AI Consulting and Strategy
We help you find where AI creates measurable value: use-case discovery and prioritization, AI readiness assessment (data, infrastructure, skills), a roadmap with KPIs, and build-vs-buy guidance.
Artificial Intelligence Development
AI Development Services That Move From Pilot to Production
Xcrino Business Solutions is an AI development company based in New Delhi, India. We build AI agents, generative AI and LLM applications, RAG systems, machine learning models, and AI features inside ERP and business software, for startups, SMEs, and enterprises.
Since 2016 we have served 560 clients in 26 countries, with 69+ in-house developers and a 94% project success rate. We build AI that connects to your data and systems, is tested before it is trusted, and keeps working after launch.
- Since 2016
- 560
- clients served
- 26
- countries
- 69+
- developers
- 94%
- project success rate
At a glance
Key takeaways
- AI development services cover use-case selection, data preparation, engineering, evaluation, deployment, and monitoring of AI systems for business use.
- An AI agent is software that uses a large language model to plan and complete multi-step tasks with your tools and data.
- Use RAG when answers must come from your own changing documents, and fine-tuning when you need consistent format, tone, or domain behaviour.
- Most AI projects stall at integration and evaluation, not at the model, so a proof of concept should have success criteria agreed in advance.
- AI systems have running costs for model usage, infrastructure, and monitoring, and these should be estimated before the build.
Quick answer
What are AI development services?
AI development services cover the strategy, data preparation, design, engineering, evaluation, deployment, and ongoing monitoring of artificial intelligence systems for business use. Xcrino Business Solutions, founded in 2016 in New Delhi, provides AI consulting, AI agent development, generative AI and LLM applications, RAG systems, machine learning, computer vision, NLP, and AI integration into ERP and custom software, with testing, guardrails, and human oversight built in, for clients in 26 countries.
Introduction
AI Only Pays Off When It Works Inside the Business
Many AI initiatives stall after a promising demo. The model works, but it can't reach your data, doesn't fit your workflows, can't be trusted on edge cases, or costs more to run than it saves.
As an AI development company that also builds ERP, custom software, and enterprise applications, we approach AI as engineering, not experimentation. Our AI development services cover use-case selection, data readiness, model and architecture choice, development, evaluation, security, deployment, and monitoring, so the system you pilot is the system you can run in production.
Services
What AI development services does Xcrino offer?
| Service | Best for |
|---|---|
| AI Consulting & Strategy | Choosing the right use cases and a realistic roadmap |
| AI Agent Development | Automating multi-step work across your tools |
| Generative AI & LLM Applications | Assistants, content, and language-driven features |
| RAG & Enterprise Knowledge Systems | Accurate answers from your own documents and data |
| Machine Learning & Predictive Analytics | Forecasting, scoring, and anomaly detection |
| Computer Vision | Inspection, recognition, and document or image analysis |
| NLP & Document Intelligence | Extracting and routing information from text |
| AI Chatbots & Conversational AI | Support and internal assistants |
| AI Integration into ERP & Software | Adding intelligence to systems you already run |
| MLOps & LLMOps | Deployment, monitoring, and cost control |
AI Agent Development
Our AI agent development services create agents that plan and complete multi-step tasks across your CRM, ERP, ticketing, and communication tools. We design tool access, permissions, memory, approvals, and fallbacks, so agents act inside clear boundaries.
Generative AI and LLM Application Development
We build generative AI applications and LLM integration for assistants, drafting, summarization, classification, and workflow automation, using the model type that fits your accuracy, cost, latency, and privacy needs.
RAG and Enterprise Knowledge Systems
RAG (retrieval-augmented generation) grounds answers in your documents, databases, and policies, with source citations and access controls, so employees and customers get answers they can check.
Machine Learning and Predictive Analytics
Machine learning development for demand and sales forecasting, churn and risk scoring, predictive maintenance, and anomaly detection, trained on your operational data and connected to your dashboards.
Computer Vision
Defect and quality inspection, object and text recognition, document and image analysis, and visual monitoring.
NLP and Document Intelligence
Extraction, classification, translation, and routing of contracts, invoices, emails, and forms, including multilingual documents.
AI Chatbots and Conversational AI
AI chatbot development for customer support, onboarding, and internal help desks, with human handover, conversation analytics, and clear AI disclosure.
AI Integration into ERP and Custom Software
We add forecasting, anomaly detection, intelligent search, document processing, and copilots to your ERP, CRM, and enterprise applications, through APIs and secure data access, without ripping out what already works.
MLOps and LLMOps
Versioning, deployment pipelines, monitoring, drift detection, evaluation, prompt and model management, and cost and latency control, so AI stays reliable and affordable after launch.
Choosing the right AI approach
RAG, Fine-Tuning, Prompting, or a Custom Model? How to Decide
| Approach | Choose it when... | Trade-offs |
|---|---|---|
| Prompting and workflow design | Choose it when...: A strong general model can already do the task with good instructions | Trade-offs: Limited control over style and domain depth |
| RAG | Choose it when...: Answers must come from your changing documents and data, with citations | Trade-offs: Quality depends on retrieval and data hygiene |
| Fine-tuning | Choose it when...: You need consistent format, tone, or domain behavior at scale | Trade-offs: Needs quality training data and re-tuning over time |
| Custom ML model | Choose it when...: You have structured data and a prediction task (forecasting, scoring, vision) | Trade-offs: Needs labeled data and ongoing monitoring |
| Agentic workflow | Choose it when...: The task involves several steps, tools, and decisions | Trade-offs: Needs strong guardrails, permissions, and evaluation |
| Off-the-shelf AI tool | Choose it when...: The use case is standard and speed matters most | Trade-offs: Less customization and integration control |
Build vs buy in one line: buy when the use case is commodity and the tool fits your workflow. Build when your data, process, or integration needs are your advantage.
We recommend the approach after discovery and are model-agnostic by design, so you aren't locked into one provider.
Pilot to production
How does Xcrino take AI from proof of concept to production?
Pilots stall when success isn't defined and production isn't planned. Our path:
- 01
Use-case selection
pick a high-value, feasible problem with measurable KPIs.
- 02
Data readiness check
confirm the data exists, is accessible, and is good enough.
- 03
Proof of concept (PoC)
a short, scoped build with agreed success criteria.
- 04
Evaluation
test on real cases against a benchmark set before anyone relies on it.
- 05
Pilot
a limited rollout with human oversight and feedback.
- 06
Production hardening
security, scalability, monitoring, cost controls, and integration.
- 07
Operate and improve
track quality, drift, cost, and adoption, and iterate.
Evaluation, guardrails, and responsible AI
How does Xcrino test AI and control hallucinations?
"Responsible AI" has to be engineering practice, not a slogan. In every project we apply:
- Evaluation before launch: test sets built from real scenarios, accuracy and failure-mode analysis, and regression tests when prompts or models change
- Hallucination control: grounding in your data, citations, confidence handling, and "I don't know" behavior
- Guardrails: input and output filters, policy enforcement, scope limits, and approval steps for sensitive actions
- Human oversight: review queues and escalation paths where errors would be costly
- Adversarial testing: checks for misuse, prompt injection, and data leakage before launch, scaled to the risk of the use case
- Monitoring: quality, drift, latency, cost, and user feedback after launch
- Documentation: model and data records for auditability
We match the level of testing and oversight to the risk of each use case, and document what was tested.
Security and data privacy
How does Xcrino secure AI systems and protect your data?
- Data protection: encryption, role-based access, and data minimization
- LLM-specific threats: prompt injection, insecure tool use, sensitive-data leakage, and over-permissioned agents, addressed in design and testing
- Deployment options: cloud deployment by default, with private or dedicated environments scoped when your data rules require them
- Data handling: we agree in writing, before the build, which providers process your data, where it is stored, and whether any of it may be used for model training
- Ownership and confidentiality: confidentiality terms agreed before discovery, with ownership of code, prompts, pipelines, and fine-tuned assets defined in your contract
Compliance and regulation
How does the EU AI Act affect your AI project?
If you sell or operate in Europe, the EU AI Act affects what you build.
- High-risk obligations have been postponed, not cancelled. The Digital Omnibus on AI, in force since 27 July 2026, moved stand-alone high-risk system obligations (Annex III, covering areas such as hiring, credit scoring, and education) to 2 December 2027, and embedded-product obligations (Annex I) to 2 August 2028.
- Transparency rules still apply. Most Article 50 transparency obligations have applied since 2 August 2026, which matters for chatbots and AI-generated content. A short grace period applies to watermarking for synthetic content from systems already on the market, until 2 December 2026.
- How we help: use-case risk classification, AI disclosure and labeling in the product, documentation, and data governance, alongside GDPR and sector rules such as HIPAA where they apply.
Last reviewed: October 2026. This section is general information, not legal advice. Compliance depends on your use case and jurisdiction, so we recommend confirming with your legal counsel. Regulations change, and we verify current requirements at the start of every project.
The Xcrino difference
What makes Xcrino different as an AI development company?
Most AI boutiques stop at the model. Because we also build custom software, ERP, and enterprise applications, we build the whole system.
- AI agents that read from and write to your ERP and CRM
- Intelligent search and Q&A over your documents, tickets, and databases
- Forecasting and anomaly detection feeding the dashboards your teams already use
- Production engineering for APIs, security, scale, and monitoring from day one
- One accountable team across AI, back end, integration, and cloud
Use cases and industries
Which business functions can AI improve?
- Customer support: AI assistants, ticket triage, and answer drafting
- Operations: workflow automation, scheduling, and exception handling
- Finance: invoice and document processing, reconciliation, and anomaly detection
- Sales and CRM: lead scoring, summarization, and next-best-action
- HR: knowledge assistants and document handling (some HR uses are regulated as high-risk in the EU)
- IT and engineering: internal assistants and code or ticket analysis
- Supply chain: demand forecasting and inventory optimization
Which industries does Xcrino build AI solutions for?
- Manufacturing and Engineering: defect detection, predictive maintenance, and quality analytics
- Logistics and Transportation: route and demand forecasting, tracking intelligence, and document automation
- Retail and Distribution: demand forecasting, product assistants, and inventory intelligence
- Healthcare and Life Sciences: documentation assistance and workflow automation
- FinTech and Banking: document processing, risk scoring, and automation
- Education and EdTech: learning assistants and assessment support
- Real Estate: document analysis, lead handling, and property insights
- Startups and SaaS: AI features inside new or existing products
Tell us your sector and we will share the most relevant work we can discuss.
Technology
Which AI technologies does Xcrino use?
| Area | Technologies |
|---|---|
| Languages and ML | Technologies: Python, TensorFlow, PyTorch, scikit-learn |
| LLMs and generative AI | Technologies: OpenAI and other LLM APIs, open-source models, prompt engineering, fine-tuning |
| RAG and agents | Technologies: Vector databases, embedding pipelines, agent frameworks and orchestration |
| Data | Technologies: PostgreSQL, MySQL, MongoDB, data pipelines |
| Cloud and deployment | Technologies: AWS, Microsoft Azure, Google Cloud, Docker, Kubernetes, CI/CD |
| MLOps and LLMOps | Technologies: Experiment tracking, model registry, monitoring and evaluation tooling |
| Application layer | Technologies: Node.js, Python, .NET, Java, React, Next.js, REST APIs |
We select tools after discovery, based on accuracy, cost, security, and your team's capabilities.
Engagement models
Which engagement model fits your AI project?
Pick the model by how fixed your scope is and how long you need the team.
| Model | Best when | How it works |
|---|---|---|
| AI discovery and roadmap | Best when: You need to find and prioritize use cases | How it works: A short engagement ending in a scoped plan |
| Proof of concept | Best when: You want to validate value before committing | How it works: A time-boxed build against agreed success criteria |
| Fixed price project | Best when: Scope is clear and stable | How it works: Defined deliverables, milestones, and budget |
| Time and material | Best when: Scope will evolve | How it works: Pay for effort, with flexibility |
| Dedicated AI team | Best when: Long-term AI roadmap | How it works: AI/ML engineers working as your extension |
| Hire AI developers | Best when: You need specific skills fast | How it works: ML, LLM, and data engineers join your team |
| AI operations and support | Best when: You have AI in production | How it works: Monitoring, evaluation, and improvement retainers |
Cost and timeline
How much does AI development cost at Xcrino?
AI work at Xcrino starts from $3,000 (about ₹2.7 lakh) for a proof of concept and from $5,000 (about ₹4.5 lakh) for a first production release. A dedicated AI/ML developer starts from $1,250 (about ₹1.13 lakh) a month. You get a fixed quote after a free consultation.
| Engagement | Starting price | Typical length | Best for |
|---|---|---|---|
| AI proof of concept | Starting price: From $3,000 (≈ ₹2.7 lakh) | Typical length: 2–4 weeks | Best for: Testing whether AI can solve one problem with your real data |
| AI MVP / first production release | Starting price: From $5,000 (≈ ₹4.5 lakh) | Typical length: 1–2 months | Best for: Putting one proven use case into daily use |
| Dedicated AI/ML developer | Starting price: From $1,250/month (≈ ₹1.13 lakh) | Typical length: Monthly, full time | Best for: Ongoing AI work inside your own team |
Prices in INR are converted at ₹90 per US dollar. Source code and IP transfer to you on full payment, and we sign an NDA whenever you ask.
What changes the price?
Cost depends on factors you can influence:
- Type of solution: assistant, agent, ML model, vision system, or integration into existing software
- Data readiness: quality, volume, labeling, and cleaning needs
- Model strategy: hosted API, fine-tuned, or self-hosted, which affects both build and running cost
- Integrations: ERP, CRM, and third-party systems
- Accuracy and risk level: higher stakes need more evaluation, oversight, and documentation
- Scale and latency: users, volume, and response-time requirements
- Security and compliance: private deployment, audits, and regulatory requirements
- Operations: monitoring, evaluation, and ongoing model and prompt maintenance
Plan for running costs too. Many AI systems carry ongoing model usage, infrastructure, and monitoring costs. We design with cost controls from the start, such as caching, routing simple requests to cheaper models, and usage limits.
How scope changes budget and timeline:
- AI discovery and roadmap: a short engagement that ends in ranked use cases and a scoped plan
- Proof of concept: a time-boxed build measured in weeks, tested against agreed success criteria
- Production AI application or agent: integration, evaluation, security, and monitoring added to a proven concept, delivered over months
- Enterprise AI platform or multi-system integration: several use cases and systems, delivered in phases
After a free consultation, we provide a scoped estimate with assumptions, risks, and expected running costs.
Why Xcrino
Why choose Xcrino for AI development?
- Experience: 560 clients served since 2016
- Expertise: 69+ developers including AI/ML engineers, data engineers, architects, and QA
- Independent profiles: company profiles on Clutch and TopDevelopers that you can check before you talk to us
- Trust: 94% project success rate, transparent milestones, evaluation-first delivery, contract-defined confidentiality and IP terms, and verifiable company details
What clients say
"Their proactive approach and technical proficiency ensured a smooth and successful project delivery. Truly a reliable partner for IT needs."
"From start to finish, they were responsive and delivered outstanding results."
Choosing a partner
What should you ask an AI development company?
- Can you show AI projects in production, with measurable results, not only demos?
- How do you evaluate accuracy and handle hallucinations before launch?
- Which models and providers do you use, and can I avoid lock-in?
- How is my data handled, and is it ever used for training?
- How do you secure agents and protect against prompt injection?
- What will this cost to run after launch, not only to build?
- How do you address regulation such as the EU AI Act and GDPR?
- Who owns the code, prompts, pipelines, and fine-tuned assets?
Fit check
Is AI the right solution for your problem?
A good fit when:
the task is repetitive and heavy on language or data, you have the data the system needs, errors can be caught by a person or a check, and success can be measured.
Not the best fit when:
a simple rule or standard automation would solve it, the data does not exist or cannot be accessed, or a wrong answer would be costly with no way to review it. We will tell you if AI is not the best answer.
Terms explained
AI development terms explained
- LLM (large language model)
- a model trained on large amounts of text that can read and write language, such as the models behind modern chat assistants.
- RAG (retrieval-augmented generation)
- a method that lets a model answer from your own documents and data, usually with citations.
- AI agent
- software that uses an LLM to plan steps and call tools to complete a task.
- Fine-tuning
- further training of a model on your examples, so it follows a specific format, tone, or domain.
- Hallucination
- a confident but incorrect answer from a model, controlled through grounding, evaluation, and human review.
- Guardrails
- checks on what goes into and comes out of a model, so it stays within agreed limits.
Get a quote
Tell us what you want to build
Share the problem, the systems involved and your timeline. We reply with next steps and, where it fits, a fixed-price first phase.
Reach us
New Delhi, India
FAQ
AI development: questions answered
What are AI development services?
AI development services cover the strategy, data preparation, engineering, testing, deployment, and monitoring of AI systems such as assistants, agents, machine learning models, and computer vision solutions. They help businesses automate work, improve decisions, and add intelligence to products and software.
What is an AI agent, and can you build one for my business?
An AI agent is software that uses a large language model to plan and complete multi-step tasks, often by using your tools and data. We design agents for support, operations, knowledge work, and workflow automation, with permissions, approvals, evaluation, and monitoring.
What is RAG, and when should I use it?
RAG (retrieval-augmented generation) lets an AI model answer using your own documents and data, usually with citations. It suits knowledge assistants, support, and policy or product Q&A where answers must reflect current, company-specific information.
Should I use RAG or fine-tuning?
Use RAG when answers must come from changing documents. Use fine-tuning when you need consistent style, format, or domain behavior. Many solutions combine them. We recommend an approach after discovery and testing.
How much does AI development cost?
AI development at Xcrino starts from $3,000 (about ₹2.7 lakh) for a proof of concept and from $5,000 (about ₹4.5 lakh) for a first production release (MVP). A dedicated AI/ML developer starts from $1,250 (about ₹1.13 lakh) a month. The final price depends on the type of solution, how ready your data is, the systems it must connect to, and the accuracy you need. You get a fixed quote after a free consultation, including expected running costs.
How long does it take to build an AI solution?
An AI proof of concept usually takes 2–4 weeks, and a first production release (MVP) 1–2 months. Larger applications with several integrations, evaluation and monitoring take longer. We confirm a realistic timeline after discovery.
Do you offer an AI proof of concept?
Yes. An AI proof of concept at Xcrino starts from $3,000 (about ₹2.7 lakh) and usually takes 2–4 weeks. It tests one use case on your real data against agreed success criteria, so you see results before committing to a full build.
Can you add AI to our existing ERP or software?
Yes. We add AI features such as assistants, forecasting, document processing, and automation to existing systems through APIs, without requiring a full rebuild.
How do you prevent AI hallucinations and errors?
We ground answers in your data, build evaluation sets from real cases, add guardrails and human review for sensitive actions, and monitor quality after launch. No AI system is error-free, so we design for detection and escalation.
Is my data used to train AI models?
Before any build, we agree in writing which providers process your data, where it is stored, and whether it may be used for training. If your rules require it, we scope a private or dedicated deployment.
Can you deploy AI privately or on-premise?
Cloud deployment is the default. Private or dedicated environments are scoped case by case, based on your data rules and the type of model the use case needs.
Do you help with EU AI Act and GDPR compliance?
We design AI systems with risk classification, transparency, documentation, and data governance in mind. The EU AI Act is phasing in, with some high-risk deadlines postponed to 2027 and 2028 while transparency obligations apply earlier. We are not a law firm, so we recommend confirming legal requirements with your counsel.
Can you build multilingual AI for French, Japanese, or Arabic users?
Yes. We test multilingual quality on real content from each language and market.
Who owns the code, prompts, and trained assets?
You do. All source code, prompts, trained models and documentation transfer to you once the project is paid in full, and are handed over at the end of the project. Xcrino signs an NDA whenever you ask for one, and confidentiality is also part of every engagement contract.
What support do you provide after launch?
We provide monitoring, evaluation, model and prompt updates, security patches, cost optimization, and feature development.
Can I hire AI developers from Xcrino?
Yes. You can hire AI/ML engineers, LLM engineers and data engineers individually or as a dedicated team. A dedicated AI/ML developer starts from $1,250 (about ₹1.13 lakh) a month.
Let's Find Where AI Can Work for Your Business
Tell us the problem you want to solve, the systems you run, and the constraints that matter. We'll recommend the right AI approach, outline a proof of concept with clear success criteria, and give you a realistic estimate, and we'll say so if AI isn't the best answer.
Company details
Xcrino Business Solutions
AI Development • AI Agents • Generative AI • RAG • Machine Learning • Computer Vision • AI for ERP & Enterprise Software
New Delhi, India | Serving Clients Worldwide
E-523, Top Floor, Sector 7, Dwarka, New Delhi 110075 • phone and email as shown on the Contact page