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.

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 — using the client's own systems. After deployment we measure the same things the same way. That is how a case study stays honest: the before and after use one definition of success. Book a feasibility assessment if you want that baseline set for your workflow.

Every production build ships with evaluation harnesses, hallucination testing and monitoring. We define an acceptance threshold on a real eval set before go-live, not after a demo. Escalation paths catch low-confidence answers so the model does not invent facts in front of customers. That pattern shows up across our RAG and AI agent engagements.

After launch we watch accuracy, latency, cost, deflection or throughput against the agreed baseline, and failure modes that need human escalation. Prompt and model versions are tracked so a regression is reversible. Handover includes runbooks so your team can operate the system without us sitting in the loop. Ongoing support options are covered on AI integration services.

We treat underperformance as an engineering problem, not a surprise. We isolate whether the issue is retrieval, prompts, model choice, data drift or an edge-case workflow. Fixes can mean a prompt change, a stronger eval gate, a different model, or a redesigned escalation path. We pick models per project and do not lock you into one provider — details live in generative AI development and our process.

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 (CampusFlow, TISA-TECH). Browse the portfolio grid on this page, then open the matching study.

Use the case study grid on this page for live product write-ups, then filter by the problem you care about. By service: AI agents, generative AI and RAG, AI integration and workflow automation. By industry: fintech, logistics, SaaS and ecommerce — start from the studies above or get in touch for a private match we have not published yet.

The hub covers production AI agents, generative AI and RAG systems, AI integration into existing stacks, and workflow automation. Individual studies show the problem, the architecture, the stack and what changed after launch. Deep service pages: AI agent development, generative AI development, RAG development, AI integration services and AI workflow automation.

Every card and full write-up is built to be comparable: client name or approved anonymized descriptor, industry tag, challenge in plain language, one headline outcome with a unit when the client approved it, and a link to the full breakdown. The full study covers business context, the old workflow, what we built, model selection, evaluation, rollout, results and what did not work. Placeholder marketing copy and identical metrics across cards are not used. We publish a case study only after the client approves the numbers — more builds stay in review until then.

A featured study leads with industry and service tags, the problem in one sentence, the measurable outcomes the client approved, the stack in plain text and a link to the full breakdown. Example shape: fintech document review with a RAG pipeline and human-in-the-loop escalation — problem, architecture, timeline and result measured the same way before and after. Stack examples often include Python, LangChain, Pinecone and AWS. Start from the portfolio above or book a feasibility assessment to walk a private featured build.

We group by function, not by logo wall.

• Models: GPT-4 class, Claude, and open-weight models where cost or data residency demands it
• Orchestration: LangChain, LangGraph and custom agent loops
• Retrieval: Pinecone, pgvector and hybrid search
• Infrastructure: AWS Lambda, FastAPI, PostgreSQL and Docker
• Observability: evaluation harnesses, prompt versioning and production monitors

We pick models per project. We do not lock you into one provider and we hand over the code. See AI application development for how that stack lands in a product.

• Feasibility assessment — we tell you if AI is the wrong tool
• Discovery and baseline — we measure the current process
• Pilot — narrow scope, real data, real users
• Production build — evaluation, monitoring, escalation paths
• Handover — code, documentation, runbooks

See our full AI development process and about THE TISA for the longer version.

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.