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:
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
Scaling Up: How Enterprise AI Drives Business Growth
According to Gartner's eye-opening estimate, artificial intelligence produced $2.9 trillion in commercial value and freed up 6.2 billion labor hours in 2021 alone. This information should serve as a wake-up call for individuals who are still unaware of the...
RFQ Data Extraction: Why OCR and Generic AI Fall Short on Engineering Drawings
A drawing lands in an RFQ inbox. Someone runs it through an OCR tool, or pastes it into a generic AI tool and asks for the dimensions. The answer looks reasonable. It's often wrong in ways nobody catches until the quote is already out. Neither tool is broken. OCR is...
Quote Turnaround Time: Why Slower Quotes Are Costing Manufacturers the Job, Not Just the Hours
Quote turnaround time gets blamed on pricing. In most shops, that's not where the delay actually comes from. The RFQ usually waits on the experienced estimators who can read the print correctly. Tolerances need checking. A casting might only have a 2D drawing to work...
Quoting Software for CNC Machining: Why the Estimate Still Breaks on Complex Jobs
A shop wins an RFQ for a part that starts as a permanent mold casting. It gets machined next, then moves to assembly. The quoting software prices the machining step well. It has no framework for the casting cost or the assembly labor. Another shop gets a different...