Insight Analytics
Service

AI Automation

Narrow, measurable automations for document-heavy work — with evaluation sets and a human in the loop, not a chatbot bolted onto the homepage.

The AI projects that succeed in mid-market companies are unglamorous. Reading an invoice and extracting fifteen fields. Classifying an inbound email into one of eight queues. Drafting a first-pass response a person then edits. They work because the task is narrow and correctness is measurable.

We start by building an evaluation set from your real documents or tickets, so we can state accuracy as a number before anything goes live. The automation handles the confident cases; anything below threshold routes to a person with the model's reasoning attached.

We're equally willing to tell you an automation isn't worth building. If the volume is low or the error cost is high, a rules-based approach is often cheaper and more predictable.

Problems this solves

What we usually walk into

  • Staff re-key data from PDFs and emails into a system all day.
  • Inbound requests sit in a shared mailbox waiting to be sorted.
  • Response quality varies by whoever picks up the ticket.
  • An AI pilot was run last year and nobody could measure whether it worked.

Technologies

What we build with

OpenAI / Anthropic modelsLangChainPythonVector searchAzure AIFastAPI

Industries that benefit

  • Insurance
  • Healthcare administration
  • Logistics
  • Legal and professional services
  • Financial services

Implementation process

How the engagement runs

  1. 01

    Use-case triage

    Score candidate tasks on volume, error tolerance, and how measurable success is. Pick one.

  2. 02

    Evaluation set

    A labelled sample of real cases becomes the benchmark every version is scored against.

  3. 03

    Prototype and iterate

    Prompting, retrieval, and structured output tuned until accuracy clears the agreed threshold.

  4. 04

    Guardrails and routing

    Confidence thresholds, schema validation, and human review for anything uncertain.

  5. 05

    Deploy and monitor

    Live accuracy tracking, cost per document, and drift alerts after go-live.

Sample screens

What the finished work looks like

Representative layouts using demonstration data — client work is never shown without written permission.

Extraction accuracy

Accuracy

96.4%

Auto-clear

81%

Fields

18

Extraction accuracy

Field-level accuracy tracked against the evaluation set.

Triage queue

Daily

1,240

Routed

94%

Escalated

72

Triage queue

Volume by category with confidence banding.

Cost per document

Per doc

$0.04

Monthly

$1.5K

Saved

310 hrs

Cost per document

Model spend by document type and version.

Illustrative example

How a ai automation engagement typically plays out

Anonymised scenario · not a verified client record

Commercial insurance brokerage

Challenge

Two staff spent most of their week keying data from carrier loss runs — inconsistent PDFs, fifteen fields each — into the agency management system, with a three-day backlog during renewal season.

Solution

A document extraction service with a labelled evaluation set of 400 historical loss runs, structured output validation, and a review queue where anything under 90% confidence is checked by a human with the source page highlighted.

Result

81% of documents now clear without human touch at 96% field accuracy, and the renewal-season backlog was eliminated.

Illustrative figures

96.4%

Field accuracy

81%

Straight-through

0

Renewal backlog

This is a composite illustration of the scope, approach, and range of results this service is designed to deliver. It does not describe a specific named client, and the figures are demonstration values rather than audited outcomes. We're happy to talk through real references under NDA on a call.

Deliverables

What you receive

  • Labelled evaluation set and accuracy baseline
  • Deployed extraction, classification, or drafting service
  • Human review queue with model reasoning shown
  • Accuracy and cost monitoring dashboard
  • Prompt and model version history

FAQs

Questions we get asked

Will our data be used to train a model?
No. We use enterprise API endpoints with training disabled, and we'll document the data flow for your compliance team.
What accuracy should we expect?
For structured document extraction, typically 92–98% field accuracy on clean sources. We measure it on your documents before committing.
Does this replace staff?
In practice it removes the first pass so the same people handle more volume and spend time on exceptions. We design for review, not replacement.
What does it cost to run?
Usually cents per document. We report cost per processed item in the monitoring dashboard so it's never a surprise.

Related services

Often scoped alongside this

Python Automation

Custom scripts for scraping, ETL, machine learning, and orchestration.

View python automation

API Integration

Connect CRM, ERP, and SaaS into one clean source of truth.

View api integration

Workflow Optimization

Process mining and re-engineering for operational efficiency.

View workflow optimization

Talk through your AI Automation project

A 30-minute call is usually enough to tell you whether this is a two-week fix or a two-month build — and roughly what it costs.

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