30+
professionals
100+
success projects
1 week
trial offer
Clutch rating

Most agencies treat AI as a science experiment. We treat it as engineering. With X+ successful projects under our belt and a team of 30+ dedicated specialists, we’ve moved past the "what if" phase. We know what works in production and what breaks under pressure. Whether you are a startup needing a quick prototype or an enterprise modernizing a legacy system, we bring a practical, engineering-first mindset to AI.

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Our AI Development Services at TwinCore

We offer engineering, architecture, and code that runs reliably in production environments.
Generative AI & RAG Engineering
Machine Learning & Predictive Modeling
Data Engineering & Analytics
Enterprise Integration
MLOps & Production Engineering
Staff Augmentation & Dedicated Teams

RAG (Retrieval-Augmented Generation)

We don't just prompt ChatGPT. We build semantic search engines using Vector Databases (Pinecone, Milvus, Qdrant) so the AI answers exclusively from your internal PDFs, Confluence pages, SQL databases, etc.

Agentic Workflows

Using LangChain or Microsoft Semantic Kernel, we build autonomous agents that can plan tasks, query APIs, and execute complex workflows.

Fine-Tuning

When generic models fail, we fine-tune open weights (Llama 3, Mistral) on your specific industry nomenclature using LoRA (Low-Rank Adaptation) for efficient, cost-effective customization.

Key Benefits of Our Custom AI Development

100+ Projects Delivered
We have moved past the "experimental" phase.
Zero IP Lock-in
You own the model, the weights, and the source code.
Dual-Stack Expertise
We speak both Python (Research) and .NET (Enterprise).
Speed to Value
We offer a 1-week POC (Proof of Concept) to validate feasibility.
Security First
Deployment on your private cloud (Azure/AWS) or on-premise.

Custom AI Solutions We Build

Our TwinCore artificial intelligence development company offers a wide range of customized AI solutions designed to meet your unique needs. Here are some examples of AI solutions we can develop for you.
Biometric Identity & Edge Face Recognition
Intelligent Document Processing & OCR
Agentic Workflows
Voice Interfaces & ASR
Anomaly Detection
Explainable Decision Support Systems

Liveness Detection

We implement active and passive liveness checks (depth sensing, micro-movements) to prevent spoofing with photos or phone screens.

Edge Deployment

We optimize models using TensorRT or TFLite to run on edge devices (like NVIDIA Jetson or Raspberry Pi) or mobile phones. This ensures authentication works even without an internet connection.

Performance Metrics

We tune our thresholds to minimize the False Rejection Rate (FRR) without compromising the False Acceptance Rate (FAR). We are adhering to NIST standards.

Ready to build something real?

Stop reading about AI and start using it. Contact us today for a consultation. We’ll sign an NDA, look at your data, and give you an honest assessment of what’s possible.

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Our Core Technical Competencies

We are engineers, not just API wrappers. Our expertise lies in selecting the right architecture to solve the problem efficiently.
Generative AI & Large Language Models
Natural Language Processing
Classical Machine Learning & Predictive Modeling
Computer Vision

Architecture

Retrieval-Augmented Generation (RAG) for grounding answers in your data; Low-Rank Adaptation (LoRA) for efficient fine-tuning.

The Stack

LangChain, LlamaIndex, Pinecone/Milvus (Vector DBs).

Models We Deploy

Llama 3, Mistral (for local/private use), and Azure OpenAI Service (GPT-4o) for enterprise scale.

Application

We build semantic search engines that "read" thousands of PDF contracts in seconds, not just generate marketing copy.

Industries We Serve

 

Logistics & Transportation

We build the systems that solve the "Last Mile" problem. Our engineering team implements Vehicle Routing Problem (VRP) solvers that factor in traffic windows, fuel costs, and driver shifts to cut deadhead miles. Beyond routing, we deploy predictive maintenance models using IoT sensor data to analyze vibration and engine load, identifying potential component failures weeks before a truck breaks down on the highway.

Fintech & Banking

In finance, a model is useless if it is slow or insecure, so we focus exclusively on low-latency architectures. We deploy anomaly detection engines that score transactions in under 200 milliseconds, blocking suspicious activity without creating friction for legitimate users. To support lending, we build credit risk models using gradient boosting on alternative data sources, ensuring every decision is backed by Explainable AI (XAI) protocols to satisfy strict regulatory audits.

Healthcare & MedTech

We handle sensitive patient data with strict HIPAA-compliant isolation. Our work includes training computer vision models on DICOM data to assist radiologists in spotting anomalies with high precision, as well as building NLP systems that automate patient triage. By automating the backend, we allow medical providers to focus on care rather than administrative overhead.

E-Commerce & Retail

We move beyond generic "people also bought" widgets to build engines that drive Gross Merchandise Value (GMV). We engineer collaborative filtering systems that understand deep user intent and taste profiles, coupled with dynamic pricing algorithms that monitor competitor data to adjust margins in real-time. We also implement visual search capabilities, allowing users to upload photos to find similar products in your catalog.

Cloud Infrastructure & Deployment

We are cloud-agnostic but opinionated. We architect secure, scalable environments that optimize for cost and latency. We deploy where your data already lives to minimize egress fees and security risks.

The Microsoft Azure Ecosystem

For enterprises already entrenched in the Microsoft stack, Azure is the logical choice. We specialize in deploying Azure OpenAI Service, giving you access to GPT-4o and DALL-E 3 with the critical difference that it runs inside your private, compliant virtual network. We ensure your data is never used to train public models.

Beyond the LLMs, we use Azure AI Vision for high-throughput optical character recognition (OCR) and object detection that integrates natively with Azure Storage. For voice interfaces, we implement Azure AI Speech to build conversational apps that handle real-time translation and transcription, seamlessly connecting these services to your existing .NET and SQL Server infrastructure.

The AWS (Amazon Web Services) Stack

When building on AWS, we use Amazon Bedrock to provide flexibility. Bedrock allows us to swap between foundational models (like Claude, Llama, or Titan) via a single API. This way, you aren't locked into one AI vendor.

For conversational interfaces, we build using Amazon Lex, utilizing the same deep learning engine that powers Alexa to create sophisticated, context-aware chatbots. We pair this with Amazon Polly for lifelike neural text-to-speech generation. Additionally, we implement Amazon Q for enterprise search. With its help, we create internal knowledge engines that allow your employees to query unstructured company data securely and instantly.

Why Us

We Are .NET Specialists
Most AI shops only know Python. We know how to run ML inside a C# environment without messy bridges.
We Are Agnostic
If a simple regression model solves the problem better than a neural network, we build the simple one.
We Are Transparent
You get the documentation, the training scripts, and the deployment guide.
Chicago & Estonia
We deliver Western business standards with Eastern European engineering talent.

Comparison Table (SaaS vs Custom vs TwinCore)

Feature Generic SaaS Wrapper In-House Hiring TwinCore Custom Dev
Data Privacy Low (Data often trains their models) High High (Private Cloud/On-Prem)
Cost Structure Per-user / Per-token (Scales up) High Fixed Salaries ($200k+/yr) Project-Based / One-time
Ownership You rent the software You own it You own the source code
Time to Launch Instant 6-12 Months 4-8 Months
Customization Zero (One size fits all) High Exact Fit

Delivery Process

We are not a "fire and forget" vendor. We are your technical partners:

Discovery (2-3 Weeks)
We sit down (or hop on a call) to understand the actual problem.
Prototyping (3-6 Weeks)
We build a proof-of-concept fast. You see results early.
Integration (2-3 Weeks)
We don't just hand you code. We integrate it into your existing software ecosystem (APIs, legacy systems, cloud, etc.).
Support (ongoing)
Models drift over time. We stick around to retrain and optimize them.

100+ Projects. Zero Vendor Lock-in.

Related Topics

Frequently Asked Questions


Will my proprietary data be used to train public models (like ChatGPT)?

Absolutely not. This is the biggest risk in AI, and we architect specifically to prevent it:

  • For External Models. If we use Azure OpenAI, we configure it with "Zero Data Retention" policies. Microsoft does not use enterprise data to train their base models.
  • For Local Models. If you require total isolation, we deploy open-source models (like Llama 3 or Mistral) directly on your own private cloud or on-premise servers. Your data never leaves your firewall. We sign strict NDAs before seeing a single row of your database.

We have a legacy .NET/C# monolith. Can you integrate AI without rewriting our entire backend?

Yes. This is our specific specialty. While most data science happens in Python, we know that the business world runs on .NET and Java. We build secure bridges (using gRPC, REST APIs, or ML.NET) that allow your legacy C# applications to consume modern AI services seamlessly.


Do I own the Intellectual Property (IP)?

Yes. You own everything. Unlike SaaS platforms where you "rent" the intelligence, with TwinCore, you are paying for custom engineering. Upon delivery, you receive:

  • The full source code;
  • The model weights and training scripts;
  • The containerized environment (Docker/Kubernetes config).

We do not use vendor lock-in. You can take our code and have your internal team maintain it tomorrow.


How do you handle AI "hallucinations" (inaccurate answers)?

We use engineering guardrails:

  • RAG Architecture. We force the AI to cite sources from your own uploaded documents. If the answer isn't in your documents, the system is programmed to say, "I don't know", rather than making something up.
  • Deterministic Layers. For critical logic (like pricing or safety checks), we bypass the LLM and use standard code or classical regression models that are 100% predictable.

What is the timeline for a typical AI project?

Custom AI is not a $50/month subscription, but it shouldn't cost millions. Typical AI project timeline varies:

  • Proof of Concept (POC). We usually start with a fixed-price pilot (typically 2–4 weeks) to validate feasibility. This keeps your risk low.
  • Production MVP. A full deployment usually ranges from 3 to 6 months depending on data complexity and integration needs.

We provide a detailed technical roadmap and budget estimate after our initial discovery call.


Do I need to hire a Data Science team to maintain your AI project?

Not necessarily. We build systems designed for "Software Engineers". We set up MLOps pipelines that automate retraining and monitoring. However, if you want us to stay on for maintenance, we offer retainer packages to handle model updates, dependency patching, and performance optimization.


How accurate will the model be?

Beware of anyone who promises 100% accuracy in AI. They are lying. We define success metrics before we write code. We establish a baseline (e.g., "current human error rate is 15%") and aim to beat it significantly (e.g., "model error rate of 5%"). If the technology isn't mature enough to solve your specific problem with high confidence, we will tell you in the Discovery phase.


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