premise AI engagements from consultancies start at five to six figures.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, Anomaly Detection, Conversational AI, Image Processing, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Text RecognitionAI Development Language
PythonAI Tools
Hugging Face, NVIDIA AI Platform, PyTorch, StreamlitAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$250
|
Standard
$1,000
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 5 days | 15 days | 30 days |
Number of Revisions | 1 | 2 | 1 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | |
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | |||
Model Documentation | |||
Model Monitoring | - | ||
Model Testing & Optimization | |||
Model Tuning | - | ||
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | ||
Source Code | - |
Frequently asked questions
About Daniel
AI Automation Engineer | Production AI Agents & Document Intelligence
Zaragoza, Spain - 1:23 am local time
I'm the co-founder of ZAIMAX AI, a two-founder engineering studio specialized in document intelligence and AI automation. We also started and run the AI & automation function of a European telecom group. A few highlights of what's in production today:
✔ Document-intelligence platform for a legal debt-recovery operation: 20,000+ case files and hundreds of thousands of documents (court rulings, payment histories, scanned PDFs) analyzed across multiple portfolios — 100% accuracy in the client's high-confidence validations. Runs entirely on dedicated NVIDIA hardware with local LLMs: nothing ever leaves the client's perimeter.
✔ Autonomous fault-diagnosis agent: correlates evidence across 5 enterprise systems, diagnoses 10 fault categories and drafts support responses in ~3 minutes at €0.49/ticket — with an independent AI verifier approving every diagnosis before publication (83% approval rate).
✔ AI invoice-processing agent: 100+ supplier invoices/month in any format (PDF, Excel, ZIP, scans via Claude Vision), 126-provider matching, 24/7, zero duplicates, full audit trail.
✔ Accounting-close and reporting automation: compliant multi-currency ledger entries in under 30 seconds, and ~2.4M daily records consolidated into executive dashboards across 19 countries, delivered every morning.
WHY OUR SYSTEMS SURVIVE PRODUCTION
Deterministic-first architecture: LLMs read and reason; business rules, metrics and decisions stay in auditable code. Expert-calibration loop: your domain expert validates, every correction becomes a testable rule — on our flagship, agreement with the client's expert went from ~50% to >80% in three iterations. Verification gates: calibrated confidence on every output, anything unverified routes to a human by design.
PRIVACY AS A PRINCIPLE
We deploy to YOUR constraint: cloud APIs (Claude/OpenAI) on standard stacks — or fully air-gapped local LLMs on dedicated hardware for legal, healthcare and financial data that can't touch a public cloud. Every output traceable and auditable.
WE ADAPT TO YOUR PROBLEM
Document processing engines, AI agents, workflow automation, internal tools, data pipelines — the sector changes, the pattern doesn't: wherever documents, data and expert judgment meet, we ship software that works. You get a two-person engineering team's depth with a single point of contact, at highly competitive rates: what drives us is the value we add to your operation.
HOW WE ENGAGE
Fixed-scope pilot (2–4 weeks) so you can evaluate us on a real problem with minimal risk → then fixed-scope builds + a monthly care & evolution retainer. Whoever builds it, maintains it.
Based in Spain, fluent English, US-hours friendly. Tell me about your process — I'll tell you honestly whether there's a fit and what we'd build.
Steps for completing your project
After purchasing the project, send requirements so Daniel can start the project.
Delivery time starts when Daniel receives requirements from you.
Daniel works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff
your use case, data sensitivity and constraints reviewed
Demo
a local LLM working on your sample documents