ForgePoint AI

Advance Your
Business With AI.

Practical  ·  Cost-efficient  ·  Built for impact

ForgePoint solves technology, process and business problems by putting AI to work through faculty-mentored engineering student teams. You get working proofs of concept, not slide decks, at a fraction of outsourcing cost, while your own team stays focused on Business As Usual (delivery/Ops).

Empower India's future engineers. Help build a Viksit Bharat.
Let's Explore → See How It Works
Working AI PoCs◆ Parallel Teams◆ Faculty-Mentored◆ Manufacturing◆ IT & Software◆ Robotics & Industrial Automation◆ Viksit Bharat◆ Working AI PoCs◆ Parallel Teams◆ Faculty-Mentored◆ Manufacturing◆ IT & Software◆ Robotics & Industrial Automation◆ Viksit Bharat◆
For Business & Technology Leaders

Explore AI in Parallel.
Decide on Evidence.

Advancing AI opportunities needs capacity that is usually committed to customer delivery and billable work. ForgePoint offers an economical way to explore multiple AI/ML approaches in parallel. Your teams review working PoCs, compare trade-offs and choose which directions to develop or pilot, focusing scarce design and engineering capacity where it creates the most customer value.

Working PoCson your data
Evidence, not slideware. See what actually works on your problem before you commit budget.
Several AI/ML approaches,one sprint
Parallel teams try different models and architectures. Compare the trade-offs side by side and back the one that works.
No pull onbillable capacity
We run the teams, the schedule and the governance. Your engineers stay on customer delivery; your involvement is a few hours.
A fraction ofthe cost
Far less than outsourcing the exploration or hiring an AI team to do it, so testing an idea is a prudent bet.

At the same time, you're helping build India's next generation of engineers: your AI challenges become real industry projects for faculty-mentored student teams.

AI in Practice

Where AI Earns
Its Place.

AI-first, not AI-forced. Three places it most often pays off. Illustrative, not exhaustive.

◎
Quality & Inspection
Computer Vision
Skilled inspectors are scarce, inconsistent, and a single point of failure.
Vision-based inspection holds quality steady regardless of who is on shift, and flags defects before they reach the customer.
→ Defect detection · Visual inspection · Process monitoring
◈
Prediction & Forecasting
Predictive Analytics
Machines fail without warning. Demand swings catch you short.
Patterns in sensor, sales and operational data reveal failures and demand shifts early, so you act before the problem lands.
→ Predictive maintenance · Demand forecasting · Anomaly detection
◇
Automation & AI Agents
Generative AI & Automation
Skilled people lose hours to documents, data entry, reconciliation and reporting.
AI agents and workflow automation read, reconcile and route the routine work, freeing people for the judgement calls.
→ Document processing · Reconciliation · Internal copilots
How It Works

Curate. Design.
Deliver.

Curate

Shape the problem and match the right faculty-led teams. 1–2 weeks

Design

Teams explore AI/ML approaches in parallel. 2–4 weeks

Prototype

Working PoCs, demonstrated at Demo Day. 6–16 weeks

Pilot

We run the pilot and hand it over to your team.

Your IP stays yours. Anything specific to your problem belongs to you, and everyone on the engagement signs NDAs and IP undertakings before day one.

Full details →

Have an AI opportunity
worth exploring?

Working PoCs · Parallel teams · Faculty-mentored engineering teams · Your IP stays yours

Let's Explore →