Enterprise AI Development
Our enterprise AI development solutions
Reimagine Legal Support Driven by in-Depth Legal Research
- Legal Chatbot Assistant
- Improved Communication Efficiency
- Research Time Reduction by 64%
Reduced Inspection Times for Property Inspectors
- Deep learning and computer vision driven image data extraction
- GPT-based NLP chatbot for enhanced customer experience
- Improved work efficiency by 80%
- Image classification for detecting anomalies
Helped Trapeze Group, Revolutionize Mobility with a Paratransit Solution
- Real-time vehicle tracking
- Advanced algorithms for efficient route planning
- In-app communication interfaces
- Strict adherence to accessibility and privacy laws
Redefining Restaurant Ordering with a Voice Ordering Solution
- State-of-the-art voice recognition
- Provides natural dialogues and verbal responses
- Multi language support for diverse customers
- Dynamic interaction for enhanced engagement
Leading brands we’ve worked with
Our enterprise AI development process
Evaluation and Idea Validation
Exploration
Pilot Project
Development
Deployment
Evaluation and Idea Validation
Exploration
Pilot Project
Development
Deploying the Model
- Settling on the perfect framework to gift-wrap the model as an API service.
- Or taking a different route – choosing and finetuning a container service for deployment.
- Crafting a safe, production-ready home for the models.
- Building a model registry – a logbook to store all metadata that matters for each model.
Deployment
Our enterprise AI development tech stack
Why choose us for enterprise AI development
What our clients say
See how businesses are using our AI solutions to make better decisions with measurable impact.
About enterprise AI development
How will AI integrate with our existing Enterprise systems and workflows?
What approach do you use for model validation, testing, and performance evaluation?
What regulatory and compliance considerations do you address for Enterprise AI projects?
Can you provide an estimate of the total costs involved in implementing and maintaining the Enterprise AI solution?
What is the expected Return on Investment (ROI) of implementing AI in Enterpise systems?
What is the expected timeline for Enterprise AI development and deployment?
Are there scalability options to accommodate future business growth or changing requirements?
Point of view
Windows and Doors Takeoff: How AI Changes the Process
A window and door takeoff starts with a drawing set, but the information needed for it rarely sits in one place. Window marks may appear on elevations, while sizes and types appear in schedules. Plans can show locations, dimensions, and other details that affect the...
CAD to BOM Matching: New CAD Files to Existing Part Library
A new customer CAD file does not always mean a new part. Manufacturers often receive CAD files for components that may already exist in their part library. The challenge is finding that connection. A new file may use a different name, contain limited metadata, or look...
Material Takeoff: What It Is, How It Works, and How AI Can Help
A material takeoff identifies the materials needed for a construction project and determines the quantity of each item. Estimators use this information to prepare accurate cost estimates, bids, and procurement plans. The process starts with construction drawings,...
Data Extraction From Technical Drawings: Manual to AI-Assisted Workflows
Technical drawings already contain the information engineering teams need. Dimensions, tolerances, materials, notes, BOM details, and other requirements are already there. So why do teams still spend time reading drawings and entering that information into...



