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Sayfitech
AI solutions

AI business automation

Judgement-heavy steps, classifying, summarising, deciding, handled by models inside your workflows.

Engagement
Fixed-scope build, accuracy monitoring optional
Typical timeline
6–7 weeks
Starts with
A folder of 300 real documents from last quarter, exactly as they arrived.
01The situation

Four hundred emails, forms and PDFs arrive each day, and someone has to read every one to decide what it is, where it belongs and what it contains. The work is not difficult. It is unbounded, and it fails quietly on the days volume spikes.

Why it matters

Automation that finally covers the messy fifth of cases rules-based tools miss.

AI that does work, not demos.

What we build

Concrete artefacts, handed over and documented.

01

A labelled set of your own past documents, sorted into the categories your process actually uses

02

Classification and extraction scored against that set per category, so you can see which document types it handles and which it does not

03

A confidence threshold with an exception queue, so uncertain items reach a person instead of the pile

04

Write-back into the system that owns the record, built so a retry can never create a duplicate

05

A monthly check comparing live accuracy against the original test set, because inputs drift even when the model does not

What changes

01

The queue clears in minutes regardless of what the day brings

02

Error rate is known per document type rather than averaged into something meaningless

03

Staff move from reading everything to reviewing the small share the system flags

How it runs

  1. Weeks 1–2

    01Label

    We take real documents from your last quarter and agree, case by case, what the correct outcome would have been.

  2. Weeks 3–5

    02Build and measure

    Extraction, classification and routing, reported as a per-category score you can argue with.

  3. Weeks 6–7

    03Shadow run

    It processes the same post as your team for two weeks and we compare every disagreement before it takes over.

06Typical stack

Chosen per project. Named here so you can see the shape of it.

  • OpenAI
  • Anthropic Claude
  • Braintrust
  • Postgres
  • n8n

Questions we get asked

Next step

Tell us the outcome, not the tooling.

Send us the situation you are in. We will tell you which discipline it belongs to, what we would do first and what it costs, including when the answer is to wait.

Start a projectOr email us directlyinfo@sayfi.ai