ZeroOne D.O.T.S AI ZeroOne D.O.T.S AI
Selected outcomes

Proof over pitch every time.

These are representative examples of the kind of work DotsAI is built for: private AI systems, document-heavy automation, and operational tooling where the outcome matters more than the trend language.

// case study 01

Logistics operation · private LLM deployment

A logistics workflow was relying on a patchwork of external tools for routine document and query handling. The replacement system consolidated the work into a private stack with internal retrieval and task-specific flows.

  • Outcome: 70% cost reduction versus the previous tool mix
  • Why it worked: fewer vendors, lower recurring cost, better control over internal context
  • Best lesson: private AI becomes compelling when the workflow is repeated daily and the context is business-specific

// case study 02

NBFC workflow · AI document pipeline

A document-heavy financial process needed faster extraction and routing without collapsing quality. The solution combined OCR, document parsing, and domain-tuned validation logic.

  • Outcome: 99% accuracy on the tuned document pipeline
  • Why it worked: the system was built around document shape, not generic prompting alone
  • Best lesson: AI performance improves sharply when workflow rules are encoded instead of left implicit

// case study 03

Compliance team · AI audit system

A manual review process was expensive and hard to scale. The replacement system reduced repetitive review load and gave operators a clearer way to inspect exceptions rather than reading everything from scratch.

  • Outcome: roughly ₹8L annual saving versus the manual-heavy path
  • Why it worked: automation focused on triage and scoring, not replacing every final decision blindly
  • Best lesson: the best AI systems reduce operator drag without removing audit visibility