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Kramiva
03 — AI-enabled operations

Backline

A study of AI operations done responsibly: agents that triage, draft, and reconcile — with a human approving every irreversible step, and an audit trail explaining every decision.

Self-Initiated Product Study
Category
AI-enabled operations
Discipline
AI and automation
Demo
In development
Related service
AI Automation and Assistants
Fig. 03 — Backline interface, running on sample data

01

Overview

What Backline is

Backline is a Kramiva self-initiated product study demonstrating AI-assisted operations for support and back-office teams. It explores request triage, drafted responses grounded in resolved history, cross-system reconciliation, and a human approval gate on every irreversible action. It is a self-initiated study, not commissioned client work.

The problem
An operations team loses hours a day to triage, data entry, and reconciliation across an inbox, a ticket queue, and three systems that disagree.
Intended users
Operations, support, and RevOps leaders at companies between 20 and 500 people.
Status
Backline is a working study rather than a shipping product. A sandboxed public demo with synthetic data is in development; no client data appears in it at any point.

02

Product approach

What we decided, and what we cut.

01

Automate the judgement, not the commitment

The reliable win is not letting a model act — it is letting it prepare. Backline drafts responses, proposes classifications, and reconciles discrepancies, then presents them for a one-click human decision. Throughput improves; the failure mode stays recoverable.

02

Measure the process before automating it

The study starts with an instrumented baseline of where time actually goes, because most automation projects optimise the step that was easiest to see rather than the step that was most expensive.

03

Deliberately left out

No autonomous agent with write access to production systems, no unattended email sending, no 'confidence score' presented as if it were an accuracy guarantee.

03

Inside the build

The experience, and the engineering.

The main experience, step by step
  1. 01IngestRequests arrive from email, a form, or a ticket queue into one stream.
  2. 02ClassifyAn agent proposes a category, a priority, and an owner, with its reasoning attached.
  3. 03DraftA response is drafted from prior resolutions and the internal knowledge base, with citations.
  4. 04ReconcileRelated records across systems are compared and discrepancies surfaced.
  5. 05ApproveA human reviews, edits, and commits. Nothing external happens without this step.
  6. 06LearnEdits are captured as evaluation data, so quality is tracked rather than assumed.
Feature set
  • Unified request inbox across email, forms, and tickets
  • Triage agent with visible reasoning and confidence bounds
  • Draft responses grounded in resolved history, with source citations
  • Cross-system reconciliation with a discrepancy queue
  • Human approval gate on every irreversible action
  • Full audit log: what was proposed, by what, on what basis, approved by whom
  • Evaluation dashboard tracking accept, edit, and reject rates over time
Architecture decisions
InterfaceKeyboard-first review queue

The interface is used all day by the same people; every extra click is multiplied by thousands of items.

LogicDurable task queue with explicit state machines

Agent steps fail, time out, and need replay. A state machine makes that recoverable instead of mysterious.

DataPostgreSQL with pgvector, append-only audit table

Retrieval and records in one system; the audit log is append-only so history cannot be quietly rewritten.

IntelligenceRetrieval-grounded generation with per-task evaluation sets

Grounding in resolved cases cuts fabrication; task-specific evaluation catches regression when a model or prompt changes.

InfrastructureBackground workers separated from the request path

A slow model call must never block the interface a team is working in.

Technology and integrations
  • Anthropic API
  • Gmail API
  • Slack
  • PostgreSQL + pgvector
  • Temporal-style durable workflows

Measured results

Not measured yet.

Estimates presented as measurements are the most common lie on an agency website, and it is not one we are willing to tell.

04

Work with us

The service this maps to.

AI Automation and Assistants

Workflow automation and assistants grounded in your own documents and systems — scoped against a measured process, not a demo.

Explore AI automation and assistants

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