AI & Software Development
Case Studies

THE TISA builds AI agents, retrieval systems and workflow automation for teams that need the system to survive real production traffic. Across recent projects we have cut manual review time for fintech document workflows, deflected support volume for SaaS products, and reduced route-planning effort for logistics operators. Every published study names the baseline, the architecture and the outcome the client measured after launch — not a recycled vanity metric. Timelines are agreed at discovery; every build ships with evaluation harnesses, hallucination testing and monitoring. Explore live write-ups such as OpsPilot, EZSKU and Narsik Logistics, or book a feasibility assessment.

We agree on the baseline before we write code. During discovery we measure the current process — time per task, cost per task, error rate and volume — from the client's own systems. After deployment we measure the same things the same way. Every number on a published case study came from those systems. We do not count model accuracy on a benchmark as a business result: a model that scores well in a lab but fails on real tickets is not a win. That measurement discipline is part of how we integrate AI into your stack and our full development process.

A good AI case study names the baseline, the architecture, the timeline and the measured outcome. It also names what did not work. Vague claims and identical metrics across cards give buyers nothing to evaluate. On this page we only publish builds that reached production and that the client approved. Browse the grid above, or start with our AI agent development services if you want the same delivery shape for your process.

Yes. Every metric on a published study comes from the client's own systems, and the client approves the case study before we publish it. We anonymize company names when asked. We do not invent percentages or reuse one headline number across projects. If a figure cannot be defended on a sales call, we cut it and describe the outcome in plain language instead. See how we integrate AI into your stack for how measurement ties into delivery.

Most projects run from discovery to first production deployment on a timeline agreed at kickoff. A narrow pilot with clean data and a clear workflow ships faster than a multi-system agent with compliance gates. Scope, data readiness and escalation design matter more than model choice alone. We map that path in a feasibility assessment before we write code. Read the steps on our full development process, or dig into AI agent development for agent-specific delivery.

Yes. Most of our clients start with no in-house AI team. We run a feasibility assessment first and tell you plainly if a rules-based system would solve the problem cheaper. When AI is the right tool, we set a baseline, ship a pilot on real data, then move to production with evaluation and monitoring. Teams building product features often start with generative AI and RAG builds or AI for SaaS companies.

You own both. We hand over the repository, the documentation and the runbooks. We do not train public models on your proprietary data without explicit approval, and we do not lock you into one model provider. Ownership and privacy terms are written into the engagement — see our privacy policy for how we handle data, and about THE TISA for how we work with US teams from Jaipur.

Tell us the industry and the process you want to automate. We will send the closest match, including projects we have not published publicly when the client allows a private walkthrough. Published work spans fintech, ecommerce, logistics, EdTech and SaaS — see OpsPilot, EZSKU, Seed2Sell and the full grid on this page. For industry-shaped delivery, also see AI workflow automation and RAG development services.

Fintech — risk analysis, document review and compliance automation (see OpsPilot and DCD). Logistics — route optimization and fleet control (Narsik Logistics). SaaS — inventory and B2B ordering (EZSKU). Ecommerce and marketplaces (Seed2Sell). EdTech (Campus Flow, TISA-TECH). Browse the portfolio grid on this page, then open the matching study.

Book a 30-minute feasibility assessment. We look at your process, tell you whether AI fits and give you a rough timeline and cost range — no deck, no pitch. Get in touch to schedule, or explore AI agent development if you already know you need an agent. Jaipur-based team serving US clients across US time zones.