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Case study

AI-Assisted Operations Platform

Document intake and support triage moved to a draft-first AI workflow with human approval, measured against an evaluation set built from two years of historical cases.

Client type
B2B SaaS company with a large support operation (sample scenario)
Industry
SaaS
Duration
7 months

Sample contentSample engagement. Illustrative scenario used while the site is in build — not a named client project.

saas2026

AI-Assisted Operations Platform

  • Python
  • TypeScript
  • LLM integrations
  • RAG

01Challenge

A support and operations team spent most of its capacity on classification, data extraction from customer documents, and drafting routine responses. Hiring scaled the cost linearly with volume and the backlog still grew each quarter.

02Solution

A retrieval-grounded assistant that classifies incoming work, extracts structured data against a schema, and drafts responses for human approval — with confidence thresholds routing anything ambiguous straight to a person.

03Business impact

Operations capacity was redirected from repetitive processing to exception handling and customer work, and the team gained a measurable quality baseline to make further automation decisions against.

04Approach

  1. 01Volume and effort analysis to identify the three highest-cost repetitive tasks
  2. 02Evaluation set assembled from two years of historical, already-resolved cases
  3. 03Retrieval pipeline grounded in the company's own documentation and past resolutions
  4. 04Structured outputs validated against a schema before reaching the queue
  5. 05Confidence thresholds and mandatory human approval on anything with financial impact

Draft-first, not decision-first

The system never resolves a case autonomously where money or a contractual commitment is involved. It prepares the work; a person approves it. That boundary was what made the rollout acceptable to both the operations team and the compliance function.

Evaluation before deployment

Nothing shipped until it could be scored against real historical cases. The evaluation harness runs continuously, so a model or prompt change that degrades quality is visible before customers experience it.

Services involved

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