AI development company · Generative AI · RAG · AI Agents · Machine Learning · MLOps · Computer Vision · Robotics
We design, build and deploy custom AI solutions — generative AI apps, RAG systems, AI agents, machine learning models, MLOps pipelines and AI robotics — that move from prototype to production and keep performing once they get there.
14+
Years of Experience
200+
Enterprises Served
50+
Countries Served
500+
Solutions Delivered
01 AI development services
From generative AI and RAG to machine learning, MLOps and robotics, our AI engineers cover the full stack — strategy, data, models, infrastructure and product — so nothing gets lost between vendors.
A · Generative AI & RAG
We build generative AI applications and retrieval-augmented generation (RAG) systems that ground large language models in your documents, databases and APIs — with citations, access control and evaluation built in.
> ask your knowledge base
B · AI agents
Tool-using AI agents and multi-agent systems that automate research, support, operations and back-office workflows — with human approval where it matters.
C · Machine learning & MLOps
Custom machine learning development for forecasting, scoring and classification, shipped with MLOps pipelines for monitoring, drift detection and automated retraining.
MLOps servicesD · Computer vision & robotics
Computer vision for inspection, detection and tracking, and AI robotics development for perception, navigation and manipulation — deployed on the cloud or at the edge on devices like NVIDIA Jetson.
AI robotics developmentAI Services
Generative AI
Machine Learning & Data
Robotics & Emerging AI
02 Industries we serve
We build industry-specific AI solutions for 31 sectors — combining domain knowledge with generative AI, machine learning and automation to solve problems unique to each one.
HIPAA-aware AI that reduces administrative load and supports clinical teams.
Explore AI in healthcare
Explainable models and secure LLM workflows for regulated financial services.
Explore AI in finance & fintech
Personalization and automation that lift conversion and cut support cost.
Explore AI for ecommerce & retail
Vision, sensor and robotics AI for the factory floor.
Explore AI in manufacturing
Forecasting and optimization models that keep goods moving.
Explore AI for logistics & supply chain
Adaptive learning products and AI tutors that scale personal attention.
Explore AI in education & edtech03 Our AI development process
A proven five-stage AI development lifecycle that de-risks every investment: validate value early, then engineer for scale.
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Why choose us
Most AI projects stall between demo and deployment. These are the engineering rules every system we build follows, so yours ships, scales and stays trustworthy.
Illustrative: every release is gated on an evaluation suite like this.
We pick between OpenAI GPT, Anthropic Claude, Google Gemini, Llama, Mistral and custom models on accuracy, latency and cost — never on vendor lock-in.
Private cloud, VPC and on-premise deployment options. Your data is never used to train third-party models.
Every model and LLM feature ships with an evaluation suite, so quality is measured, not assumed.
Role-based access, audit logs, PII redaction and architectures designed around HIPAA, GDPR and SOC 2 requirements.
Versioned data, models and prompts with automated testing, monitoring and one-click rollback.
Source code, trained models, fine-tuned weights, prompts and documentation are handed over in full.
05 AI technology stack
We choose proven, well-supported AI models, frameworks and cloud platforms for each project, so your solution is maintainable long after launch. Drag the ring to explore.
LLMsSTK-01
Open sourceSTK-02
AgentsSTK-03
RAGSTK-04
Vector DBSTK-05
Deep learningSTK-06
Classical MLSTK-07
VisionSTK-08
VoiceSTK-09
MLOpsSTK-10
CloudSTK-11
RoboticsSTK-12
12 stack layers · drag to explore
06 Engagement models
Whether you need AI strategy, a complete AI product or extra AI engineering capacity, pick the model that fits your stage and budget.
Best for Teams defining their AI strategy
Most requested
Best for Launching a new AI product or feature
Best for Scaling an existing AI roadmap
We provide end-to-end AI development services: AI consulting, generative AI development, RAG development, LLM fine-tuning, AI agent and chatbot development, AI integration, machine learning development, MLOps, computer vision, NLP, predictive analytics, intelligent automation, edge AI and AI robotics development.
Cost depends on scope, data readiness and deployment requirements. Most clients start with a fixed-price discovery or proof of concept, which gives a validated solution and an accurate estimate for the full build. Book a free consultation to get a tailored quote.
A proof of concept typically takes two to four weeks. A production-ready generative AI app, RAG system or AI agent usually takes two to four months, depending on integrations, data preparation and compliance needs.
Retrieval-augmented generation (RAG) connects a large language model to your own documents and databases, so answers are grounded in your data and can cite sources. RAG is the right choice for knowledge assistants, customer support bots and internal search where accuracy and up-to-date information matter.
Yes. We integrate AI into existing web and mobile apps, CRMs, ERPs, helpdesks and data warehouses through APIs, so your team gets AI capabilities inside the tools they already use.
We are model-agnostic and work with OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral and open-source models from Hugging Face, as well as custom models trained with PyTorch and TensorFlow.
We support private cloud, VPC and on-premise deployments, encrypt data in transit and at rest, apply role-based access and PII redaction, and design systems around HIPAA, GDPR and SOC 2 requirements. Your data is never used to train third-party models.
Yes. Our MLOps and LLMOps services cover monitoring, drift detection, retraining, prompt and model versioning, and cost optimization, so your AI keeps performing after launch.
