Our AI Development Services
From generative AI and custom ML models to autonomous agents and MLOps infrastructure. Every engagement is staffed with AI specialists who have shipped production systems, not just built demos.
Generative AI Development
We build generative AI systems that ship to real users: custom GPT-based applications, multimodal AI products, AI copilots, and content generation pipelines. We work with OpenAI's GPT-4o, Anthropic Claude, Google Gemini, and open-source models like LLaMA 3 and Mistral. Every system includes structured output handling, safety guardrails, and observability from day one.
- GPT-4o and Claude-powered applications with RAG, tool use, and function calling
- Custom AI copilots embedded in your existing web and mobile products
Machine Learning Development
We design, train, and deploy custom ML models for prediction, classification, clustering, and anomaly detection. We work across the full ML lifecycle, from data preprocessing and feature engineering through model selection, hyperparameter tuning, validation, and production deployment on AWS SageMaker, Azure ML, or Google Vertex AI.
- End-to-end ML pipeline: data prep, training, evaluation, and scalable model serving
- Experiment tracking with MLflow and Weights & Biases for full reproducibility
Natural Language Processing (NLP)
We build NLP systems that extract meaning from unstructured text: sentiment analysis, entity extraction, document classification, named entity recognition, summarization, semantic search, and language translation. We use spaCy, Hugging Face Transformers, and fine-tuned BERT and RoBERTa models tailored to your domain vocabulary.
- Custom NLP pipelines for text classification, NER, and document summarization
- Semantic search powered by dense vector embeddings and similarity re-ranking
Computer Vision
We develop computer vision systems for image recognition, object detection, video analytics, OCR, visual quality inspection, and medical imaging analysis. We use YOLO v11, Detectron2, OpenCV, and MediaPipe, with edge deployment on NVIDIA Jetson hardware for real-time inference in manufacturing and logistics environments.
- Real-time object detection and tracking at 30+ FPS on edge and cloud infrastructure
- Visual quality inspection at 99%+ precision for manufacturing and logistics pipelines
AI Agent Development
We build autonomous AI agents that plan, reason, and execute multi-step tasks across your enterprise systems: browsing the web, calling APIs, reading documents, and taking actions with minimal human oversight. Built with LangChain, LangGraph, and AutoGen for reliable, auditable agent behavior with structured error handling.
- Multi-step reasoning with tool use across your internal APIs and external data sources
- Audit logs and human-in-the-loop checkpoints for enterprise compliance requirements
AI Chatbot & Conversational AI
We build intelligent chatbots and voice assistants that go beyond keyword matching. They understand intent, maintain context across a conversation, and handle complex queries about your products, services, and knowledge base. We integrate with Twilio, Intercom, WhatsApp Business API, Slack, and custom web interfaces.
- Context-aware multi-turn conversations with structured handoff to human agents
- Grounded responses via RAG: your chatbot answers from your knowledge, not hallucinations
LLM Fine-Tuning & RAG Development
We fine-tune foundation models on your proprietary datasets using LoRA and QLoRA techniques for domain-specific accuracy. We also build retrieval-augmented generation (RAG) pipelines that connect LLMs to your knowledge base using vector databases (Pinecone, Weaviate, or pgvector) for accurate, grounded responses without hallucination.
- LoRA and QLoRA fine-tuning for domain adaptation on datasets as small as a few thousand examples
- Hybrid RAG with vector search + BM25 keyword ranking for higher retrieval precision
AI Consulting & Strategy
We assess your AI readiness, identify high-ROI use cases, evaluate your data maturity, and build a phased AI roadmap aligned with your business objectives. You get specific, prioritized recommendations backed by our experience delivering production AI across healthcare, fintech, logistics, and retail. No generic strategy decks.
- AI readiness assessment covering data quality, infrastructure, and team capability gaps
- Prioritized use case backlog with ROI estimates and implementation feasibility scores
AI Integration & Deployment
We integrate AI capabilities into your existing enterprise systems (ERP, CRM, legacy databases, SaaS platforms) via REST APIs, event-driven architecture, and middleware. We handle model serving infrastructure, latency optimization, and API rate management so your AI layer runs at enterprise scale without disrupting existing systems.
- Zero-downtime integration with production systems via API layer and feature flags
- Sub-100ms P99 inference latency at enterprise request volumes via optimized serving
MLOps & AI Infrastructure
We build the engineering infrastructure that keeps your AI systems healthy in production: CI/CD for ML models, automated retraining pipelines triggered by data drift detection, experiment tracking with MLflow and Weights & Biases, model versioning, A/B testing frameworks, and scalable serving on Kubernetes.
- Automated retraining pipelines with drift detection alerts and performance thresholds
- Multi-environment model registry with blue-green deployment for zero-risk model updates
AI Capabilities We Engineer
These are the specific AI capabilities we build and deploy in production, not a list of buzzwords. Each represents systems we have shipped for real clients across industries.
Predictive Analytics
Historical data contains the patterns that predict your future: customer churn, equipment failure, demand spikes, revenue shortfall. We build ML models that surface these signals before they become problems. Most organizations sitting on 2+ years of transactional data are holding untapped predictive value they haven't yet extracted.
AI Solutions We've Delivered
Real outcomes from real AI systems. Each engagement below reflects the full lifecycle, from data assessment through to production deployment and ongoing monitoring.
Four million transactions a day, sub-200ms latency, and a false positive rate low enough not to block legitimate purchases at checkout: that was the bar a US payments company set for the ML-powered fraud detection system it needed.
We designed a two-stage ML pipeline: a real-time gradient boosting model (XGBoost) scoring every transaction at the transaction layer, followed by a PyTorch-based deep learning model that runs contextual analysis on high-risk cases. Feature engineering drew on 180+ behavioral signals, deployed on AWS SageMaker with auto-scaling inference endpoints.
AI Solutions Across Industries
We have delivered production AI systems across 12+ industries. Deep domain knowledge means fewer surprises. We understand your data, your compliance constraints, and your users before model training begins.
- AI Diagnostic Imaging
- Predictive Patient Monitoring
- Drug Discovery Acceleration
- Clinical Documentation AI
- Real-Time Fraud Detection
- AI Credit Scoring
- Algorithmic Risk Assessment
- Anti-Money Laundering Models
- Personalized Recommendations
- Demand Forecasting
- Dynamic Pricing Models
- Visual Product Search
- Route Optimization AI
- Predictive Maintenance
- Inventory Demand Forecasting
- Warehouse Automation
- Vision-Based Quality Inspection
- Predictive Equipment Maintenance
- Production Line Optimization
- Defect Detection AI
- Automated Claims Processing
- AI Risk Assessment
- Underwriting Model Automation
- Fraud Detection Pipelines
- Personalized Learning Paths
- AI Tutoring Systems
- Automated Assessment Tools
- Student Performance Prediction
- AI Property Valuation
- Market Prediction Models
- Intelligent Property Matching
- Document Extraction AI
Our AI Development Process
AI development is not standard software development. Data is central. This 7-step process reflects that, from assessing your data readiness through to automated retraining in production.
We start by understanding your business: the decisions you make, where data exists, and where AI can create the most measurable impact. We run a structured AI readiness assessment covering your data maturity, infrastructure, team capability, and regulatory constraints. The output is a prioritized list of AI use cases with estimated ROI and implementation feasibility.
- AI readiness assessment across data maturity, infrastructure, and team capability
- Prioritized use case backlog with ROI estimates and feasibility scores
AI quality starts with data quality. We audit your existing data sources, design collection pipelines where gaps exist, clean and normalize data, handle missing values and class imbalances, and create labeled training datasets. This phase often reveals data quality issues that would have silently degraded model performance if caught later.
- Data audit covering quality, completeness, and labeling requirements
- Collection pipeline design and preprocessing for training-ready datasets
We select the right approach for your problem: custom model architecture, fine-tuning a foundation model, or building a RAG pipeline. This decision depends on your data volume, required accuracy, inference latency requirements, and budget. We document the architecture and present it to your team before any compute spend begins.
- Architecture decision: custom model vs. fine-tuning vs. RAG, based on your data and latency needs
- Full architecture document reviewed with your team before any compute spend
We train models iteratively, running controlled experiments tracked in MLflow or Weights & Biases. Each experiment compares hyperparameter configurations, architecture variants, and feature sets against defined performance benchmarks. Your team sees results in real-time — you always know what was tried and why.
- Controlled experiments with full tracking in MLflow or Weights & Biases
- Real-time experiment dashboards shared with your team throughout training
We evaluate models beyond accuracy, covering fairness across demographic groups, robustness to edge cases and out-of-distribution inputs, calibration of confidence scores, and behavior under adversarial conditions. We conduct A/B testing on held-out data and, where appropriate, human-in-the-loop validation with your domain experts.
- Fairness, robustness, and calibration testing beyond standard accuracy metrics
- Human-in-the-loop validation with your domain experts on held-out data
We deploy trained models to production on your cloud environment (AWS SageMaker, Azure ML, or Google Vertex AI) and integrate with your existing systems via REST APIs or event-driven architecture. We set up model serving infrastructure, configure auto-scaling, and run load tests before go-live.
- Model serving on SageMaker, Azure ML, or Vertex AI with auto-scaling endpoints
- Load-tested integration via REST API or event-driven architecture before go-live
Production AI systems drift. As real-world data distribution shifts away from training data, model performance degrades. We build automated monitoring that tracks prediction quality, triggers alerts when drift is detected, and initiates retraining pipelines when thresholds are breached. You get monthly performance reports, not silence until something breaks.
- Automated drift detection with retraining triggers and performance alerts
- Monthly model performance reports, not silence until something breaks
Why Choose Avenotech as Your AI Development Company
These are the reasons clients trust us with their most technically complex initiatives, and why they stay with us after the first project.
We have shipped AI systems at production scale, not just built proof-of-concepts that never see users. The gap between a working prototype and a system your users can rely on is where most AI projects fail. We think about inference latency, monitoring, retraining pipelines, and data drift from the first architecture call, not after the model is deployed.
Our team includes AI engineers, data scientists, NLP specialists, computer vision engineers, and MLOps practitioners. We work daily with GPT-4o, Claude, LangChain, TensorFlow, PyTorch, Hugging Face Transformers, and the full MLOps toolchain. We don't learn on your project. We bring depth from 60+ AI/ML systems delivered across 5+ years.
Every AI system we build includes data privacy controls, encrypted model serving, role-based access to predictions and training data, model explainability reporting, and bias mitigation documentation. For regulated industries, we build to HIPAA, GDPR, SOC 2, and ISO 27001 from the architecture phase. Compliance is not retrofitted after the model is trained.
Most AI vendors specialize in one layer: data, or modeling, or deployment. We cover the entire AI lifecycle: data engineering and preparation, model development, system integration, deployment infrastructure, and ongoing MLOps. One team, one contract, no handoffs between vendors at each phase.
Generic AI models produce generic results. Our engineers bring hands-on experience in healthcare, fintech, logistics, retail, and enterprise software. They understand the domain constraints, regulatory requirements, and data patterns specific to your industry before writing a line of code.
Every experiment is documented. Every decision has a rationale. You get access to experiment tracking dashboards in real-time, sprint demos after every two-week cycle, and direct access to your AI engineering team. No black boxes, in the model or in the process.
AI Technologies & Frameworks
We build with production-proven AI tools, the same frameworks and models used by the best AI engineering teams globally. No proprietary wrappers, no lock-in.
What Our Clients Say
The fraud detection system they built handles 4 million transactions a day with a false positive rate under 0.3%, and we've seen fraudulent payouts drop 62% since it went live. Honestly the bigger win was they got our compliance constraints from day one, didn't need weeks of back and forth like most vendors do.
Our ticket resolution time dropped 58% in the first 90 days after their RAG pipeline went live, which was great, but honestly what I appreciated more was that they explained every architectural decision in plain terms so we could actually evaluate it ourselves. We came away with a system we understand and can maintain after handoff.
We'd had another vendor's model failing accuracy benchmarks in production, so we brought Avenotech in to sort it out. Took them eight weeks to find the data leakage issue, retrain the model, and hit our 94% F1 target. That kind of depth isn't something you can really fake.
Flexible Engagement Models
Three formats to match your AI project stage, whether you're validating an idea, building a defined system, or scaling an existing AI initiative.
A fully integrated AI team (engineers, data scientists, ML specialists, and MLOps practitioners) working exclusively on your project. The team attends your standups, uses your tools, and treats your AI initiative as their core product. Scales up or down month to month.
Long-term AI initiatives and ongoing model development- Dedicated AI engineers and data scientists on your sprint board
- MLOps engineer included for production monitoring and retraining
- Scale team size up or down with 30-day notice
- Works in your tools: Jira, Linear, Slack, Notion
- Monthly retainer billing, no surprise invoices
Pay for actual hours worked. Best for AI projects with evolving scope, exploratory R&D phases, or when you need to redirect priorities as experiments complete. Full visibility into hours logged with task-level breakdowns each week.
Exploratory AI projects and R&D phases- Weekly time reports with experiment-level task breakdown
- Redirect priorities between experiments without contract changes
- Ideal for proof-of-concept work before committing to full builds
- No minimum commitment beyond 4-week notice period
- Transparent hourly rates with no hidden fees
Defined scope, fixed timeline, fixed budget. Best for well-scoped AI deliverables: a specific model, a defined integration, or a proof-of-concept with clear acceptance criteria. Full source code, model weights, and IP transfer included.
Well-defined AI builds with clear acceptance criteria- Detailed technical specification and acceptance criteria before start
- Milestone-based delivery with demo at each stage
- Fixed price with 30-day post-delivery warranty on defects
- Full model weights, code, and documentation ownership transferred
- Includes model performance report against defined benchmarks
Certifications & Compliance Standards
We build AI systems with data privacy, model explainability, bias mitigation, and regulatory compliance designed in from day one, not retrofitted before an audit. Our approach covers GDPR-compliant data handling, HIPAA-safe healthcare AI, responsible AI practices, and full audit trails for model decisions.
How Much Does AI Development Cost?
AI project costs vary widely based on complexity, data readiness, and scope. The biggest variable is often your data — if it's clean and structured, development begins fast. If it needs collection and labeling, that work alone can account for 30–50% of total cost.
These ranges reflect what clients typically invest. Data preparation costs are not always included and can add significantly to the total, especially for computer vision and NLP projects requiring labeled datasets.
- Customer support
- Internal knowledge assistants
- Document search
- FAQ automation
- Workflow automation
- AI copilots
- Recommendation engines
- Analytics assistants
- Multi-agent systems
- Computer vision
- Custom ML pipelines
- Enterprise integrations
Every AI initiative is unique. We provide a detailed, itemized estimate after our discovery call — at no charge and with no obligation.
Frequently Asked Questions About AI Development
Straight answers to the questions technical decision-makers ask most often. No jargon, no vague estimates.
How much does AI development cost?
AI projects start from around $25k for a chatbot or knowledge assistant, scale to $50k–$150k for business AI platforms, and move to custom pricing for enterprise systems involving custom model training, computer vision, or full MLOps infrastructure. The biggest variable is often your data — if it is clean and ready, development begins fast. If it needs collection and labeling, that work alone can account for 30–50% of total project cost. See the Investment section above for tier-by-tier ranges, or request a tailored estimate based on your specific use case and data maturity.
How long does it take to build an AI solution?
AI chatbots and knowledge assistants built on existing LLM APIs typically take 4–8 weeks from kickoff to deployment. Business AI platforms — custom ML models, recommendation engines, AI copilots, and workflow automation — run 2–4 months. Enterprise systems involving custom model training, computer vision pipelines, multi-agent architectures, or full MLOps infrastructure are scope-dependent and typically range from 4–8 months. Data preparation is the most common timeline variable. Projects with clean, structured data start model development immediately; projects requiring data collection and labeling add 3–6 weeks before a single model is trained.
What is the difference between AI, ML, and deep learning?
AI (artificial intelligence) is the broad field of building systems that can perform tasks that typically require human intelligence: planning, reasoning, language, and perception. Machine learning (ML) is a subset of AI where systems learn from data rather than being explicitly programmed with rules. Deep learning is a subset of ML that uses artificial neural networks with many layers to process complex patterns in images, text, speech, and video. In practice: an ML model predicts loan defaults from structured data; a deep learning model reads an X-ray and flags anomalies. Both are AI. The distinction matters when you're selecting the right approach for your problem.
Can AI be integrated with our existing systems?
Yes. We design AI systems with integration as a first-class concern, not an afterthought. We connect AI capabilities to ERP systems, CRM platforms, legacy databases, REST and GraphQL APIs, data warehouses, and cloud-native services via well-defined microservices and API contracts. Most integrations follow an API-first pattern: the AI system exposes endpoints your existing applications call, and no core system rewrites are required. We audit your existing integration landscape during discovery and design middleware for cases where standard API patterns don't apply.
What data do you need to build an AI solution?
It depends on your use case. Predictive analytics typically needs 2–5 years of structured historical data. Computer vision models need thousands of labeled images per category. NLP models need text samples relevant to your domain. We assess your data during the discovery phase, covering quantity, quality, labeling status, storage location, and privacy requirements. If you don't have enough data, we can help design a data collection strategy, use data augmentation techniques, or select a foundation model that can be fine-tuned on smaller datasets.
How do you ensure data security in AI projects?
We treat data security as non-negotiable from day one. We sign an NDA before any data is shared. All data in transit is encrypted with TLS 1.3; data at rest uses AES-256 encryption. We deploy in your cloud environment or a dedicated isolated environment, so your data never sits on shared infrastructure. Role-based access controls limit who can see what at every layer. For regulated industries, we build to GDPR, HIPAA, and CCPA requirements. Data anonymization and differential privacy techniques are applied where appropriate before model training.
What is generative AI and how can it help my business?
Generative AI is a class of AI models that produce new content (text, images, code, structured data, audio) rather than just classifying or predicting from existing data. Business applications include customer support automation, content generation at scale, code assistance, document processing, and internal knowledge management. The key is identifying where content generation or intelligent retrieval genuinely saves time, not adding AI for its own sake.
What is LLM fine-tuning and when do I need it?
Fine-tuning takes a pre-trained foundation model (like LLaMA 3 or Mistral) and continues training it on your proprietary dataset so it learns your domain's terminology, tone, and knowledge. You need it when generic LLMs give responses that miss your industry context, use incorrect terminology, or fail to follow your specific output format consistently. It's not always the right answer. For many use cases, RAG (retrieval-augmented generation) with a well-structured knowledge base delivers better results at lower cost and with easier ongoing updates. We assess both approaches and recommend based on your use case and data volume.
What is RAG (Retrieval-Augmented Generation)?
RAG is an architecture that connects an LLM to an external knowledge base: your documentation, database, product catalog, legal contracts, or support history. When a user asks a question, the system retrieves the most relevant chunks from your knowledge base, passes them to the LLM as context, and the model generates a response grounded in your data. This eliminates hallucination from generic training data, keeps answers current without retraining, and lets you control exactly what knowledge the model draws on. RAG powers most of the enterprise AI search, support automation, and knowledge management systems we build.
Do you provide ongoing AI model maintenance?
Yes. Production AI systems require ongoing care that traditional software does not. Models drift as real-world data distribution shifts away from training data, performance degrades over time, and infrastructure needs scaling as usage grows. Our MLOps service covers model monitoring with drift detection alerts, automated retraining pipelines triggered by performance thresholds, A/B testing for model updates, infrastructure scaling, and monthly performance reporting. Most clients retain us post-launch on a monthly engagement covering the full monitoring and optimization cycle.
What engagement models do you offer for AI projects?
We offer three models. A Dedicated AI Team gives you full-time AI engineers, data scientists, and MLOps specialists working exclusively on your project, best for long-term initiatives where scope evolves with new findings. Time and Material is ideal for exploratory phases, R&D, and projects where you want to redirect priorities as experiments complete. Fixed Price suits well-defined AI projects with a clear, locked scope: a specific model, an integration task, or a defined proof-of-concept. Each model is described in the Engagement Models section above.
How do you handle intellectual property for AI models?
You own everything. We sign an NDA before our first call. All trained models, fine-tuned weights, data pipelines, code, documentation, and evaluation results are transferred to you upon project completion. We use isolated development environments with strict access controls; your data and models are never used for any other project or client. We do not retain any copies after handoff. Your models, trained on your data, using your compute, belong entirely to you.
Ready to Build Your AI Solution?
From intelligent automation to custom AI products, let's engineer AI that delivers measurable business impact. Free consultation, NDA protected. We'll review your use case, assess your data readiness, and give you an honest estimate with no obligation.