AI implementation services in weeks, not months
Production-ready AI systems deployed in 3 to 8 weeks. Senior AI engineers deliver measurable results, not consulting decks.
- Weeks to production
- 3 to 8 wks
- Implementations delivered
- 200+
- Working software
- Weekly
- Timeline agreed in writing
- Fixed
The short answer
What are AI implementation services?
See our manufacturing AI case study for a real deployment.
Timeline
How long does AI deployment take?
Most production AI deployments take 3 to 8 weeks from kickoff. A focused automation can go live in about 3 weeks. A custom machine learning or LLM system with several integrations sits at the longer end. We fix the timeline in writing, with milestones, before the build starts.
| Phase | Typical duration | What you get |
|---|---|---|
| Discovery and readiness assessment | 3 to 5 days | The highest-ROI use case, data gaps, and risks |
| Architecture and roadmap | 1 week | A fixed scope, timeline, and price |
| Build and iterate | 2 to 6 weeks | Working software every week |
| Deploy and optimise | 1 to 2 weeks | Production system, monitoring, and runbooks |
Last updated: 29 September 2026
Timeline risks
What makes an AI deployment take longer?
- Data readiness. Scattered or unlabelled data adds data engineering time before modelling starts.
- Integrations. Each ERP, CRM, or legacy system the AI must read from or write to adds work.
- Security and compliance review. Regulated sectors often need extra review before go-live.
- Scope changes. New requirements mid-build extend the plan, so we agree scope up front.
Services
AI implementation services
From strategy to production deployment, every service is scoped for speed and measurable outcomes.
AI strategy and implementation
Turn your AI plans into production-ready systems in weeks.
- Rapid AI readiness assessment
- Custom AI roadmap and architecture
- Production deployment
- ROI-focused outcomes
ML model development and deployment
Build and deploy machine learning models at enterprise scale.
- Predictive analytics
- NLP and document processing
- Computer vision
- Real-time ML pipelines
Generative AI integration
Use GPT, Claude, and Llama for business automation.
- Custom LLM fine-tuning
- RAG systems
- AI chatbots and assistants
- Document generation
AI system optimization
Improve existing AI systems for better performance and lower cost.
- Model performance tuning
- Cost reduction architecture
- Scalability improvements
- Continuous monitoring
Data pipeline engineering
Build the data infrastructure your AI systems need to succeed.
- ETL and ELT pipelines
- Vector database setup
- Real-time data streams
- Data quality automation
AI readiness assessment
Understand exactly where AI can move the needle in your business.
- Use case discovery workshop
- Data readiness audit
- Build vs. buy analysis
- 90-day implementation roadmap
Case studies
Real implementations, real results
Concrete case studies we've shipped, not whitepaper concepts.
Insurance statement automation
Insurance
OCR and NLP with automated CRM updates for a UK brokerage handling 500+ statements a day: processing cut from 4 hours to 15 minutes per batch and 95% less manual data entry.
Heart disease risk prediction
Healthcare
A predictive model for a regional health network: 85% accuracy predicting heart disease risk up to 30 days ahead and 20% fewer cardiac readmissions.
Demand forecasting
Manufacturing
AI-driven demand forecasting for a manufacturer: 88% prediction accuracy in targeted demand scenarios, 40% lower safety stock and 35% lower inventory carrying costs.
Process
Our implementation process
From discovery to production in 3 to 8 weeks, with working software at every stage.
- 013 to 5 days
AI readiness assessment
We audit your data, systems, and workflows to identify the highest-ROI AI opportunities.
- 021 week
Architecture and roadmap
Custom AI architecture design with a phased implementation plan and fixed timeline.
- 032 to 6 weeks
Build and iterate
Agile sprints with weekly working software, not presentations. You see progress every week.
- 041 to 2 weeks
Deploy and optimize
Production deployment with monitoring, optimization, and ongoing support included.
Why Agility
Why companies choose Agility
Senior engineers only
No junior developers. Direct access to architects with 10+ years of AI and ML implementation experience.
Weekly working software
See progress every week with working prototypes, not slide decks. Agile sprints with continuous delivery.
Fixed timeline commitments
Timelines agreed in writing before the build, with milestones and transparent project tracking.
Production-ready code
Scalable, secure code ready for production from day one. No throwaway prototypes.
Full-stack expertise
End-to-end implementation from data pipelines to frontend interfaces, with no dependency on third parties.
Global delivery
Teams across the USA, India, and Israel. We work in your time zone with real-time collaboration.
Technology
Our technology stack
We work with every major AI platform and framework: model-agnostic and infrastructure-flexible.
LLMs
OpenAI GPT-4o, Claude 3.5 Sonnet, Gemini Pro, Llama 3, Mistral
Frameworks
LangChain, LangGraph, LlamaIndex, Haystack
ML platforms
TensorFlow, PyTorch, scikit-learn, Hugging Face, Keras
Vector databases
Pinecone, Weaviate, Chroma, pgvector, Qdrant
Cloud AI
Azure OpenAI, AWS Bedrock, Google Vertex AI, AWS SageMaker
MLOps
Kubernetes, Docker, MLflow, Kubeflow, Weights & Biases
FAQ
AI implementation services: common questions
What teams ask before starting an AI implementation project.
How much does an AI implementation project cost?
Most production AI implementations land between $25,000 and $150,000 depending on scope, data readiness, and integration complexity. We scope every engagement to a fixed price with defined milestones before any code is written, so you know the full cost up front, no open-ended retainers. Book a free assessment and we will give you a concrete estimate for your use case.
How long does it take to implement AI?
Most production AI ships in 3 to 8 weeks from kickoff: a focused automation in about 3 to 6 weeks, and data engineering or a custom ML or LLM system in 4 to 8 weeks. Complex integrations or compliance reviews can extend that, which is why we fix the timeline in writing before the build. Speed comes from senior engineers working directly on your project in weekly delivery sprints instead of months of discovery decks.
What do we actually receive at the end of the project?
You get a deployed, production-grade AI system running in your environment, the full source code and infrastructure-as-code, an evaluation framework that measures accuracy and ROI, monitoring dashboards, and runbooks so your team can operate it. We hand over working software you own, not a strategy document.
Do we need clean data or an in-house data science team first?
No. A typical engagement starts with a short data and readiness assessment, and our data engineers build the pipelines, cleaning, and infrastructure needed as part of delivery. You do not need an internal ML team. Our senior engineers cover model development, data engineering, and MLOps end to end.
How is Agility different from a traditional AI consulting firm?
We deploy senior AI engineers directly onto your project with no layers of account managers or junior staff, and we ship production code in weeks instead of slide decks. Pricing is fixed-scope, delivery is milestone-based, and every sprint produces working software you can test.
What happens after the AI system goes live?
We provide post-deployment support including monitoring, model retraining, optimization, and issue resolution. AI systems drift as data changes, so we track production accuracy and proactively retrain and tune models to keep results reliable after launch.
Ready to implement AI in weeks?
Get a free AI readiness assessment and custom implementation roadmap. See exactly how we'll deliver your AI system in 3 to 8 weeks.
Or call us at +91 70414 02034