We are an AI development company, not a feature-plugging shop. We train custom models on your data, fine-tune frontier LLMs for your domain, build RAG pipelines over your knowledge base, and deploy them with production-grade MLOps infrastructure, RAGAS-evaluated accuracy, and LangSmith observability from day one.
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AI development is the full engineering lifecycle of creating artificial intelligence systems: collecting and structuring training data, designing or selecting model architectures, executing training runs or fine-tuning experiments on GPU infrastructure, evaluating model quality with domain-specific metrics, deploying models to production inference endpoints, and operating and improving models over time with MLOps practices. It is the work of building the AI system itself, not building a product wrapper around an existing API.
AI development at Maze Digital spans ten disciplines: custom AI model training on proprietary datasets using PyTorch and TensorFlow; LLM fine-tuning with LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) on models including GPT-5.5 mini, Claude Opus 4.7, Gemini 3.1 Pro, Llama 4 Scout and Maverick, and Mistral; RAG pipeline development grounding frontier LLMs in private data with Pinecone, Weaviate, and pgvector vector databases; computer vision model training for object detection, image classification, and semantic segmentation
We cover the full spectrum of production AI engineering, from raw dataset curation to fine-tuned model deployment, from RAG pipeline design to MLOps infrastructure and drift monitoring.
We build and train custom AI models from scratch on your proprietary datasets when no existing pre-trained model fits your domain. This includes dataset collection and curation, data labeling and quality engineering, model architecture selection (transformer, CNN, RNN, or hybrid), training run management on GPU clusters (A100, H100), hyperparameter optimization, and rigorous train/validation/test evaluation with domain-specific metrics. We train models for clinical text analysis, industrial defect detection, legal document classification, financial fraud identification, and any domain where generic pre-trained models underperform on your data distribution.
Model landscape verified April 29, 2026. Every model choice is justified against your specific use case, latency budget, cost envelope, and compliance requirements, we do not default to one LLM for everything.
OpenAI Released on Apr 23, 2026
OpenAI's most capable model. Designed for agentic coding, computer use, and complex knowledge work. Matches GPT-5.4 latency while delivering significantly higher intelligence. API available from April 24, 2026 via gpt-5.5-2026-04-23. Fine-tunable via OpenAI fine-tuning API (GPT-5.5 mini for cost-effective fine-tuning at scale).
OpenAI Released on Apr 23, 2026
Anthropic's most capable publicly available model. Step-change improvement in advanced software engineering, complex long-running agentic tasks, and higher-resolution vision. Reports missing data correctly rather than hallucinating. Outranked GPT-5.5 across 7 benchmark categories in independent testing. Note: Claude Mythos Preview (Apr 7, 2026) is more powerful but invitation-only for cybersecurity use.
Meta Released on Apr 23, 2026
Open-weight MoE models. Scout: 17B active params, 10M token context, runs on single H100. Maverick: 17B active params, 128 experts, 1M token context. Self-hostable on AWS, GCP, or on-premise, zero data leaves your infrastructure. Fine-tunable with Unsloth on your own GPU infrastructure. The only option when data residency or HIPAA prevents API-based LLMs.
Mistral AI Released on Apr 8, 2026
Mistral: French AI company with EU data sovereignty guarantees. Mixtral 8x22B MoE delivers near-frontier quality at significantly lower inference cost. GDPR-native. Muse Spark: Meta's newest and most capable model family released April 8, 2026 by Meta Superintelligence Labs, separate from the Llama series. Access model still being determined.
Google DeepMind Released on Apr 23, 2026
Google's current flagship reasoning model. 1 million token context window with adaptive thinking for complex agentic workflows. Integrated Google Search grounding for up-to-date knowledge. State-of-the-art multimodal reasoning across text, images, audio, and video. Available via Gemini API, Vertex AI, and NotebookLM. Strong for Google Workspace automation.
HOW WE BUILD AI
A rigorous, evaluation-driven process that protects your investment and ensures the AI system we deploy actually solves the problem you hired us to solve.
We run a structured discovery process to define the AI problem precisely, assess data readiness (what exists, what needs collection, what needs labeling), evaluate whether to train from scratch, fine-tune, or use RAG, estimate the data and compute requirements, and design the evaluation framework that will define success. We produce an AI feasibility document with a clear recommendation and three alternative approaches ranked by risk, cost, and timeline before any contract is signed. This prevents the most expensive mistake in AI projects: discovering mid-build that the problem requires more data than exists, or that the chosen approach cannot achieve the required accuracy.
We collect, clean, deduplicate, label, and structure your training data into the format required for the chosen training approach. For fine-tuning: instruction-input-output triplet construction for instruction tuning, preference pair creation for RLHF-style alignment, or domain-specific completion pairs for domain adaptation. For computer vision: bounding box annotation, polygon segmentation masks, or image-level classification labels. For RAG: document ingestion pipeline, chunk size tuning against RAGAS retrieval metrics, embedding model benchmarking. For predictive ML: feature engineering, categorical encoding, temporal splits for time-series, and class balance analysis with resampling strategy. Data quality is the single largest determinant of model quality. We treat data engineering as a first-class engineering discipline, not a preprocessing afterthought.
Before executing any training run, we establish a baseline by evaluating the best available pre-trained model on a held-out test set using the domain-specific metrics agreed in stage one. This tells us exactly what gap fine-tuning or custom training needs to close, which pretrained model to start from, and whether the task is technically feasible at the required accuracy threshold. We evaluate multiple candidate models (GPT-5.5 vs Claude Opus 4.7 vs Llama 4 vs Mistral) against your test set to select the best base before training begins. This eliminates the expensive failure mode of fine-tuning the wrong base model and discovering it after a full training run.
For fine-tuning projects: we execute LoRA or QLoRA training runs on GPU infrastructure (A100 or H100 clusters on AWS or GCP), run ablation studies varying rank, alpha, learning rate, and batch size, track experiments in MLflow or Weights and Biases, merge adapters and evaluate the merged model against the baseline metrics from stage three. For RAG pipeline projects: we build the complete ingestion, chunking, embedding, and vector store pipeline, implement hybrid BM25 plus semantic retrieval, add reranking, and run RAGAS evaluation against the ground-truth dataset. For custom model training: we train from the selected architecture with the prepared dataset, monitor training curves for convergence and overfitting, and evaluate on the held-out test set at checkpoint intervals. You receive evaluation reports and staging access after each training milestone.
Before production deployment, we run a comprehensive evaluation and security suite. For LLM-based systems: RAGAS faithfulness, answer relevancy, context precision, and context recall against a ground-truth test set. OWASP LLM Top 10 adversarial prompt injection testing; jailbreaking attempt simulation; PII leakage audit. For vision and predictive models: held-out test set evaluation with confidence interval reporting; worst-case analysis on failure mode slices; demographic parity analysis for bias in classification models; latency benchmarking under peak inference load. Every model we deploy has a model card documenting training data, evaluation methodology, known failure modes, intended use population, and out-of-scope use cases. This documentation is required for healthcare AI, and we produce it for every project regardless of regulatory requirements.
We deploy the AI system to production with containerized serving (Docker, FastAPI or vLLM for LLMs), auto-scaling Kubernetes for inference traffic management, CI/CD pipelines for model update deployment with automated evaluation gates, LangSmith tracing for every LLM call, Datadog dashboards for prediction latency, throughput, and business accuracy metrics, and automated drift detection triggering retraining alerts when incoming data distribution deviates from training distribution. Post-launch, we offer monthly AI improvement sprints: analyzing production failure patterns from LangSmith or monitoring data, executing targeted prompt updates or fine-tuning runs, re-evaluating against the ground-truth dataset, and deploying improvements through the CI/CD pipeline with staged rollout and rollback capability.
Most vendors can call an LLM API. Very few can train a model, measure it rigorously, deploy it with proper MLOps, and improve it continuously. We do all of it.
50
B2B SaaS Products Shipped
100%
TypeScript, Type-Safe End to End
4.9
Clutch Rating from SaaS Clients
$0
Data Isolation Incidents Across Portfolio
50
B2B SaaS Products Shipped
100%
TypeScript, Type-Safe End to End
4.9
Clutch Rating from SaaS Clients
$0
Data Isolation Incidents Across Portfolio
Our Work
Maze Digital is your Dedicated Design and Frontend Engineering partner for Complex Software.
Construction • GIS Web App
A full-scale hazard management and asset tracking web platform for industrial power generation, petroleum, and construction environments. Real-time GIS mapping, work order management, and multi-site dashboards.
Faster Incident Reporting
User Adoption Rate
Saloon App
An AI-powered salon platform for hairstylists and beauty studios. Clients can preview hair color, length, hairstyles, and wig transformations. Features include AI styling recommendations, client management, and personalized beauty previews.
Increase in Online Orders
Page Load Speed
AI Technology Stack
Every technology in our AI stack is chosen for production reliability, observability, and the ability to hire engineers who know these tools, not for novelty or hype.
Verified reviews from startup founders who went from idea to live product with Maze Digital.
Maze Digital launched WorkSafe on iOS and Android simultaneously in 8 weeks. Their design-first sprint approach meant development never waited on designs, and I received a working build I could test on my real phone after every two-week sprint. The RAG-based AI hazard classification they built passed our enterprise clients' security reviews on first assessment. This is a team that actually delivers what they scope.
Jonathan Reed
CTO, WorkSafe Solutions
We evaluated seven app development companies before choosing Maze Digital. What made them stand out was that they scoped the Alpha Arc MVP with us in a three-day session, showed us exactly what we would get and when, and then delivered it exactly on time at the agreed price. The AI coaching system they built using Claude reduced our Day 30 churn from 59% to 33%. 10,000 downloads in 90 days. Worth every dollar.
Mark S.
Founder, Alpha Arc
The level of detail in their UI/UX work is truly exceptional. From the initial research phase to the final design, every aspect was meticulously structured, thoughtful, and centered around the user. They not only considered the aesthetic appeal but also ensured that the functionality was seamless and intuitive. Their commitment to understanding user needs led to innovative solutions that enhanced the overall experience, making it both engaging and efficient.
Jennie
Book Writer
AI performance is highly domain-specific. A medical AI system requires clinical accuracy standards, HIPAA compliance, and model cards. An industrial AI system must run offline on rugged hardware. We bring domain knowledge to every project.
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Real questions from technical decision-makers and founders, answered with current accuracy.
