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Production AI, with the evidence.

Explore what the founding team built, how it works, and what the evidence supports. Every case carries its deployment status.

Delivered by our founding team at a previous firm. Clients anonymised under NDA.

Insurance marketplace, India

Live

From retyping policies to ~98% of fields read correctly

~98% fields read correctly

hand-verified test set

Read the build

Logistics network, India

Live

Eight back-office workflows on agents: 5,000+ hours back

5,000+ hours saved across the eight workflows

client-reported result

Read the build

Top-10 e-commerce platform, India

Live

Pricing millions of SKUs without the catalogue leaving

targeted 15% sales lift at SKU level

target

Read the build

Same insurance marketplace, commercial lines

Recently deployed

Commercial-insurance placement, out of the inbox

Results to follow.

Read the build

Investment research firm, East Africa

Proof of concept

A research desk that writes the report, on the firm's own servers

Results to follow.

Read the build

At-home services marketplace

In build

An owned data and AI core for 12,000+ professionals

Results to follow.

Read the build

Straight answers

Why are clients anonymised?

Under NDA. Figures are as the clients reported them.

Were these delivered by Ideolyst?

By the Ideolyst founding team at a previous firm, before Ideolyst existed. Ideolyst does not claim them as Ideolyst contracts and publishes them with that attribution.

What do the four labels mean?

Live: in production with client-reported results. Recently deployed: in production, results not yet measured. Proof of concept: working end to end on the client's data, full build pending. In build: contracted or in internal build, under construction.

Can I speak to a reference?

Yes, on request, subject to the client's consent.

Will you publish my results?

Only with written consent, labelled the same way, anonymised if you prefer.

Last reviewed 2026-10-24

Bring one problem.

Tell us about one costly process. A founder replies within two working days.

Start with the problem. Agree the right next step together.

For example:

  • a document your team retypes
  • a queue that grows with volume
  • data that can't leave the building
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